Creator · simstudioai
Last updated · Sep 3, 2026
Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination)
Creator · simstudioai
Last updated · Sep 3, 2026
Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination)
Creator · simstudioai
Last updated · Sep 3, 2026
Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination)
Creator · simstudioai
Last updated · Sep 3, 2026
Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination)
Sandbox only
Install targets
Codex install prompt
Install the "add-model" agent skill from https://github.com/simstudioai/sim/tree/main/.agents/skills/add-model. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"simstudioai-add-model","task":"Install add-model","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add simstudioai/sim --skill add-model
Maintenance
fresh
3d since push
Risk
Risky
Dependency or permission surface needs review
GitHub quality
30K
92/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
RiskyA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
30K GitHub stars
Repo activity
30K stars, 3.8K forks
Maintenance
3d since push
License
Apache-2.0
Install
npx skills add simstudioai/sim --skill add-model
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add simstudioai/sim --skill add-modelDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/simstudioai-add-model/install
Agent should check
Copy prompt
Task: Use add-model in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/simstudioai-add-model/install
Install command: npx skills add simstudioai/sim --skill add-model
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/simstudioai-add-model/install
LLM text format
/api/skills/simstudioai-add-model/install?format=text
Find alternatives
/api/skills/search?q=add-model&limit=3
Agent prompt
Use add-model for this task. Review https://www.openagentskill.com/api/skills/simstudioai-add-model/install, then install with: npx skills add simstudioai/sim --skill add-modelRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/simstudioai-add-model
LLM text
/api/registry/manifest/simstudioai-add-model?format=text
Install alias
/api/registry/install/simstudioai-add-model
Recommend
/api/registry/recommend?task=Use%20add-model%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Research agents
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS30K GitHub stars
Stars/forks activity
PASS30K stars, 3.8K forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSApache-2.0
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: add-model description: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) argument-hint: <provider> <model-id> [docs-url] ---
# Add Model Skill
You add a new model entry to `apps/sim/providers/models.ts`. **Every numeric and capability claim MUST be derived from a live web fetch of the provider's official docs in this session.** Marketing emails, training data, and your prior knowledge are not sources of truth — they routinely hallucinate pricing, context windows, and capability lists.
## Hard rules (do not skip)
1. **Live-fetch or refuse.** Before writing the entry, you must successfully WebFetch the provider's official models/pricing page in this session. If you cannot reach an authoritative source for any field, **mark the field as UNVERIFIED in your report and ask the user before guessing**. Never fill in pricing or capabilities from memory. 2. **Two-source rule for pricing.** Cross-check input/output/cached pricing against at least one secondary source (OpenRouter, Artificial Analysis, CloudPrice, mem0, intuitionlabs). If sources disagree, the provider's own docs win — but flag the disagreement. 3. **Read the code before setting capability flags.** Capability flags are dead unless the provider's implementation under `apps/sim/providers/{provider}/` actually consumes them (see Consumption Matrix below). Setting a flag the provider ignores is a silent bug. 4. **Cite every fact.** Your final report must list the URL each value came from. No URL → not verified.
## Your Task
1. Identify provider and model id from user args 2. Live-fetch official docs + pricing page + capability/parameter pages + at least one secondary source 3. Apply the Consumption Matrix to know which capability flags are real 4. Read 2-3 sibling entries in `models.ts` and match their pattern exactly 5. Check the repo-side touchpoints that are NOT data-driven (hosted-key billing, tests, provider code) 6. Insert the entry, run `bun run lint`, print the verification report
## Step 1: Live source-of-truth lookup
In priority order — fetch all that exist for the provider:
| Provider | Models index | Pricing | Reasoning/parameter caveats | |---|---|---|---| | OpenAI | platform.openai.com/docs/models | openai.com/api/pricing | platform.openai.com/docs/guides/reasoning | | Anthropic | platform.claude.com/docs/en/about-claude/models/overview | claude.com/pricing (API section) | platform.claude.com/docs/en/build-with-claude/extended-thinking | | Google (Gemini) | ai.google.dev/gemini-api/docs/models | ai.google.dev/pricing | ai.google.dev/gemini-api/docs/thinking | | xAI | docs.x.ai/developers/models | docs.x.ai/developers/models (per-model detail page) | docs.x.ai/developers/model-capabilities/text/reasoning | | Mistral | docs.mistral.ai/getting-started/models/models_overview | mistral.ai/pricing | n/a | | DeepSeek | api-docs.deepseek.com/quick_start/pricing | same | api-docs.deepseek.com/guides/reasoning_model | | Groq | console.groq.com/docs/models | groq.com/pricing | n/a | | Cerebras | inference-docs.cerebras.ai/models | cerebras.ai/pricing | n/a |
Secondary verification (use at least one): `openrouter.ai/<provider>/<model>`, `artificialanalysis.ai/models/<model>`, `cloudprice.net/models/<provider>-<model>`.
Use a precise WebFetch prompt: *"Extract for {model_id}: exact model id string, context window in tokens, input price per 1M, cached input price per 1M, output price per 1M, max output tokens, supported reasoning effort levels, accepted parameters (temperature, top_p), release date. Do not fill in fields you cannot find."*
## Step 2: Consumption Matrix (which provider honors which capability)
| Capability | Honored by | Effect if set elsewhere | |---|---|---| | `temperature` | All providers (passed through if set) | Safe but inert on always-reasoning models that reject it | | `toolUsageControl` | All providers (provider-level, not per-model) | n/a — set on `ProviderDefinition`, not models | | `reasoningEffort` | `openai/core.ts`, `azure-openai`, `xai`, `deepseek`, `groq`, `zai`, `meta`, `litellm` (each `index.ts`) | Not read by anthropic/gemini (they use `thinking`) or by mistral, cerebras, openrouter, fireworks, vertex — re-grep before assuming | | `verbosity` | `openai/core.ts`, `azure-openai/index.ts` only | Dead elsewhere | | `thinking` | `anthropic/core.ts`, `gemini/core.ts`; `deepseek`, `groq`, `zai`, `kimi` (each `index.ts`) read the resolved `thinkingLevel` | Dead elsewhere | | `thinking.streamed` | Docs generator + `getThinkingStreamVisibility` (`models.ts`); `anthropic/core.ts` uses `'summary'` to request `display: 'summarized'` on agent-events runs | **Mandatory on Anthropic-family thinking models** (`agent-stream-docs:check` fails without it); other families fall back to provider defaults | | `nativeStructuredOutputs` | `anthropic/core.ts`, `bedrock/index.ts` (via `models.ts` `supportsNativeStructuredOutputs`, which reads the flag) | Dead elsewhere — fireworks/baseten/together/openrouter call their own provider-level `supportsNativeStructuredOutputs` that ignores the model flag (always on, always off, or OpenRouter API metadata) | | `maxOutputTokens` | Read by UI + executor for token estimation | Always meaningful — set if provider documents a cap | | `computerUse` | `providers/utils.ts` (`getComputerUseModels` → `computerUseModels` routing) | Set only on actual computer-use SKUs | | `deepResearch` | UI flag for routing to deep-research SKUs | Set only on actual deep-research model IDs | | `memory: false` | Conversation persistence opt-out | Set only when model genuinely cannot maintain history (e.g., deep-research) |
**Always re-grep before relying on this table** — the codebase moves:
```bash rg "reasoningEffort|reasoning_effort" apps/sim/providers/<provider>/ rg "verbosity" apps/sim/providers/<provider>/ rg "request\.thinking|thinking:" apps/sim/providers/<provider>/ rg "supportsNativeStructuredOutputs|nativeStructuredOutputs" apps/sim/providers/<provider>/ ```
## Step 3: Match the provider's existing entry pattern
Open `apps/sim/providers/models.ts`, find `PROVIDER_DEFINITIONS[<provider>].models`, read 2-3 sibling entries. Match field order exactly:
```ts { id: '<exact-api-id>', pricing: { input: <number>, cachedInput: <number>, // omit if provider doesn't offer caching output: <number>, updatedAt: '<today YYYY-MM-DD>', }, capabilities: { // only flags the provider actually consumes — see matrix }, contextWindow: <tokens>, releaseDate: '<YYYY-MM-DD>', recommended: true, // only if new flagship; ask user before swapping speedOptimized: true, // only on smallest/fastest tier deprecated: true, // only on retired models } ```
### Reseller providers (azure-openai, azure-anthropic, vertex, bedrock, openrouter)
Model id MUST be prefixed: `azure/`, `azure-anthropic/`, `vertex/`, `bedrock/`, `openrouter/`. Pricing usually mirrors the upstream provider but verify on the reseller's own pricing page.
### Insertion order
Within a family, newest first (as the existing entries are ordered). Across families, biggest/flagship at top of list.
### `recommended` / `speedOptimized`
- At most one or two `recommended: true` per provider — the current flagship(s). - If you're adding a new flagship, ask the user before removing `recommended` from the previous flagship. Never silently flip it. - `speedOptimized: true` only on the smallest/fastest tier (nano, flash-lite, haiku class). - Use today's date for `pricing.updatedAt`; never copy a sibling's. - `cachedInput` is an explicit documented number — never derived from `input` (ratios vary by provider).
## Step 4: Repo-side touchpoints beyond the entry
Adding the `models.ts` entry is most of the job because nearly every consumer is **data-driven** and picks the model up automatically: the ~40 query helpers in `models.ts` / `providers/utils.ts`, the public `/models` catalog (`app/(landing)/models/utils.ts` iterates `PROVIDER_DEFINITIONS`), the agent-block model dropdown, and copilot's `isKnownModelId` / `suggestModelIdsForUnknownModel` validation. The touchpoints below are the exceptions — they are **not** data-driven, so check each one.
### Hosted = auto-billed, by provider
`getHostedModels()` in `apps/sim/providers/models.ts` returns the model IDs served with Sim's rotating hosted key and billed to the workspace via `shouldBillModelUsage()` (`providers/utils.ts`). It builds that list by expanding whole providers (`getProviderModels('openai')`, `'anthropic'`, `'google'`, and others) plus the static Fireworks catalog, so any model added under one of those providers is hosted automatically. Read the function before inserting — the provider set changes. Before you insert:
- **If the model should be BYOK-only / never-billed**, do not add it under a provider that `getHostedModels()` expands — that silently enrolls it in hosted billing. After inserting, verify with `getHostedModels().includes('<new-model-id>')` (a one-line `bun -e` or the assertion in `providers/utils.test.ts`). Confirm hosting/billing intent with the user. (Ollama Cloud is a deliberately separate `isReseller` provider specifically to stay BYOK-only/never-billed.) - **If the model should be hosted**, the deployment must actually have a key for it — the provider's `{PREFIX}_COUNT` / `{PREFIX}_1..N` env vars must be set, or hosted runs fail at execution time. - State the hosted/billing status explicitly in the verification report.
### Tests with hardcoded model IDs
`bun run lint` does **not** run tests. A few tests assert specific model IDs and can break or need updating when you touch a hosted or flagship model:
- `apps/sim/providers/utils.test.ts` — asserts membership of `getHostedModels()` / `shouldBillModelUsage()` - `apps/sim/providers/index.test.ts` and serializer tests — reference concrete model IDs
```bash rg "<new-model-id>|getHostedModels|shouldBillModelUsage" apps/sim/providers/*.test.ts ```
If anything matches, run the affected provider tests and update assertions as needed.
### New API behavior is NOT data-driven
The Consumption Matrix (Step 2) tells you which capability *flags* are honored by existing provider code. But if the new model needs **net-new** request handling that the provider doesn't implement yet — a new beta header, a new thinking/reasoning encoding, a Responses-API quirk — you must edit `apps/sim/providers/<provider>/core.ts` / `index.ts`. Setting a flag whose behavior isn't implemented is a silent no-op. When you do edit provider code, reuse the shared helpers rather than hand-rolling: streaming responses are assembled via `createStreamingExecution` (`@/providers/streaming-execution`) and tool schemas via `adaptOpenAIChatToolSchema` / `adaptAnthropicToolSchema` (`@/providers/tool-schema-adapter`).
### Thinking/reasoning models: `streamed` visibility + generated docs
If the entry has `capabilities.thinking` or `capabilities.reasoningEffort`, it appears in the autogenerated "Streamed thinking and tool calls" table on the Agent block docs page:
- **Anthropic-family (`anthropic`, `azure-anthropic`) thinking models MUST declare `capabilities.thinking.streamed`** (`'full' | 'summary' | 'none'`). Verify against Anthropic's current thinking-display and streaming docs: visible thinking returned by the API is summarized, including when Sim opts models whose default display is `omitted` into `display: 'summarized'` on agent-events runs, so current Claude thinking models use `'summary'`. Use `'full'` only if future official API docs explicitly guarantee raw thinking deltas. `bun run agent-stream-docs:check` (CI) fails if the field is missing. - Other families usually omit the field and inherit the provider default in `getThinkingStreamVisibility` (Gemini/OpenAI → summaries; Bedrock/Meta → none; OpenAI-compatible vendors with documented reasoning fields → full deltas). Set it explicitly only when the mo
Source provenance
Decision snapshot
29,529 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for add-model, ready for a manual X post.
A practical pick for source-backed research: add-model: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucinat... 29.5K stars https://www.openagentskill.com/skills/simstudioai-add-model?ref=x
Listing + install path for add-model: https://www.openagentskill.com/skills/simstudioai-add-model?ref=x Install: npx skills add simstudioai/sim --skill add-model
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to simstudioai but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model/audit)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)simstudioai
@simstudioai
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsSandbox only
Install targets
Codex install prompt
Install the "add-model" agent skill from https://github.com/simstudioai/sim/tree/main/.agents/skills/add-model. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"simstudioai-add-model","task":"Install add-model","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add simstudioai/sim --skill add-model
Maintenance
fresh
3d since push
Risk
Risky
Dependency or permission surface needs review
GitHub quality
30K
92/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
RiskyA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
30K GitHub stars
Repo activity
30K stars, 3.8K forks
Maintenance
3d since push
License
Apache-2.0
Install
npx skills add simstudioai/sim --skill add-model
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add simstudioai/sim --skill add-modelDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/simstudioai-add-model/install
Agent should check
Copy prompt
Task: Use add-model in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/simstudioai-add-model/install
Install command: npx skills add simstudioai/sim --skill add-model
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/simstudioai-add-model/install
LLM text format
/api/skills/simstudioai-add-model/install?format=text
Find alternatives
/api/skills/search?q=add-model&limit=3
Agent prompt
Use add-model for this task. Review https://www.openagentskill.com/api/skills/simstudioai-add-model/install, then install with: npx skills add simstudioai/sim --skill add-modelRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/simstudioai-add-model
LLM text
/api/registry/manifest/simstudioai-add-model?format=text
Install alias
/api/registry/install/simstudioai-add-model
Recommend
/api/registry/recommend?task=Use%20add-model%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Research agents
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS30K GitHub stars
Stars/forks activity
PASS30K stars, 3.8K forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSApache-2.0
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: add-model description: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) argument-hint: <provider> <model-id> [docs-url] ---
# Add Model Skill
You add a new model entry to `apps/sim/providers/models.ts`. **Every numeric and capability claim MUST be derived from a live web fetch of the provider's official docs in this session.** Marketing emails, training data, and your prior knowledge are not sources of truth — they routinely hallucinate pricing, context windows, and capability lists.
## Hard rules (do not skip)
1. **Live-fetch or refuse.** Before writing the entry, you must successfully WebFetch the provider's official models/pricing page in this session. If you cannot reach an authoritative source for any field, **mark the field as UNVERIFIED in your report and ask the user before guessing**. Never fill in pricing or capabilities from memory. 2. **Two-source rule for pricing.** Cross-check input/output/cached pricing against at least one secondary source (OpenRouter, Artificial Analysis, CloudPrice, mem0, intuitionlabs). If sources disagree, the provider's own docs win — but flag the disagreement. 3. **Read the code before setting capability flags.** Capability flags are dead unless the provider's implementation under `apps/sim/providers/{provider}/` actually consumes them (see Consumption Matrix below). Setting a flag the provider ignores is a silent bug. 4. **Cite every fact.** Your final report must list the URL each value came from. No URL → not verified.
## Your Task
1. Identify provider and model id from user args 2. Live-fetch official docs + pricing page + capability/parameter pages + at least one secondary source 3. Apply the Consumption Matrix to know which capability flags are real 4. Read 2-3 sibling entries in `models.ts` and match their pattern exactly 5. Check the repo-side touchpoints that are NOT data-driven (hosted-key billing, tests, provider code) 6. Insert the entry, run `bun run lint`, print the verification report
## Step 1: Live source-of-truth lookup
In priority order — fetch all that exist for the provider:
| Provider | Models index | Pricing | Reasoning/parameter caveats | |---|---|---|---| | OpenAI | platform.openai.com/docs/models | openai.com/api/pricing | platform.openai.com/docs/guides/reasoning | | Anthropic | platform.claude.com/docs/en/about-claude/models/overview | claude.com/pricing (API section) | platform.claude.com/docs/en/build-with-claude/extended-thinking | | Google (Gemini) | ai.google.dev/gemini-api/docs/models | ai.google.dev/pricing | ai.google.dev/gemini-api/docs/thinking | | xAI | docs.x.ai/developers/models | docs.x.ai/developers/models (per-model detail page) | docs.x.ai/developers/model-capabilities/text/reasoning | | Mistral | docs.mistral.ai/getting-started/models/models_overview | mistral.ai/pricing | n/a | | DeepSeek | api-docs.deepseek.com/quick_start/pricing | same | api-docs.deepseek.com/guides/reasoning_model | | Groq | console.groq.com/docs/models | groq.com/pricing | n/a | | Cerebras | inference-docs.cerebras.ai/models | cerebras.ai/pricing | n/a |
Secondary verification (use at least one): `openrouter.ai/<provider>/<model>`, `artificialanalysis.ai/models/<model>`, `cloudprice.net/models/<provider>-<model>`.
Use a precise WebFetch prompt: *"Extract for {model_id}: exact model id string, context window in tokens, input price per 1M, cached input price per 1M, output price per 1M, max output tokens, supported reasoning effort levels, accepted parameters (temperature, top_p), release date. Do not fill in fields you cannot find."*
## Step 2: Consumption Matrix (which provider honors which capability)
| Capability | Honored by | Effect if set elsewhere | |---|---|---| | `temperature` | All providers (passed through if set) | Safe but inert on always-reasoning models that reject it | | `toolUsageControl` | All providers (provider-level, not per-model) | n/a — set on `ProviderDefinition`, not models | | `reasoningEffort` | `openai/core.ts`, `azure-openai`, `xai`, `deepseek`, `groq`, `zai`, `meta`, `litellm` (each `index.ts`) | Not read by anthropic/gemini (they use `thinking`) or by mistral, cerebras, openrouter, fireworks, vertex — re-grep before assuming | | `verbosity` | `openai/core.ts`, `azure-openai/index.ts` only | Dead elsewhere | | `thinking` | `anthropic/core.ts`, `gemini/core.ts`; `deepseek`, `groq`, `zai`, `kimi` (each `index.ts`) read the resolved `thinkingLevel` | Dead elsewhere | | `thinking.streamed` | Docs generator + `getThinkingStreamVisibility` (`models.ts`); `anthropic/core.ts` uses `'summary'` to request `display: 'summarized'` on agent-events runs | **Mandatory on Anthropic-family thinking models** (`agent-stream-docs:check` fails without it); other families fall back to provider defaults | | `nativeStructuredOutputs` | `anthropic/core.ts`, `bedrock/index.ts` (via `models.ts` `supportsNativeStructuredOutputs`, which reads the flag) | Dead elsewhere — fireworks/baseten/together/openrouter call their own provider-level `supportsNativeStructuredOutputs` that ignores the model flag (always on, always off, or OpenRouter API metadata) | | `maxOutputTokens` | Read by UI + executor for token estimation | Always meaningful — set if provider documents a cap | | `computerUse` | `providers/utils.ts` (`getComputerUseModels` → `computerUseModels` routing) | Set only on actual computer-use SKUs | | `deepResearch` | UI flag for routing to deep-research SKUs | Set only on actual deep-research model IDs | | `memory: false` | Conversation persistence opt-out | Set only when model genuinely cannot maintain history (e.g., deep-research) |
**Always re-grep before relying on this table** — the codebase moves:
```bash rg "reasoningEffort|reasoning_effort" apps/sim/providers/<provider>/ rg "verbosity" apps/sim/providers/<provider>/ rg "request\.thinking|thinking:" apps/sim/providers/<provider>/ rg "supportsNativeStructuredOutputs|nativeStructuredOutputs" apps/sim/providers/<provider>/ ```
## Step 3: Match the provider's existing entry pattern
Open `apps/sim/providers/models.ts`, find `PROVIDER_DEFINITIONS[<provider>].models`, read 2-3 sibling entries. Match field order exactly:
```ts { id: '<exact-api-id>', pricing: { input: <number>, cachedInput: <number>, // omit if provider doesn't offer caching output: <number>, updatedAt: '<today YYYY-MM-DD>', }, capabilities: { // only flags the provider actually consumes — see matrix }, contextWindow: <tokens>, releaseDate: '<YYYY-MM-DD>', recommended: true, // only if new flagship; ask user before swapping speedOptimized: true, // only on smallest/fastest tier deprecated: true, // only on retired models } ```
### Reseller providers (azure-openai, azure-anthropic, vertex, bedrock, openrouter)
Model id MUST be prefixed: `azure/`, `azure-anthropic/`, `vertex/`, `bedrock/`, `openrouter/`. Pricing usually mirrors the upstream provider but verify on the reseller's own pricing page.
### Insertion order
Within a family, newest first (as the existing entries are ordered). Across families, biggest/flagship at top of list.
### `recommended` / `speedOptimized`
- At most one or two `recommended: true` per provider — the current flagship(s). - If you're adding a new flagship, ask the user before removing `recommended` from the previous flagship. Never silently flip it. - `speedOptimized: true` only on the smallest/fastest tier (nano, flash-lite, haiku class). - Use today's date for `pricing.updatedAt`; never copy a sibling's. - `cachedInput` is an explicit documented number — never derived from `input` (ratios vary by provider).
## Step 4: Repo-side touchpoints beyond the entry
Adding the `models.ts` entry is most of the job because nearly every consumer is **data-driven** and picks the model up automatically: the ~40 query helpers in `models.ts` / `providers/utils.ts`, the public `/models` catalog (`app/(landing)/models/utils.ts` iterates `PROVIDER_DEFINITIONS`), the agent-block model dropdown, and copilot's `isKnownModelId` / `suggestModelIdsForUnknownModel` validation. The touchpoints below are the exceptions — they are **not** data-driven, so check each one.
### Hosted = auto-billed, by provider
`getHostedModels()` in `apps/sim/providers/models.ts` returns the model IDs served with Sim's rotating hosted key and billed to the workspace via `shouldBillModelUsage()` (`providers/utils.ts`). It builds that list by expanding whole providers (`getProviderModels('openai')`, `'anthropic'`, `'google'`, and others) plus the static Fireworks catalog, so any model added under one of those providers is hosted automatically. Read the function before inserting — the provider set changes. Before you insert:
- **If the model should be BYOK-only / never-billed**, do not add it under a provider that `getHostedModels()` expands — that silently enrolls it in hosted billing. After inserting, verify with `getHostedModels().includes('<new-model-id>')` (a one-line `bun -e` or the assertion in `providers/utils.test.ts`). Confirm hosting/billing intent with the user. (Ollama Cloud is a deliberately separate `isReseller` provider specifically to stay BYOK-only/never-billed.) - **If the model should be hosted**, the deployment must actually have a key for it — the provider's `{PREFIX}_COUNT` / `{PREFIX}_1..N` env vars must be set, or hosted runs fail at execution time. - State the hosted/billing status explicitly in the verification report.
### Tests with hardcoded model IDs
`bun run lint` does **not** run tests. A few tests assert specific model IDs and can break or need updating when you touch a hosted or flagship model:
- `apps/sim/providers/utils.test.ts` — asserts membership of `getHostedModels()` / `shouldBillModelUsage()` - `apps/sim/providers/index.test.ts` and serializer tests — reference concrete model IDs
```bash rg "<new-model-id>|getHostedModels|shouldBillModelUsage" apps/sim/providers/*.test.ts ```
If anything matches, run the affected provider tests and update assertions as needed.
### New API behavior is NOT data-driven
The Consumption Matrix (Step 2) tells you which capability *flags* are honored by existing provider code. But if the new model needs **net-new** request handling that the provider doesn't implement yet — a new beta header, a new thinking/reasoning encoding, a Responses-API quirk — you must edit `apps/sim/providers/<provider>/core.ts` / `index.ts`. Setting a flag whose behavior isn't implemented is a silent no-op. When you do edit provider code, reuse the shared helpers rather than hand-rolling: streaming responses are assembled via `createStreamingExecution` (`@/providers/streaming-execution`) and tool schemas via `adaptOpenAIChatToolSchema` / `adaptAnthropicToolSchema` (`@/providers/tool-schema-adapter`).
### Thinking/reasoning models: `streamed` visibility + generated docs
If the entry has `capabilities.thinking` or `capabilities.reasoningEffort`, it appears in the autogenerated "Streamed thinking and tool calls" table on the Agent block docs page:
- **Anthropic-family (`anthropic`, `azure-anthropic`) thinking models MUST declare `capabilities.thinking.streamed`** (`'full' | 'summary' | 'none'`). Verify against Anthropic's current thinking-display and streaming docs: visible thinking returned by the API is summarized, including when Sim opts models whose default display is `omitted` into `display: 'summarized'` on agent-events runs, so current Claude thinking models use `'summary'`. Use `'full'` only if future official API docs explicitly guarantee raw thinking deltas. `bun run agent-stream-docs:check` (CI) fails if the field is missing. - Other families usually omit the field and inherit the provider default in `getThinkingStreamVisibility` (Gemini/OpenAI → summaries; Bedrock/Meta → none; OpenAI-compatible vendors with documented reasoning fields → full deltas). Set it explicitly only when the mo
Source provenance
Decision snapshot
29,529 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for add-model, ready for a manual X post.
A practical pick for source-backed research: add-model: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucinat... 29.5K stars https://www.openagentskill.com/skills/simstudioai-add-model?ref=x
Listing + install path for add-model: https://www.openagentskill.com/skills/simstudioai-add-model?ref=x Install: npx skills add simstudioai/sim --skill add-model
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to simstudioai but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model/audit)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)simstudioai
@simstudioai
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsSandbox only
Install targets
Codex install prompt
Install the "add-model" agent skill from https://github.com/simstudioai/sim/tree/main/.agents/skills/add-model. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"simstudioai-add-model","task":"Install add-model","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add simstudioai/sim --skill add-model
Maintenance
fresh
3d since push
Risk
Risky
Dependency or permission surface needs review
GitHub quality
30K
92/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
RiskyA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
30K GitHub stars
Repo activity
30K stars, 3.8K forks
Maintenance
3d since push
License
Apache-2.0
Install
npx skills add simstudioai/sim --skill add-model
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add simstudioai/sim --skill add-modelDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/simstudioai-add-model/install
Agent should check
Copy prompt
Task: Use add-model in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/simstudioai-add-model/install
Install command: npx skills add simstudioai/sim --skill add-model
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/simstudioai-add-model/install
LLM text format
/api/skills/simstudioai-add-model/install?format=text
Find alternatives
/api/skills/search?q=add-model&limit=3
Agent prompt
Use add-model for this task. Review https://www.openagentskill.com/api/skills/simstudioai-add-model/install, then install with: npx skills add simstudioai/sim --skill add-modelRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/simstudioai-add-model
LLM text
/api/registry/manifest/simstudioai-add-model?format=text
Install alias
/api/registry/install/simstudioai-add-model
Recommend
/api/registry/recommend?task=Use%20add-model%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Research agents
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS30K GitHub stars
Stars/forks activity
PASS30K stars, 3.8K forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSApache-2.0
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: add-model description: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) argument-hint: <provider> <model-id> [docs-url] ---
# Add Model Skill
You add a new model entry to `apps/sim/providers/models.ts`. **Every numeric and capability claim MUST be derived from a live web fetch of the provider's official docs in this session.** Marketing emails, training data, and your prior knowledge are not sources of truth — they routinely hallucinate pricing, context windows, and capability lists.
## Hard rules (do not skip)
1. **Live-fetch or refuse.** Before writing the entry, you must successfully WebFetch the provider's official models/pricing page in this session. If you cannot reach an authoritative source for any field, **mark the field as UNVERIFIED in your report and ask the user before guessing**. Never fill in pricing or capabilities from memory. 2. **Two-source rule for pricing.** Cross-check input/output/cached pricing against at least one secondary source (OpenRouter, Artificial Analysis, CloudPrice, mem0, intuitionlabs). If sources disagree, the provider's own docs win — but flag the disagreement. 3. **Read the code before setting capability flags.** Capability flags are dead unless the provider's implementation under `apps/sim/providers/{provider}/` actually consumes them (see Consumption Matrix below). Setting a flag the provider ignores is a silent bug. 4. **Cite every fact.** Your final report must list the URL each value came from. No URL → not verified.
## Your Task
1. Identify provider and model id from user args 2. Live-fetch official docs + pricing page + capability/parameter pages + at least one secondary source 3. Apply the Consumption Matrix to know which capability flags are real 4. Read 2-3 sibling entries in `models.ts` and match their pattern exactly 5. Check the repo-side touchpoints that are NOT data-driven (hosted-key billing, tests, provider code) 6. Insert the entry, run `bun run lint`, print the verification report
## Step 1: Live source-of-truth lookup
In priority order — fetch all that exist for the provider:
| Provider | Models index | Pricing | Reasoning/parameter caveats | |---|---|---|---| | OpenAI | platform.openai.com/docs/models | openai.com/api/pricing | platform.openai.com/docs/guides/reasoning | | Anthropic | platform.claude.com/docs/en/about-claude/models/overview | claude.com/pricing (API section) | platform.claude.com/docs/en/build-with-claude/extended-thinking | | Google (Gemini) | ai.google.dev/gemini-api/docs/models | ai.google.dev/pricing | ai.google.dev/gemini-api/docs/thinking | | xAI | docs.x.ai/developers/models | docs.x.ai/developers/models (per-model detail page) | docs.x.ai/developers/model-capabilities/text/reasoning | | Mistral | docs.mistral.ai/getting-started/models/models_overview | mistral.ai/pricing | n/a | | DeepSeek | api-docs.deepseek.com/quick_start/pricing | same | api-docs.deepseek.com/guides/reasoning_model | | Groq | console.groq.com/docs/models | groq.com/pricing | n/a | | Cerebras | inference-docs.cerebras.ai/models | cerebras.ai/pricing | n/a |
Secondary verification (use at least one): `openrouter.ai/<provider>/<model>`, `artificialanalysis.ai/models/<model>`, `cloudprice.net/models/<provider>-<model>`.
Use a precise WebFetch prompt: *"Extract for {model_id}: exact model id string, context window in tokens, input price per 1M, cached input price per 1M, output price per 1M, max output tokens, supported reasoning effort levels, accepted parameters (temperature, top_p), release date. Do not fill in fields you cannot find."*
## Step 2: Consumption Matrix (which provider honors which capability)
| Capability | Honored by | Effect if set elsewhere | |---|---|---| | `temperature` | All providers (passed through if set) | Safe but inert on always-reasoning models that reject it | | `toolUsageControl` | All providers (provider-level, not per-model) | n/a — set on `ProviderDefinition`, not models | | `reasoningEffort` | `openai/core.ts`, `azure-openai`, `xai`, `deepseek`, `groq`, `zai`, `meta`, `litellm` (each `index.ts`) | Not read by anthropic/gemini (they use `thinking`) or by mistral, cerebras, openrouter, fireworks, vertex — re-grep before assuming | | `verbosity` | `openai/core.ts`, `azure-openai/index.ts` only | Dead elsewhere | | `thinking` | `anthropic/core.ts`, `gemini/core.ts`; `deepseek`, `groq`, `zai`, `kimi` (each `index.ts`) read the resolved `thinkingLevel` | Dead elsewhere | | `thinking.streamed` | Docs generator + `getThinkingStreamVisibility` (`models.ts`); `anthropic/core.ts` uses `'summary'` to request `display: 'summarized'` on agent-events runs | **Mandatory on Anthropic-family thinking models** (`agent-stream-docs:check` fails without it); other families fall back to provider defaults | | `nativeStructuredOutputs` | `anthropic/core.ts`, `bedrock/index.ts` (via `models.ts` `supportsNativeStructuredOutputs`, which reads the flag) | Dead elsewhere — fireworks/baseten/together/openrouter call their own provider-level `supportsNativeStructuredOutputs` that ignores the model flag (always on, always off, or OpenRouter API metadata) | | `maxOutputTokens` | Read by UI + executor for token estimation | Always meaningful — set if provider documents a cap | | `computerUse` | `providers/utils.ts` (`getComputerUseModels` → `computerUseModels` routing) | Set only on actual computer-use SKUs | | `deepResearch` | UI flag for routing to deep-research SKUs | Set only on actual deep-research model IDs | | `memory: false` | Conversation persistence opt-out | Set only when model genuinely cannot maintain history (e.g., deep-research) |
**Always re-grep before relying on this table** — the codebase moves:
```bash rg "reasoningEffort|reasoning_effort" apps/sim/providers/<provider>/ rg "verbosity" apps/sim/providers/<provider>/ rg "request\.thinking|thinking:" apps/sim/providers/<provider>/ rg "supportsNativeStructuredOutputs|nativeStructuredOutputs" apps/sim/providers/<provider>/ ```
## Step 3: Match the provider's existing entry pattern
Open `apps/sim/providers/models.ts`, find `PROVIDER_DEFINITIONS[<provider>].models`, read 2-3 sibling entries. Match field order exactly:
```ts { id: '<exact-api-id>', pricing: { input: <number>, cachedInput: <number>, // omit if provider doesn't offer caching output: <number>, updatedAt: '<today YYYY-MM-DD>', }, capabilities: { // only flags the provider actually consumes — see matrix }, contextWindow: <tokens>, releaseDate: '<YYYY-MM-DD>', recommended: true, // only if new flagship; ask user before swapping speedOptimized: true, // only on smallest/fastest tier deprecated: true, // only on retired models } ```
### Reseller providers (azure-openai, azure-anthropic, vertex, bedrock, openrouter)
Model id MUST be prefixed: `azure/`, `azure-anthropic/`, `vertex/`, `bedrock/`, `openrouter/`. Pricing usually mirrors the upstream provider but verify on the reseller's own pricing page.
### Insertion order
Within a family, newest first (as the existing entries are ordered). Across families, biggest/flagship at top of list.
### `recommended` / `speedOptimized`
- At most one or two `recommended: true` per provider — the current flagship(s). - If you're adding a new flagship, ask the user before removing `recommended` from the previous flagship. Never silently flip it. - `speedOptimized: true` only on the smallest/fastest tier (nano, flash-lite, haiku class). - Use today's date for `pricing.updatedAt`; never copy a sibling's. - `cachedInput` is an explicit documented number — never derived from `input` (ratios vary by provider).
## Step 4: Repo-side touchpoints beyond the entry
Adding the `models.ts` entry is most of the job because nearly every consumer is **data-driven** and picks the model up automatically: the ~40 query helpers in `models.ts` / `providers/utils.ts`, the public `/models` catalog (`app/(landing)/models/utils.ts` iterates `PROVIDER_DEFINITIONS`), the agent-block model dropdown, and copilot's `isKnownModelId` / `suggestModelIdsForUnknownModel` validation. The touchpoints below are the exceptions — they are **not** data-driven, so check each one.
### Hosted = auto-billed, by provider
`getHostedModels()` in `apps/sim/providers/models.ts` returns the model IDs served with Sim's rotating hosted key and billed to the workspace via `shouldBillModelUsage()` (`providers/utils.ts`). It builds that list by expanding whole providers (`getProviderModels('openai')`, `'anthropic'`, `'google'`, and others) plus the static Fireworks catalog, so any model added under one of those providers is hosted automatically. Read the function before inserting — the provider set changes. Before you insert:
- **If the model should be BYOK-only / never-billed**, do not add it under a provider that `getHostedModels()` expands — that silently enrolls it in hosted billing. After inserting, verify with `getHostedModels().includes('<new-model-id>')` (a one-line `bun -e` or the assertion in `providers/utils.test.ts`). Confirm hosting/billing intent with the user. (Ollama Cloud is a deliberately separate `isReseller` provider specifically to stay BYOK-only/never-billed.) - **If the model should be hosted**, the deployment must actually have a key for it — the provider's `{PREFIX}_COUNT` / `{PREFIX}_1..N` env vars must be set, or hosted runs fail at execution time. - State the hosted/billing status explicitly in the verification report.
### Tests with hardcoded model IDs
`bun run lint` does **not** run tests. A few tests assert specific model IDs and can break or need updating when you touch a hosted or flagship model:
- `apps/sim/providers/utils.test.ts` — asserts membership of `getHostedModels()` / `shouldBillModelUsage()` - `apps/sim/providers/index.test.ts` and serializer tests — reference concrete model IDs
```bash rg "<new-model-id>|getHostedModels|shouldBillModelUsage" apps/sim/providers/*.test.ts ```
If anything matches, run the affected provider tests and update assertions as needed.
### New API behavior is NOT data-driven
The Consumption Matrix (Step 2) tells you which capability *flags* are honored by existing provider code. But if the new model needs **net-new** request handling that the provider doesn't implement yet — a new beta header, a new thinking/reasoning encoding, a Responses-API quirk — you must edit `apps/sim/providers/<provider>/core.ts` / `index.ts`. Setting a flag whose behavior isn't implemented is a silent no-op. When you do edit provider code, reuse the shared helpers rather than hand-rolling: streaming responses are assembled via `createStreamingExecution` (`@/providers/streaming-execution`) and tool schemas via `adaptOpenAIChatToolSchema` / `adaptAnthropicToolSchema` (`@/providers/tool-schema-adapter`).
### Thinking/reasoning models: `streamed` visibility + generated docs
If the entry has `capabilities.thinking` or `capabilities.reasoningEffort`, it appears in the autogenerated "Streamed thinking and tool calls" table on the Agent block docs page:
- **Anthropic-family (`anthropic`, `azure-anthropic`) thinking models MUST declare `capabilities.thinking.streamed`** (`'full' | 'summary' | 'none'`). Verify against Anthropic's current thinking-display and streaming docs: visible thinking returned by the API is summarized, including when Sim opts models whose default display is `omitted` into `display: 'summarized'` on agent-events runs, so current Claude thinking models use `'summary'`. Use `'full'` only if future official API docs explicitly guarantee raw thinking deltas. `bun run agent-stream-docs:check` (CI) fails if the field is missing. - Other families usually omit the field and inherit the provider default in `getThinkingStreamVisibility` (Gemini/OpenAI → summaries; Bedrock/Meta → none; OpenAI-compatible vendors with documented reasoning fields → full deltas). Set it explicitly only when the mo
Source provenance
Decision snapshot
29,529 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for add-model, ready for a manual X post.
A practical pick for source-backed research: add-model: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucinat... 29.5K stars https://www.openagentskill.com/skills/simstudioai-add-model?ref=x
Listing + install path for add-model: https://www.openagentskill.com/skills/simstudioai-add-model?ref=x Install: npx skills add simstudioai/sim --skill add-model
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to simstudioai but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model/audit)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)simstudioai
@simstudioai
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsSandbox only
Install targets
Codex install prompt
Install the "add-model" agent skill from https://github.com/simstudioai/sim/tree/main/.agents/skills/add-model. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"simstudioai-add-model","task":"Install add-model","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add simstudioai/sim --skill add-model
Maintenance
fresh
3d since push
Risk
Risky
Dependency or permission surface needs review
GitHub quality
30K
92/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
RiskyA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
30K GitHub stars
Repo activity
30K stars, 3.8K forks
Maintenance
3d since push
License
Apache-2.0
Install
npx skills add simstudioai/sim --skill add-model
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add simstudioai/sim --skill add-modelDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/simstudioai-add-model/install
Agent should check
Copy prompt
Task: Use add-model in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20add-model%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/simstudioai-add-model/install
Install command: npx skills add simstudioai/sim --skill add-model
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/simstudioai-add-model/install
LLM text format
/api/skills/simstudioai-add-model/install?format=text
Find alternatives
/api/skills/search?q=add-model&limit=3
Agent prompt
Use add-model for this task. Review https://www.openagentskill.com/api/skills/simstudioai-add-model/install, then install with: npx skills add simstudioai/sim --skill add-modelRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/simstudioai-add-model
LLM text
/api/registry/manifest/simstudioai-add-model?format=text
Install alias
/api/registry/install/simstudioai-add-model
Recommend
/api/registry/recommend?task=Use%20add-model%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Research agents
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS30K GitHub stars
Stars/forks activity
PASS30K stars, 3.8K forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSApache-2.0
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Build and ship code
I need a coding agent that can understand a repository, edit code, and review pull requests.
Workflow fit
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: add-model description: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucination) argument-hint: <provider> <model-id> [docs-url] ---
# Add Model Skill
You add a new model entry to `apps/sim/providers/models.ts`. **Every numeric and capability claim MUST be derived from a live web fetch of the provider's official docs in this session.** Marketing emails, training data, and your prior knowledge are not sources of truth — they routinely hallucinate pricing, context windows, and capability lists.
## Hard rules (do not skip)
1. **Live-fetch or refuse.** Before writing the entry, you must successfully WebFetch the provider's official models/pricing page in this session. If you cannot reach an authoritative source for any field, **mark the field as UNVERIFIED in your report and ask the user before guessing**. Never fill in pricing or capabilities from memory. 2. **Two-source rule for pricing.** Cross-check input/output/cached pricing against at least one secondary source (OpenRouter, Artificial Analysis, CloudPrice, mem0, intuitionlabs). If sources disagree, the provider's own docs win — but flag the disagreement. 3. **Read the code before setting capability flags.** Capability flags are dead unless the provider's implementation under `apps/sim/providers/{provider}/` actually consumes them (see Consumption Matrix below). Setting a flag the provider ignores is a silent bug. 4. **Cite every fact.** Your final report must list the URL each value came from. No URL → not verified.
## Your Task
1. Identify provider and model id from user args 2. Live-fetch official docs + pricing page + capability/parameter pages + at least one secondary source 3. Apply the Consumption Matrix to know which capability flags are real 4. Read 2-3 sibling entries in `models.ts` and match their pattern exactly 5. Check the repo-side touchpoints that are NOT data-driven (hosted-key billing, tests, provider code) 6. Insert the entry, run `bun run lint`, print the verification report
## Step 1: Live source-of-truth lookup
In priority order — fetch all that exist for the provider:
| Provider | Models index | Pricing | Reasoning/parameter caveats | |---|---|---|---| | OpenAI | platform.openai.com/docs/models | openai.com/api/pricing | platform.openai.com/docs/guides/reasoning | | Anthropic | platform.claude.com/docs/en/about-claude/models/overview | claude.com/pricing (API section) | platform.claude.com/docs/en/build-with-claude/extended-thinking | | Google (Gemini) | ai.google.dev/gemini-api/docs/models | ai.google.dev/pricing | ai.google.dev/gemini-api/docs/thinking | | xAI | docs.x.ai/developers/models | docs.x.ai/developers/models (per-model detail page) | docs.x.ai/developers/model-capabilities/text/reasoning | | Mistral | docs.mistral.ai/getting-started/models/models_overview | mistral.ai/pricing | n/a | | DeepSeek | api-docs.deepseek.com/quick_start/pricing | same | api-docs.deepseek.com/guides/reasoning_model | | Groq | console.groq.com/docs/models | groq.com/pricing | n/a | | Cerebras | inference-docs.cerebras.ai/models | cerebras.ai/pricing | n/a |
Secondary verification (use at least one): `openrouter.ai/<provider>/<model>`, `artificialanalysis.ai/models/<model>`, `cloudprice.net/models/<provider>-<model>`.
Use a precise WebFetch prompt: *"Extract for {model_id}: exact model id string, context window in tokens, input price per 1M, cached input price per 1M, output price per 1M, max output tokens, supported reasoning effort levels, accepted parameters (temperature, top_p), release date. Do not fill in fields you cannot find."*
## Step 2: Consumption Matrix (which provider honors which capability)
| Capability | Honored by | Effect if set elsewhere | |---|---|---| | `temperature` | All providers (passed through if set) | Safe but inert on always-reasoning models that reject it | | `toolUsageControl` | All providers (provider-level, not per-model) | n/a — set on `ProviderDefinition`, not models | | `reasoningEffort` | `openai/core.ts`, `azure-openai`, `xai`, `deepseek`, `groq`, `zai`, `meta`, `litellm` (each `index.ts`) | Not read by anthropic/gemini (they use `thinking`) or by mistral, cerebras, openrouter, fireworks, vertex — re-grep before assuming | | `verbosity` | `openai/core.ts`, `azure-openai/index.ts` only | Dead elsewhere | | `thinking` | `anthropic/core.ts`, `gemini/core.ts`; `deepseek`, `groq`, `zai`, `kimi` (each `index.ts`) read the resolved `thinkingLevel` | Dead elsewhere | | `thinking.streamed` | Docs generator + `getThinkingStreamVisibility` (`models.ts`); `anthropic/core.ts` uses `'summary'` to request `display: 'summarized'` on agent-events runs | **Mandatory on Anthropic-family thinking models** (`agent-stream-docs:check` fails without it); other families fall back to provider defaults | | `nativeStructuredOutputs` | `anthropic/core.ts`, `bedrock/index.ts` (via `models.ts` `supportsNativeStructuredOutputs`, which reads the flag) | Dead elsewhere — fireworks/baseten/together/openrouter call their own provider-level `supportsNativeStructuredOutputs` that ignores the model flag (always on, always off, or OpenRouter API metadata) | | `maxOutputTokens` | Read by UI + executor for token estimation | Always meaningful — set if provider documents a cap | | `computerUse` | `providers/utils.ts` (`getComputerUseModels` → `computerUseModels` routing) | Set only on actual computer-use SKUs | | `deepResearch` | UI flag for routing to deep-research SKUs | Set only on actual deep-research model IDs | | `memory: false` | Conversation persistence opt-out | Set only when model genuinely cannot maintain history (e.g., deep-research) |
**Always re-grep before relying on this table** — the codebase moves:
```bash rg "reasoningEffort|reasoning_effort" apps/sim/providers/<provider>/ rg "verbosity" apps/sim/providers/<provider>/ rg "request\.thinking|thinking:" apps/sim/providers/<provider>/ rg "supportsNativeStructuredOutputs|nativeStructuredOutputs" apps/sim/providers/<provider>/ ```
## Step 3: Match the provider's existing entry pattern
Open `apps/sim/providers/models.ts`, find `PROVIDER_DEFINITIONS[<provider>].models`, read 2-3 sibling entries. Match field order exactly:
```ts { id: '<exact-api-id>', pricing: { input: <number>, cachedInput: <number>, // omit if provider doesn't offer caching output: <number>, updatedAt: '<today YYYY-MM-DD>', }, capabilities: { // only flags the provider actually consumes — see matrix }, contextWindow: <tokens>, releaseDate: '<YYYY-MM-DD>', recommended: true, // only if new flagship; ask user before swapping speedOptimized: true, // only on smallest/fastest tier deprecated: true, // only on retired models } ```
### Reseller providers (azure-openai, azure-anthropic, vertex, bedrock, openrouter)
Model id MUST be prefixed: `azure/`, `azure-anthropic/`, `vertex/`, `bedrock/`, `openrouter/`. Pricing usually mirrors the upstream provider but verify on the reseller's own pricing page.
### Insertion order
Within a family, newest first (as the existing entries are ordered). Across families, biggest/flagship at top of list.
### `recommended` / `speedOptimized`
- At most one or two `recommended: true` per provider — the current flagship(s). - If you're adding a new flagship, ask the user before removing `recommended` from the previous flagship. Never silently flip it. - `speedOptimized: true` only on the smallest/fastest tier (nano, flash-lite, haiku class). - Use today's date for `pricing.updatedAt`; never copy a sibling's. - `cachedInput` is an explicit documented number — never derived from `input` (ratios vary by provider).
## Step 4: Repo-side touchpoints beyond the entry
Adding the `models.ts` entry is most of the job because nearly every consumer is **data-driven** and picks the model up automatically: the ~40 query helpers in `models.ts` / `providers/utils.ts`, the public `/models` catalog (`app/(landing)/models/utils.ts` iterates `PROVIDER_DEFINITIONS`), the agent-block model dropdown, and copilot's `isKnownModelId` / `suggestModelIdsForUnknownModel` validation. The touchpoints below are the exceptions — they are **not** data-driven, so check each one.
### Hosted = auto-billed, by provider
`getHostedModels()` in `apps/sim/providers/models.ts` returns the model IDs served with Sim's rotating hosted key and billed to the workspace via `shouldBillModelUsage()` (`providers/utils.ts`). It builds that list by expanding whole providers (`getProviderModels('openai')`, `'anthropic'`, `'google'`, and others) plus the static Fireworks catalog, so any model added under one of those providers is hosted automatically. Read the function before inserting — the provider set changes. Before you insert:
- **If the model should be BYOK-only / never-billed**, do not add it under a provider that `getHostedModels()` expands — that silently enrolls it in hosted billing. After inserting, verify with `getHostedModels().includes('<new-model-id>')` (a one-line `bun -e` or the assertion in `providers/utils.test.ts`). Confirm hosting/billing intent with the user. (Ollama Cloud is a deliberately separate `isReseller` provider specifically to stay BYOK-only/never-billed.) - **If the model should be hosted**, the deployment must actually have a key for it — the provider's `{PREFIX}_COUNT` / `{PREFIX}_1..N` env vars must be set, or hosted runs fail at execution time. - State the hosted/billing status explicitly in the verification report.
### Tests with hardcoded model IDs
`bun run lint` does **not** run tests. A few tests assert specific model IDs and can break or need updating when you touch a hosted or flagship model:
- `apps/sim/providers/utils.test.ts` — asserts membership of `getHostedModels()` / `shouldBillModelUsage()` - `apps/sim/providers/index.test.ts` and serializer tests — reference concrete model IDs
```bash rg "<new-model-id>|getHostedModels|shouldBillModelUsage" apps/sim/providers/*.test.ts ```
If anything matches, run the affected provider tests and update assertions as needed.
### New API behavior is NOT data-driven
The Consumption Matrix (Step 2) tells you which capability *flags* are honored by existing provider code. But if the new model needs **net-new** request handling that the provider doesn't implement yet — a new beta header, a new thinking/reasoning encoding, a Responses-API quirk — you must edit `apps/sim/providers/<provider>/core.ts` / `index.ts`. Setting a flag whose behavior isn't implemented is a silent no-op. When you do edit provider code, reuse the shared helpers rather than hand-rolling: streaming responses are assembled via `createStreamingExecution` (`@/providers/streaming-execution`) and tool schemas via `adaptOpenAIChatToolSchema` / `adaptAnthropicToolSchema` (`@/providers/tool-schema-adapter`).
### Thinking/reasoning models: `streamed` visibility + generated docs
If the entry has `capabilities.thinking` or `capabilities.reasoningEffort`, it appears in the autogenerated "Streamed thinking and tool calls" table on the Agent block docs page:
- **Anthropic-family (`anthropic`, `azure-anthropic`) thinking models MUST declare `capabilities.thinking.streamed`** (`'full' | 'summary' | 'none'`). Verify against Anthropic's current thinking-display and streaming docs: visible thinking returned by the API is summarized, including when Sim opts models whose default display is `omitted` into `display: 'summarized'` on agent-events runs, so current Claude thinking models use `'summary'`. Use `'full'` only if future official API docs explicitly guarantee raw thinking deltas. `bun run agent-stream-docs:check` (CI) fails if the field is missing. - Other families usually omit the field and inherit the provider default in `getThinkingStreamVisibility` (Gemini/OpenAI → summaries; Bedrock/Meta → none; OpenAI-compatible vendors with documented reasoning fields → full deltas). Set it explicitly only when the mo
Source provenance
Decision snapshot
29,529 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for add-model, ready for a manual X post.
A practical pick for source-backed research: add-model: Add a new LLM model to apps/sim/providers/models.ts with specs verified against the provider's live API docs (no hallucinat... 29.5K stars https://www.openagentskill.com/skills/simstudioai-add-model?ref=x
Listing + install path for add-model: https://www.openagentskill.com/skills/simstudioai-add-model?ref=x Install: npx skills add simstudioai/sim --skill add-model
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to simstudioai but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/simstudioai-add-model/audit)
[](https://www.openagentskill.com/skills/simstudioai-add-model?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)simstudioai
@simstudioai
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsPermission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness