Creator · getsentry
Last updated · Sep 4, 2026
Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts betwe
Creator · getsentry
Last updated · Sep 4, 2026
Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts betwe
Creator · getsentry
Last updated · Sep 4, 2026
Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts betwe
Creator · getsentry
Last updated · Sep 4, 2026
Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts betwe
Sandbox only
Install targets
Codex install prompt
Install the "prompt-optimizer" agent skill from https://github.com/getsentry/skills/tree/main/skills/prompt-optimizer. 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: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. 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":"getsentry-prompt-optimizer","task":"Install prompt-optimizer","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add getsentry/skills --skill prompt-optimizer
Maintenance
fresh
10d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
979
77/100 Quality · 77/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · No explicit guidance in SKILL.md for defending optimized prompts against prompt injection from untrusted external context; it relies on separation and ownership rather than stating a concrete injection-check step.
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA 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
979 GitHub stars
Repo activity
979 stars, 51 forks
Maintenance
10d since push
License
Apache-2.0
Install
npx skills add getsentry/skills --skill prompt-optimizer
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 getsentry/skills --skill prompt-optimizerDo not use when
Alternative
174.2K Stars
npx skills add anthropics/skills --skill frontend-design
Alternative
84.4K Stars
npx skills add Leonxlnx/taste-skill --skill design-taste-frontend
Alternative
174.2K Stars
npx skills add anthropics/skills --skill canvas-design
Alternative
174.2K Stars
npx skills add anthropics/skills --skill brand-guidelines
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
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.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
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%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/getsentry-prompt-optimizer/install
Agent should check
Copy prompt
Task: Use prompt-optimizer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install
Install command: npx skills add getsentry/skills --skill prompt-optimizer
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/getsentry-prompt-optimizer/install
LLM text format
/api/skills/getsentry-prompt-optimizer/install?format=text
Find alternatives
/api/skills/search?q=prompt-optimizer&limit=3
Agent prompt
Use prompt-optimizer for this task. Review https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install, then install with: npx skills add getsentry/skills --skill prompt-optimizerRegistry 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/getsentry-prompt-optimizer
LLM text
/api/registry/manifest/getsentry-prompt-optimizer?format=text
Install alias
/api/registry/install/getsentry-prompt-optimizer
Recommend
/api/registry/recommend?task=Use%20prompt-optimizer%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
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
Browser automation
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
INFO979 GitHub stars
Stars/forks activity
INFO979 stars, 51 forks; issue activity unavailable in current metadata
Recent maintenance
PASS10d 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
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Verify behavior
I need my agent to test a web app, reproduce bugs, and verify fixes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
--- name: prompt-optimizer description: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. ---
# Prompt Optimizer
Optimize prompts with evals. Keep every instruction, example, and external context reference causal.
## Load Only What You Need
| Need | Read | |------|------| | New prompt | `references/core-patterns.md`, `references/model-family-notes.md`, `references/transformed-examples.md` | | Existing prompt | `references/meta-optimization-loop.md`, `references/core-patterns.md`, `references/model-family-notes.md` | | Model-family port | `references/model-family-notes.md`, `references/core-patterns.md` | | Repeated failures | `references/meta-optimization-loop.md`, `references/core-patterns.md` | | Weak or ambiguous draft | `references/transformed-examples.md` | | Provenance | `SOURCES.md` |
## Step 1: Capture Contract
Record before editing:
- task type: new, refine, port, or debug - target model family and snapshot, if known - prompt surface: `system`, `developer`, `user`, tool descriptions, examples, schemas - layer owners: platform, deployer/persona, retrieved context, user payload - objective and non-goals - inputs, tools, and external files available - required output shape - success criteria and failure cases - hard constraints: latency, verbosity, safety, budget, tool use, style
If success criteria or examples are missing, create a small eval set first. If the bottleneck is model choice, retrieval, tool schema, or missing evals, say so before rewriting.
## Step 2: Inventory External Context
For repo or agent prompts, list stable context by exact path:
| Context type | Examples | |--------------|----------| | Agent rules | `AGENTS.md`, `CLAUDE.md` | | Specs | `specs/*.md`, `docs/api.md` | | Policies | `SECURITY.md`, `docs/releasing.md` | | Examples | `examples/`, `tests/fixtures/` |
Rules:
- Reference stable files by repo-relative path instead of copying them. - Paste only excerpts needed for the prompt or eval case. - Mark whether a file is `loaded`, `referenced`, or `out of scope`. - Avoid vague context pointers such as "read the docs".
## Step 3: Choose Model Strategy
Read `references/model-family-notes.md`.
- Known family: optimize for that family. - Unknown family: write a portable base plus short adapter notes. - Snapshot changes: rerun evals. - Cross-family divergence: specialize only the failing layer.
## Step 4: Shape Prompt
Read `references/core-patterns.md`.
- Put stable policy in `system` or `developer`. - Put task-local facts, retrieved context, and variables in user-facing sections. - Keep one owner per behavior rule. - Use headings or tags only to separate content types. - Put tool policy in prompt text; keep schemas in provider-native tools. - Keep persona light unless it changes behavior. - Use the shortest wording that preserves the constraint. - Cut filler, repeated reminders, dead examples, and rationale that does not affect evals.
## Step 5: Optimize
Read `references/meta-optimization-loop.md` for refinements.
1. Baseline the current prompt on the same eval slice. 2. Cluster failures by root cause. 3. Write concrete edit criticisms. 4. Generate two to four candidates: - minimal-diff repair - structure-first rewrite - examples-first or tool-rule variant - provider adapter when needed 5. Compare candidates on the same cases. 6. Keep a short optimization log. 7. Validate the winner on holdout cases. 8. Stop on plateau, oscillation, overfit, excessive cost, or non-prompt bottleneck.
## Step 6: Return Package
Return:
1. `Target` 2. `Success Criteria` 3. `External Context` 4. `Optimized Prompt` 5. `Adapter Notes` 6. `Eval Set` 7. `Optimization Log` 8. `Residual Risks`
For existing prompts, include a concise diff-style note of the main behavioral changes.
## Failure Modes
- editing before defining the eval target - mixing policy, examples, and raw context without boundaries - duplicating rules across layers - putting durable policy in user payloads - asking for chain-of-thought - keeping contradictory legacy instructions - overfitting to one or two examples - retaining examples that no longer improve evals - fixing tool-use failures only in prompt text when tool descriptions or schemas are weak - adding markup that does not reduce ambiguity - using persona as a substitute for behavior rules
Source provenance
Decision snapshot
979 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 prompt-optimizer, ready for a manual X post.
A practical pick for design or creative work: prompt-optimizer: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates... 979 stars https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x
Listing + install path for prompt-optimizer: https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x Install: npx skills add getsentry/skills --skill prompt-optimizer
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 getsentry 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/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer/audit)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)getsentry
@getsentry
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
Frontend Design
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
174.2K StarsTaste Skill: Anti-Slop Frontend
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
84.4K StarsCanvas Design
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
174.2K StarsAnthropic Brand Guidelines
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
174.2K StarsSandbox only
Install targets
Codex install prompt
Install the "prompt-optimizer" agent skill from https://github.com/getsentry/skills/tree/main/skills/prompt-optimizer. 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: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. 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":"getsentry-prompt-optimizer","task":"Install prompt-optimizer","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add getsentry/skills --skill prompt-optimizer
Maintenance
fresh
10d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
979
77/100 Quality · 77/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · No explicit guidance in SKILL.md for defending optimized prompts against prompt injection from untrusted external context; it relies on separation and ownership rather than stating a concrete injection-check step.
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA 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
979 GitHub stars
Repo activity
979 stars, 51 forks
Maintenance
10d since push
License
Apache-2.0
Install
npx skills add getsentry/skills --skill prompt-optimizer
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 getsentry/skills --skill prompt-optimizerDo not use when
Alternative
174.2K Stars
npx skills add anthropics/skills --skill frontend-design
Alternative
84.4K Stars
npx skills add Leonxlnx/taste-skill --skill design-taste-frontend
Alternative
174.2K Stars
npx skills add anthropics/skills --skill canvas-design
Alternative
174.2K Stars
npx skills add anthropics/skills --skill brand-guidelines
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
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.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
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%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/getsentry-prompt-optimizer/install
Agent should check
Copy prompt
Task: Use prompt-optimizer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install
Install command: npx skills add getsentry/skills --skill prompt-optimizer
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/getsentry-prompt-optimizer/install
LLM text format
/api/skills/getsentry-prompt-optimizer/install?format=text
Find alternatives
/api/skills/search?q=prompt-optimizer&limit=3
Agent prompt
Use prompt-optimizer for this task. Review https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install, then install with: npx skills add getsentry/skills --skill prompt-optimizerRegistry 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/getsentry-prompt-optimizer
LLM text
/api/registry/manifest/getsentry-prompt-optimizer?format=text
Install alias
/api/registry/install/getsentry-prompt-optimizer
Recommend
/api/registry/recommend?task=Use%20prompt-optimizer%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
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
Browser automation
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
INFO979 GitHub stars
Stars/forks activity
INFO979 stars, 51 forks; issue activity unavailable in current metadata
Recent maintenance
PASS10d 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
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Verify behavior
I need my agent to test a web app, reproduce bugs, and verify fixes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
--- name: prompt-optimizer description: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. ---
# Prompt Optimizer
Optimize prompts with evals. Keep every instruction, example, and external context reference causal.
## Load Only What You Need
| Need | Read | |------|------| | New prompt | `references/core-patterns.md`, `references/model-family-notes.md`, `references/transformed-examples.md` | | Existing prompt | `references/meta-optimization-loop.md`, `references/core-patterns.md`, `references/model-family-notes.md` | | Model-family port | `references/model-family-notes.md`, `references/core-patterns.md` | | Repeated failures | `references/meta-optimization-loop.md`, `references/core-patterns.md` | | Weak or ambiguous draft | `references/transformed-examples.md` | | Provenance | `SOURCES.md` |
## Step 1: Capture Contract
Record before editing:
- task type: new, refine, port, or debug - target model family and snapshot, if known - prompt surface: `system`, `developer`, `user`, tool descriptions, examples, schemas - layer owners: platform, deployer/persona, retrieved context, user payload - objective and non-goals - inputs, tools, and external files available - required output shape - success criteria and failure cases - hard constraints: latency, verbosity, safety, budget, tool use, style
If success criteria or examples are missing, create a small eval set first. If the bottleneck is model choice, retrieval, tool schema, or missing evals, say so before rewriting.
## Step 2: Inventory External Context
For repo or agent prompts, list stable context by exact path:
| Context type | Examples | |--------------|----------| | Agent rules | `AGENTS.md`, `CLAUDE.md` | | Specs | `specs/*.md`, `docs/api.md` | | Policies | `SECURITY.md`, `docs/releasing.md` | | Examples | `examples/`, `tests/fixtures/` |
Rules:
- Reference stable files by repo-relative path instead of copying them. - Paste only excerpts needed for the prompt or eval case. - Mark whether a file is `loaded`, `referenced`, or `out of scope`. - Avoid vague context pointers such as "read the docs".
## Step 3: Choose Model Strategy
Read `references/model-family-notes.md`.
- Known family: optimize for that family. - Unknown family: write a portable base plus short adapter notes. - Snapshot changes: rerun evals. - Cross-family divergence: specialize only the failing layer.
## Step 4: Shape Prompt
Read `references/core-patterns.md`.
- Put stable policy in `system` or `developer`. - Put task-local facts, retrieved context, and variables in user-facing sections. - Keep one owner per behavior rule. - Use headings or tags only to separate content types. - Put tool policy in prompt text; keep schemas in provider-native tools. - Keep persona light unless it changes behavior. - Use the shortest wording that preserves the constraint. - Cut filler, repeated reminders, dead examples, and rationale that does not affect evals.
## Step 5: Optimize
Read `references/meta-optimization-loop.md` for refinements.
1. Baseline the current prompt on the same eval slice. 2. Cluster failures by root cause. 3. Write concrete edit criticisms. 4. Generate two to four candidates: - minimal-diff repair - structure-first rewrite - examples-first or tool-rule variant - provider adapter when needed 5. Compare candidates on the same cases. 6. Keep a short optimization log. 7. Validate the winner on holdout cases. 8. Stop on plateau, oscillation, overfit, excessive cost, or non-prompt bottleneck.
## Step 6: Return Package
Return:
1. `Target` 2. `Success Criteria` 3. `External Context` 4. `Optimized Prompt` 5. `Adapter Notes` 6. `Eval Set` 7. `Optimization Log` 8. `Residual Risks`
For existing prompts, include a concise diff-style note of the main behavioral changes.
## Failure Modes
- editing before defining the eval target - mixing policy, examples, and raw context without boundaries - duplicating rules across layers - putting durable policy in user payloads - asking for chain-of-thought - keeping contradictory legacy instructions - overfitting to one or two examples - retaining examples that no longer improve evals - fixing tool-use failures only in prompt text when tool descriptions or schemas are weak - adding markup that does not reduce ambiguity - using persona as a substitute for behavior rules
Source provenance
Decision snapshot
979 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 prompt-optimizer, ready for a manual X post.
A practical pick for design or creative work: prompt-optimizer: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates... 979 stars https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x
Listing + install path for prompt-optimizer: https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x Install: npx skills add getsentry/skills --skill prompt-optimizer
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 getsentry 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/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer/audit)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)getsentry
@getsentry
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
Frontend Design
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
174.2K StarsTaste Skill: Anti-Slop Frontend
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
84.4K StarsCanvas Design
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
174.2K StarsAnthropic Brand Guidelines
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
174.2K StarsSandbox only
Install targets
Codex install prompt
Install the "prompt-optimizer" agent skill from https://github.com/getsentry/skills/tree/main/skills/prompt-optimizer. 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: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. 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":"getsentry-prompt-optimizer","task":"Install prompt-optimizer","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add getsentry/skills --skill prompt-optimizer
Maintenance
fresh
10d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
979
77/100 Quality · 77/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · No explicit guidance in SKILL.md for defending optimized prompts against prompt injection from untrusted external context; it relies on separation and ownership rather than stating a concrete injection-check step.
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA 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
979 GitHub stars
Repo activity
979 stars, 51 forks
Maintenance
10d since push
License
Apache-2.0
Install
npx skills add getsentry/skills --skill prompt-optimizer
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 getsentry/skills --skill prompt-optimizerDo not use when
Alternative
174.2K Stars
npx skills add anthropics/skills --skill frontend-design
Alternative
84.4K Stars
npx skills add Leonxlnx/taste-skill --skill design-taste-frontend
Alternative
174.2K Stars
npx skills add anthropics/skills --skill canvas-design
Alternative
174.2K Stars
npx skills add anthropics/skills --skill brand-guidelines
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
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.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
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%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/getsentry-prompt-optimizer/install
Agent should check
Copy prompt
Task: Use prompt-optimizer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install
Install command: npx skills add getsentry/skills --skill prompt-optimizer
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/getsentry-prompt-optimizer/install
LLM text format
/api/skills/getsentry-prompt-optimizer/install?format=text
Find alternatives
/api/skills/search?q=prompt-optimizer&limit=3
Agent prompt
Use prompt-optimizer for this task. Review https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install, then install with: npx skills add getsentry/skills --skill prompt-optimizerRegistry 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/getsentry-prompt-optimizer
LLM text
/api/registry/manifest/getsentry-prompt-optimizer?format=text
Install alias
/api/registry/install/getsentry-prompt-optimizer
Recommend
/api/registry/recommend?task=Use%20prompt-optimizer%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
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
Browser automation
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
INFO979 GitHub stars
Stars/forks activity
INFO979 stars, 51 forks; issue activity unavailable in current metadata
Recent maintenance
PASS10d 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
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Verify behavior
I need my agent to test a web app, reproduce bugs, and verify fixes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
--- name: prompt-optimizer description: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. ---
# Prompt Optimizer
Optimize prompts with evals. Keep every instruction, example, and external context reference causal.
## Load Only What You Need
| Need | Read | |------|------| | New prompt | `references/core-patterns.md`, `references/model-family-notes.md`, `references/transformed-examples.md` | | Existing prompt | `references/meta-optimization-loop.md`, `references/core-patterns.md`, `references/model-family-notes.md` | | Model-family port | `references/model-family-notes.md`, `references/core-patterns.md` | | Repeated failures | `references/meta-optimization-loop.md`, `references/core-patterns.md` | | Weak or ambiguous draft | `references/transformed-examples.md` | | Provenance | `SOURCES.md` |
## Step 1: Capture Contract
Record before editing:
- task type: new, refine, port, or debug - target model family and snapshot, if known - prompt surface: `system`, `developer`, `user`, tool descriptions, examples, schemas - layer owners: platform, deployer/persona, retrieved context, user payload - objective and non-goals - inputs, tools, and external files available - required output shape - success criteria and failure cases - hard constraints: latency, verbosity, safety, budget, tool use, style
If success criteria or examples are missing, create a small eval set first. If the bottleneck is model choice, retrieval, tool schema, or missing evals, say so before rewriting.
## Step 2: Inventory External Context
For repo or agent prompts, list stable context by exact path:
| Context type | Examples | |--------------|----------| | Agent rules | `AGENTS.md`, `CLAUDE.md` | | Specs | `specs/*.md`, `docs/api.md` | | Policies | `SECURITY.md`, `docs/releasing.md` | | Examples | `examples/`, `tests/fixtures/` |
Rules:
- Reference stable files by repo-relative path instead of copying them. - Paste only excerpts needed for the prompt or eval case. - Mark whether a file is `loaded`, `referenced`, or `out of scope`. - Avoid vague context pointers such as "read the docs".
## Step 3: Choose Model Strategy
Read `references/model-family-notes.md`.
- Known family: optimize for that family. - Unknown family: write a portable base plus short adapter notes. - Snapshot changes: rerun evals. - Cross-family divergence: specialize only the failing layer.
## Step 4: Shape Prompt
Read `references/core-patterns.md`.
- Put stable policy in `system` or `developer`. - Put task-local facts, retrieved context, and variables in user-facing sections. - Keep one owner per behavior rule. - Use headings or tags only to separate content types. - Put tool policy in prompt text; keep schemas in provider-native tools. - Keep persona light unless it changes behavior. - Use the shortest wording that preserves the constraint. - Cut filler, repeated reminders, dead examples, and rationale that does not affect evals.
## Step 5: Optimize
Read `references/meta-optimization-loop.md` for refinements.
1. Baseline the current prompt on the same eval slice. 2. Cluster failures by root cause. 3. Write concrete edit criticisms. 4. Generate two to four candidates: - minimal-diff repair - structure-first rewrite - examples-first or tool-rule variant - provider adapter when needed 5. Compare candidates on the same cases. 6. Keep a short optimization log. 7. Validate the winner on holdout cases. 8. Stop on plateau, oscillation, overfit, excessive cost, or non-prompt bottleneck.
## Step 6: Return Package
Return:
1. `Target` 2. `Success Criteria` 3. `External Context` 4. `Optimized Prompt` 5. `Adapter Notes` 6. `Eval Set` 7. `Optimization Log` 8. `Residual Risks`
For existing prompts, include a concise diff-style note of the main behavioral changes.
## Failure Modes
- editing before defining the eval target - mixing policy, examples, and raw context without boundaries - duplicating rules across layers - putting durable policy in user payloads - asking for chain-of-thought - keeping contradictory legacy instructions - overfitting to one or two examples - retaining examples that no longer improve evals - fixing tool-use failures only in prompt text when tool descriptions or schemas are weak - adding markup that does not reduce ambiguity - using persona as a substitute for behavior rules
Source provenance
Decision snapshot
979 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 prompt-optimizer, ready for a manual X post.
A practical pick for design or creative work: prompt-optimizer: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates... 979 stars https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x
Listing + install path for prompt-optimizer: https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x Install: npx skills add getsentry/skills --skill prompt-optimizer
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 getsentry 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/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer/audit)
[](https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)getsentry
@getsentry
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
Frontend Design
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
174.2K StarsTaste Skill: Anti-Slop Frontend
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
84.4K StarsCanvas Design
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
174.2K StarsAnthropic Brand Guidelines
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
174.2K StarsSandbox only
Install targets
Codex install prompt
Install the "prompt-optimizer" agent skill from https://github.com/getsentry/skills/tree/main/skills/prompt-optimizer. 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: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. 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":"getsentry-prompt-optimizer","task":"Install prompt-optimizer","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
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add getsentry/skills --skill prompt-optimizer
Maintenance
fresh
10d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
979
77/100 Quality · 77/100 Trust
Coverage tags
Review notes
Permission surface may require sandboxing · No explicit guidance in SKILL.md for defending optimized prompts against prompt injection from untrusted external context; it relies on separation and ownership rather than stating a concrete injection-check step.
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
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA 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
979 GitHub stars
Repo activity
979 stars, 51 forks
Maintenance
10d since push
License
Apache-2.0
Install
npx skills add getsentry/skills --skill prompt-optimizer
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 getsentry/skills --skill prompt-optimizerDo not use when
Alternative
174.2K Stars
npx skills add anthropics/skills --skill frontend-design
Alternative
84.4K Stars
npx skills add Leonxlnx/taste-skill --skill design-taste-frontend
Alternative
174.2K Stars
npx skills add anthropics/skills --skill canvas-design
Alternative
174.2K Stars
npx skills add anthropics/skills --skill brand-guidelines
Agent safety v2
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
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.
medium
Skill may inspect schemas, query databases, or work with persistent stores.
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%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/getsentry-prompt-optimizer/install
Agent should check
Copy prompt
Task: Use prompt-optimizer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20prompt-optimizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install
Install command: npx skills add getsentry/skills --skill prompt-optimizer
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/getsentry-prompt-optimizer/install
LLM text format
/api/skills/getsentry-prompt-optimizer/install?format=text
Find alternatives
/api/skills/search?q=prompt-optimizer&limit=3
Agent prompt
Use prompt-optimizer for this task. Review https://www.openagentskill.com/api/skills/getsentry-prompt-optimizer/install, then install with: npx skills add getsentry/skills --skill prompt-optimizerRegistry 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/getsentry-prompt-optimizer
LLM text
/api/registry/manifest/getsentry-prompt-optimizer?format=text
Install alias
/api/registry/install/getsentry-prompt-optimizer
Recommend
/api/registry/recommend?task=Use%20prompt-optimizer%20in%20an%20agent%20workflow&limit=3
Agent fit
Browser automation
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
Browser automation
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
INFO979 GitHub stars
Stars/forks activity
INFO979 stars, 51 forks; issue activity unavailable in current metadata
Recent maintenance
PASS10d 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
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Verify behavior
I need my agent to test a web app, reproduce bugs, and verify fixes.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
--- name: prompt-optimizer description: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates. Use when asked to improve a prompt, optimize a system prompt, rewrite an agent prompt, tune prompt wording, make a prompt more reliable, port prompts between OpenAI, Claude, or Gemini, or build prompt evals. ---
# Prompt Optimizer
Optimize prompts with evals. Keep every instruction, example, and external context reference causal.
## Load Only What You Need
| Need | Read | |------|------| | New prompt | `references/core-patterns.md`, `references/model-family-notes.md`, `references/transformed-examples.md` | | Existing prompt | `references/meta-optimization-loop.md`, `references/core-patterns.md`, `references/model-family-notes.md` | | Model-family port | `references/model-family-notes.md`, `references/core-patterns.md` | | Repeated failures | `references/meta-optimization-loop.md`, `references/core-patterns.md` | | Weak or ambiguous draft | `references/transformed-examples.md` | | Provenance | `SOURCES.md` |
## Step 1: Capture Contract
Record before editing:
- task type: new, refine, port, or debug - target model family and snapshot, if known - prompt surface: `system`, `developer`, `user`, tool descriptions, examples, schemas - layer owners: platform, deployer/persona, retrieved context, user payload - objective and non-goals - inputs, tools, and external files available - required output shape - success criteria and failure cases - hard constraints: latency, verbosity, safety, budget, tool use, style
If success criteria or examples are missing, create a small eval set first. If the bottleneck is model choice, retrieval, tool schema, or missing evals, say so before rewriting.
## Step 2: Inventory External Context
For repo or agent prompts, list stable context by exact path:
| Context type | Examples | |--------------|----------| | Agent rules | `AGENTS.md`, `CLAUDE.md` | | Specs | `specs/*.md`, `docs/api.md` | | Policies | `SECURITY.md`, `docs/releasing.md` | | Examples | `examples/`, `tests/fixtures/` |
Rules:
- Reference stable files by repo-relative path instead of copying them. - Paste only excerpts needed for the prompt or eval case. - Mark whether a file is `loaded`, `referenced`, or `out of scope`. - Avoid vague context pointers such as "read the docs".
## Step 3: Choose Model Strategy
Read `references/model-family-notes.md`.
- Known family: optimize for that family. - Unknown family: write a portable base plus short adapter notes. - Snapshot changes: rerun evals. - Cross-family divergence: specialize only the failing layer.
## Step 4: Shape Prompt
Read `references/core-patterns.md`.
- Put stable policy in `system` or `developer`. - Put task-local facts, retrieved context, and variables in user-facing sections. - Keep one owner per behavior rule. - Use headings or tags only to separate content types. - Put tool policy in prompt text; keep schemas in provider-native tools. - Keep persona light unless it changes behavior. - Use the shortest wording that preserves the constraint. - Cut filler, repeated reminders, dead examples, and rationale that does not affect evals.
## Step 5: Optimize
Read `references/meta-optimization-loop.md` for refinements.
1. Baseline the current prompt on the same eval slice. 2. Cluster failures by root cause. 3. Write concrete edit criticisms. 4. Generate two to four candidates: - minimal-diff repair - structure-first rewrite - examples-first or tool-rule variant - provider adapter when needed 5. Compare candidates on the same cases. 6. Keep a short optimization log. 7. Validate the winner on holdout cases. 8. Stop on plateau, oscillation, overfit, excessive cost, or non-prompt bottleneck.
## Step 6: Return Package
Return:
1. `Target` 2. `Success Criteria` 3. `External Context` 4. `Optimized Prompt` 5. `Adapter Notes` 6. `Eval Set` 7. `Optimization Log` 8. `Residual Risks`
For existing prompts, include a concise diff-style note of the main behavioral changes.
## Failure Modes
- editing before defining the eval target - mixing policy, examples, and raw context without boundaries - duplicating rules across layers - putting durable policy in user payloads - asking for chain-of-thought - keeping contradictory legacy instructions - overfitting to one or two examples - retaining examples that no longer improve evals - fixing tool-use failures only in prompt text when tool descriptions or schemas are weak - adding markup that does not reduce ambiguity - using persona as a substitute for behavior rules
Source provenance
Decision snapshot
979 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 prompt-optimizer, ready for a manual X post.
A practical pick for design or creative work: prompt-optimizer: Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates... 979 stars https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x
Listing + install path for prompt-optimizer: https://www.openagentskill.com/skills/getsentry-prompt-optimizer?ref=x Install: npx skills add getsentry/skills --skill prompt-optimizer
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174.2K StarsPermission surface
filesystem or document access, network or browser access
Agent outcomes
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Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
filesystem or document access, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness