Creator Β· samber
Last updated Β· Sep 6, 2026
Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape'
Creator Β· samber
Last updated Β· Sep 6, 2026
Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape'
Creator Β· samber
Last updated Β· Sep 6, 2026
Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape'
Creator Β· samber
Last updated Β· Sep 6, 2026
Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape'
Sandbox only
Install targets
Codex install prompt
Install the "deep-research" agent skill from https://github.com/samber/cc-skills/tree/main/skills/deep-research. 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: Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature revie 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":"samber-deep-research","task":"Install deep-research","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 samber/cc-skills --skill deep-research
Maintenance
fresh
3d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
203
70/100 Quality Β· 69/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision Β· No explicit guardrail against prompt injection from fetched web content; the skill instructs fetching pages but does not state that page content must be treated as untrusted data, not as instructions.
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
203 GitHub stars
Repo activity
203 stars, 15 forks
Maintenance
3d since push
License
MIT
Install
npx skills add samber/cc-skills --skill deep-research
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 samber/cc-skills --skill deep-researchDo 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
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
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.
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%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/samber-deep-research/install
Agent should check
Copy prompt
Task: Use deep-research in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/samber-deep-research/install
Install command: npx skills add samber/cc-skills --skill deep-research
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/samber-deep-research/install
LLM text format
/api/skills/samber-deep-research/install?format=text
Find alternatives
/api/skills/search?q=deep-research&limit=3
Agent prompt
Use deep-research for this task. Review https://www.openagentskill.com/api/skills/samber-deep-research/install, then install with: npx skills add samber/cc-skills --skill deep-researchRegistry 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/samber-deep-research
LLM text
/api/registry/manifest/samber-deep-research?format=text
Install alias
/api/registry/install/samber-deep-research
Recommend
/api/registry/recommend?task=Use%20deep-research%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
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
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
INFO203 GitHub stars
Stars/forks activity
CHECK203 stars, 15 forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSMIT
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
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
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.
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.
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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
A relentless interview to sharpen a plan or design.
--- name: deep-research description: "Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature review, citation networks, key authors), person/org (due diligence on a company or public figure), financial (funding landscape, valuation multiples, revenue signals), legal (IP, patent landscape, litigation, compliance), trend (emerging signals, foresight, scenario mapping), community (ecosystem health, key voices, governance, fragmentation). Trigger even when phrased casually: 'look into X', 'what's the deal with Y', 'dig into Z', 'I need to understand the space', 'catch me up on X'. Do NOT use for single-fact lookups or one-off web questions." user-invocable: true license: MIT compatibility: Designed for Claude Code, Codex or similar harness. Requires internet access (web search and page fetching). metadata: author: samber authors: - Maxme Courant (github.com/mcourant) - Samuel Berthe (github.com/samber) version: "1.2.1" openclaw: emoji: "π" homepage: https://github.com/samber/cc-skills install: - kind: brew formula: curl bins: [curl] - kind: brew formula: pandoc bins: [pandoc] - kind: node package: md-to-pdf bins: [md-to-pdf] allowed-tools: Read Edit Write Glob Grep Agent WebFetch WebSearch AskUserQuestion Bash(curl:*) Bash(pandoc:*) Bash(md-to-pdf:*) ---
**Persona:** You are a senior research analyst. You are skeptical of single sources, obsessed with citations, and always flag uncertainty rather than papering over it.
**Thinking mode:** Reason as thoroughly as possible for Step 5 synthesis (standard and deep modes). Reconciling conflicting multi-source data and ranking recommendations requires deep reasoning β shallow inference produces wrong conclusions. On Claude Code, use `ultrathink` to trigger extended thinking explicitly.
**Modes:**
| Mode | When | Execution | | --- | --- | --- | | **Interview** | Step 1 β scope | Sequential; ask questions, confirm before proceeding | | **Parallel research** | Steps 2β4 β evidence gathering | Fan out 3β20 sub-agents per step; each owns one axis | | **Synthesis** | Step 5 β conclusions | Sequential + ultrathink; reconcile conflicts before recommending |
**Research depth** β select automatically based on the request:
| Depth | When | Steps | | --- | --- | --- | | **Quick** | Narrow, time-sensitive question; user says "brief" or "quick" | Steps 1 (auto-scope), 2, 5 | | **Standard** | Typical research request [default] | Steps 1β5 | | **Deep** | Comprehensive review, critical decision; user says "thorough", "exhaustive", "comprehensive" | Steps 1β5 + 4.5 (outline refinement) + critique pass |
**Autonomy:** For specific, well-scoped prompts, state assumptions and proceed without a full interview β surface them in the report header instead. Reserve the full scope interview for genuinely vague prompts (e.g., "Research blockchain", "Tell me about AI").
**Questions:** Ask the user through the environment's question tool β never as plain-text prose. One question at a time, 2β4 tappable options, wait for the answer. If the environment has no question tool, ask in prose with the same options, one at a time.
## Critical rules
- Web search is the core capability of this skill. If the environment has no web access, halt immediately and tell the user. - **Every claim must cite a source URL.** Unsourced assertions are not findings β they are guesses. - Critical claims (market size, growth rates, competitive positioning...) require **2+ independent sources** or get `confidence: Low`. - Write findings to the output file **immediately after each step** β do not batch at the end. - Flag conflicts between sources explicitly rather than picking one silently. - **Prose-first:** Write in full sentences and paragraphs (aim for β₯80% prose). Use bullets only for true lists β never as the primary content delivery. "The market reached $4.2B in 2024 [Source]" is better than "\* Market: $4.2B". - **Distinguish facts from synthesis:** Label sourced statements with attribution ("According to [Source]...") and analytical conclusions with hedges ("This suggests...", "The pattern across sources indicates..."). Never present inference as fact. - **Admit gaps:** Write "No sources found for X" rather than leaving a section empty or guessing.
## Reference files
Load these files at the steps indicated only β not all upfront.
| File | Load at | | ------------------------------- | ----------------------------------- | | `references/citations.md` | Step 2 (before first search) | | `references/parallel-search.md` | Step 2 (before spawning sub-agents) | | `references/market.md` | Step 2, if type == market | | `references/domain.md` | Step 2, if type == domain | | `references/technical.md` | Step 2, if type == technical | | `references/competitive.md` | Step 2, if type == competitive | | `references/product.md` | Step 2, if type == product | | `references/academic.md` | Step 2, if type == academic | | `references/org.md` | Step 2, if type == person/org | | `references/financial.md` | Step 2, if type == financial | | `references/legal.md` | Step 2, if type == legal | | `references/trend.md` | Step 2, if type == trend | | `references/community.md` | Step 2, if type == community |
## Step 1 β Scope
First, get today's date: `date +%Y-%m-%d`. Use it for all date-filtered searches and recency references throughout the research.
**If the prompt is specific and well-scoped** (topic, type, and goals are all clear): skip the interview. Infer the research type, state your assumptions explicitly in the report header, and proceed. Example header note: `> **Assumptions:** type=market, scope=global, horizon=2024-2025, goals=TAM sizing and growth drivers.`
**If the prompt is vague or ambiguous** (e.g., "Research blockchain", "Tell me about AI"): ask the user:
1. What type? (see list below) 2. What specific questions or goals should the research answer? 3. Any geographic, time, or segment constraints?
Research types:
- `market` β customers, competition, sizing, pricing, trends - `domain` β industry structure, regulatory landscape, ecosystem - `technical` β architecture, tools, benchmarks, integration - `competitive` β focused competitor teardown: positioning, reviews, win/loss signals - `product` β deep analysis of a specific product: features, UX, roadmap signals, changelog - `academic` β literature survey, citation networks, state of research, key authors - `person/org` β due diligence on a company or public figure: funding, leadership, press, controversies - `financial` β funding rounds, valuation multiples, revenue signals, investor patterns - `legal` β IP landscape, patents, litigation history, regulatory enforcement, contract norms - `trend` β emerging signals, weak signals, foresight, scenario mapping - `community` β ecosystem health, key voices, governance dynamics, fragmentation risks - If none fit, infer the type and design your own axis breakdown β the process (fan-out, citation discipline, write-as-you-go, synthesis) is the same regardless of type.
Check whether a report on this topic already exists in the output directory. If found, summarize what it covers and ask: extend or start fresh?
Set output path: `./research/{type}-{topic}-{YYYY-MM-DD}.md` (lowercase, hyphens). Ask if the user wants a different path. Load `assets/report-template.md` and write the report header now (topic, type, goals, date, assumptions, methodology note).
## Step 2 β Core research (parallel fan-out)
Load `references/citations.md` and `references/parallel-search.md`. Load the type-specific reference file.
Spawn **3β20 sub-agents in a single message** (one per axis from the type reference). Each agent:
- Searches its axis on the web and fetches the sources it cites - Writes findings as prose paragraphs with inline citations β not bullet lists - Returns URL, accessed date, and confidence level per claim - Tags each source: **Primary** (official docs, filings, peer-reviewed), **Established** (major publications, analyst firms), or **Low** (blogs, forums, single opinions). Flag Low-tier sources prominently. - Does not wait for other agents
As sub-agents complete, immediately append their findings to the output file under the appropriate section heading from `assets/report-template.md`. Do not wait for all agents to finish before writing.
## Step 3 β Competitive / landscape analysis (parallel fan-out)
Spawn 3β5 sub-agents covering the axes defined in the type reference file's landscape section. Same citation discipline. Append results to the output file immediately.
## Step 4 β Deep dive (parallel fan-out)
Spawn sub-agents covering the deep-dive axes for the chosen type (see type reference file). Append results immediately.
## Step 4.5 β Outline refinement (deep mode only)
After Steps 2β4, review whether the evidence warrants restructuring before synthesis. Ask:
- Did findings contradict the initial scope assumptions? - Did an important angle emerge that wasn't in the original plan? - Are any sections underpowered by evidence β or overloaded?
If yes: adapt the outline. Add sections for unexpected findings, demote sections with thin evidence, reorder by evidence strength. Run 2β3 targeted gap-fill searches for newly identified angles (time-box to 5 minutes). Document what changed and why in the report's methodology note.
Skip in quick and standard modes.
## Step 5 β Synthesis
**Use `ultrathink` here** (standard and deep modes).
Read the full output file. Write the synthesis section:
```md ## Key Findings
(5 critical insights written as prose paragraphs, each with a source reference)
## Strategic Recommendations
1. [Recommendation] β Rationale. Evidence: [source]. 2. ... (3β5 recommendations, ranked by impact)
## Risks and Uncertainties
- Data gaps: what could not be found or confirmed - Low-confidence claims requiring further validation - Conflicts between sources that could not be resolved - Domain or market risks to monitor
## Next Steps
- Recommended follow-up research - If the initial request is not fulfilled, loop on step 1 and ask more questions - Decisions this research enables ```
Keep the fact/synthesis distinction throughout: "According to [Source], X" for sourced claims; "This suggests Y" for your analysis. If a recommendation rests on Low-confidence data, say so explicitly.
**Critique pass (deep mode only):** Before finalizing, red-team the synthesis. Ask: What's missing? What could be wrong? What alternative explanations exist? What biases might be present? If a critical gap emerges, run 2β3 delta-queries to fill it before concluding.
## Step 6 β PDF export (optional)
After the Markdown report is final, offer this step if the user wants a PDF.
Try each tool in order, stop at the first that works:
Source provenance
Decision snapshot
recent repository activity
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 deep-research, ready for a manual X post.
deep-research: Deep research on any topic β broad parallel web searches, multi-source validation, confidence... 203 stars https://www.openagentskill.com/skills/samber-deep-research?ref=x
Listing + install path for deep-research: https://www.openagentskill.com/skills/samber-deep-research?ref=x Install: npx skills add samber/cc-skills --skill deep-research
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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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsSandbox only
Install targets
Codex install prompt
Install the "deep-research" agent skill from https://github.com/samber/cc-skills/tree/main/skills/deep-research. 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: Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature revie 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":"samber-deep-research","task":"Install deep-research","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 samber/cc-skills --skill deep-research
Maintenance
fresh
3d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
203
70/100 Quality Β· 69/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision Β· No explicit guardrail against prompt injection from fetched web content; the skill instructs fetching pages but does not state that page content must be treated as untrusted data, not as instructions.
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
203 GitHub stars
Repo activity
203 stars, 15 forks
Maintenance
3d since push
License
MIT
Install
npx skills add samber/cc-skills --skill deep-research
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 samber/cc-skills --skill deep-researchDo 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
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
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.
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%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/samber-deep-research/install
Agent should check
Copy prompt
Task: Use deep-research in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/samber-deep-research/install
Install command: npx skills add samber/cc-skills --skill deep-research
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
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/api/skills/samber-deep-research/install
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/api/skills/samber-deep-research/install?format=text
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/api/skills/search?q=deep-research&limit=3
Agent prompt
Use deep-research for this task. Review https://www.openagentskill.com/api/skills/samber-deep-research/install, then install with: npx skills add samber/cc-skills --skill deep-researchRegistry metadata
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Manifest
/api/registry/manifest/samber-deep-research
LLM text
/api/registry/manifest/samber-deep-research?format=text
Install alias
/api/registry/install/samber-deep-research
Recommend
/api/registry/recommend?task=Use%20deep-research%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
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
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
INFO203 GitHub stars
Stars/forks activity
CHECK203 stars, 15 forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSMIT
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
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
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.
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.
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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
A relentless interview to sharpen a plan or design.
--- name: deep-research description: "Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature review, citation networks, key authors), person/org (due diligence on a company or public figure), financial (funding landscape, valuation multiples, revenue signals), legal (IP, patent landscape, litigation, compliance), trend (emerging signals, foresight, scenario mapping), community (ecosystem health, key voices, governance, fragmentation). Trigger even when phrased casually: 'look into X', 'what's the deal with Y', 'dig into Z', 'I need to understand the space', 'catch me up on X'. Do NOT use for single-fact lookups or one-off web questions." user-invocable: true license: MIT compatibility: Designed for Claude Code, Codex or similar harness. Requires internet access (web search and page fetching). metadata: author: samber authors: - Maxme Courant (github.com/mcourant) - Samuel Berthe (github.com/samber) version: "1.2.1" openclaw: emoji: "π" homepage: https://github.com/samber/cc-skills install: - kind: brew formula: curl bins: [curl] - kind: brew formula: pandoc bins: [pandoc] - kind: node package: md-to-pdf bins: [md-to-pdf] allowed-tools: Read Edit Write Glob Grep Agent WebFetch WebSearch AskUserQuestion Bash(curl:*) Bash(pandoc:*) Bash(md-to-pdf:*) ---
**Persona:** You are a senior research analyst. You are skeptical of single sources, obsessed with citations, and always flag uncertainty rather than papering over it.
**Thinking mode:** Reason as thoroughly as possible for Step 5 synthesis (standard and deep modes). Reconciling conflicting multi-source data and ranking recommendations requires deep reasoning β shallow inference produces wrong conclusions. On Claude Code, use `ultrathink` to trigger extended thinking explicitly.
**Modes:**
| Mode | When | Execution | | --- | --- | --- | | **Interview** | Step 1 β scope | Sequential; ask questions, confirm before proceeding | | **Parallel research** | Steps 2β4 β evidence gathering | Fan out 3β20 sub-agents per step; each owns one axis | | **Synthesis** | Step 5 β conclusions | Sequential + ultrathink; reconcile conflicts before recommending |
**Research depth** β select automatically based on the request:
| Depth | When | Steps | | --- | --- | --- | | **Quick** | Narrow, time-sensitive question; user says "brief" or "quick" | Steps 1 (auto-scope), 2, 5 | | **Standard** | Typical research request [default] | Steps 1β5 | | **Deep** | Comprehensive review, critical decision; user says "thorough", "exhaustive", "comprehensive" | Steps 1β5 + 4.5 (outline refinement) + critique pass |
**Autonomy:** For specific, well-scoped prompts, state assumptions and proceed without a full interview β surface them in the report header instead. Reserve the full scope interview for genuinely vague prompts (e.g., "Research blockchain", "Tell me about AI").
**Questions:** Ask the user through the environment's question tool β never as plain-text prose. One question at a time, 2β4 tappable options, wait for the answer. If the environment has no question tool, ask in prose with the same options, one at a time.
## Critical rules
- Web search is the core capability of this skill. If the environment has no web access, halt immediately and tell the user. - **Every claim must cite a source URL.** Unsourced assertions are not findings β they are guesses. - Critical claims (market size, growth rates, competitive positioning...) require **2+ independent sources** or get `confidence: Low`. - Write findings to the output file **immediately after each step** β do not batch at the end. - Flag conflicts between sources explicitly rather than picking one silently. - **Prose-first:** Write in full sentences and paragraphs (aim for β₯80% prose). Use bullets only for true lists β never as the primary content delivery. "The market reached $4.2B in 2024 [Source]" is better than "\* Market: $4.2B". - **Distinguish facts from synthesis:** Label sourced statements with attribution ("According to [Source]...") and analytical conclusions with hedges ("This suggests...", "The pattern across sources indicates..."). Never present inference as fact. - **Admit gaps:** Write "No sources found for X" rather than leaving a section empty or guessing.
## Reference files
Load these files at the steps indicated only β not all upfront.
| File | Load at | | ------------------------------- | ----------------------------------- | | `references/citations.md` | Step 2 (before first search) | | `references/parallel-search.md` | Step 2 (before spawning sub-agents) | | `references/market.md` | Step 2, if type == market | | `references/domain.md` | Step 2, if type == domain | | `references/technical.md` | Step 2, if type == technical | | `references/competitive.md` | Step 2, if type == competitive | | `references/product.md` | Step 2, if type == product | | `references/academic.md` | Step 2, if type == academic | | `references/org.md` | Step 2, if type == person/org | | `references/financial.md` | Step 2, if type == financial | | `references/legal.md` | Step 2, if type == legal | | `references/trend.md` | Step 2, if type == trend | | `references/community.md` | Step 2, if type == community |
## Step 1 β Scope
First, get today's date: `date +%Y-%m-%d`. Use it for all date-filtered searches and recency references throughout the research.
**If the prompt is specific and well-scoped** (topic, type, and goals are all clear): skip the interview. Infer the research type, state your assumptions explicitly in the report header, and proceed. Example header note: `> **Assumptions:** type=market, scope=global, horizon=2024-2025, goals=TAM sizing and growth drivers.`
**If the prompt is vague or ambiguous** (e.g., "Research blockchain", "Tell me about AI"): ask the user:
1. What type? (see list below) 2. What specific questions or goals should the research answer? 3. Any geographic, time, or segment constraints?
Research types:
- `market` β customers, competition, sizing, pricing, trends - `domain` β industry structure, regulatory landscape, ecosystem - `technical` β architecture, tools, benchmarks, integration - `competitive` β focused competitor teardown: positioning, reviews, win/loss signals - `product` β deep analysis of a specific product: features, UX, roadmap signals, changelog - `academic` β literature survey, citation networks, state of research, key authors - `person/org` β due diligence on a company or public figure: funding, leadership, press, controversies - `financial` β funding rounds, valuation multiples, revenue signals, investor patterns - `legal` β IP landscape, patents, litigation history, regulatory enforcement, contract norms - `trend` β emerging signals, weak signals, foresight, scenario mapping - `community` β ecosystem health, key voices, governance dynamics, fragmentation risks - If none fit, infer the type and design your own axis breakdown β the process (fan-out, citation discipline, write-as-you-go, synthesis) is the same regardless of type.
Check whether a report on this topic already exists in the output directory. If found, summarize what it covers and ask: extend or start fresh?
Set output path: `./research/{type}-{topic}-{YYYY-MM-DD}.md` (lowercase, hyphens). Ask if the user wants a different path. Load `assets/report-template.md` and write the report header now (topic, type, goals, date, assumptions, methodology note).
## Step 2 β Core research (parallel fan-out)
Load `references/citations.md` and `references/parallel-search.md`. Load the type-specific reference file.
Spawn **3β20 sub-agents in a single message** (one per axis from the type reference). Each agent:
- Searches its axis on the web and fetches the sources it cites - Writes findings as prose paragraphs with inline citations β not bullet lists - Returns URL, accessed date, and confidence level per claim - Tags each source: **Primary** (official docs, filings, peer-reviewed), **Established** (major publications, analyst firms), or **Low** (blogs, forums, single opinions). Flag Low-tier sources prominently. - Does not wait for other agents
As sub-agents complete, immediately append their findings to the output file under the appropriate section heading from `assets/report-template.md`. Do not wait for all agents to finish before writing.
## Step 3 β Competitive / landscape analysis (parallel fan-out)
Spawn 3β5 sub-agents covering the axes defined in the type reference file's landscape section. Same citation discipline. Append results to the output file immediately.
## Step 4 β Deep dive (parallel fan-out)
Spawn sub-agents covering the deep-dive axes for the chosen type (see type reference file). Append results immediately.
## Step 4.5 β Outline refinement (deep mode only)
After Steps 2β4, review whether the evidence warrants restructuring before synthesis. Ask:
- Did findings contradict the initial scope assumptions? - Did an important angle emerge that wasn't in the original plan? - Are any sections underpowered by evidence β or overloaded?
If yes: adapt the outline. Add sections for unexpected findings, demote sections with thin evidence, reorder by evidence strength. Run 2β3 targeted gap-fill searches for newly identified angles (time-box to 5 minutes). Document what changed and why in the report's methodology note.
Skip in quick and standard modes.
## Step 5 β Synthesis
**Use `ultrathink` here** (standard and deep modes).
Read the full output file. Write the synthesis section:
```md ## Key Findings
(5 critical insights written as prose paragraphs, each with a source reference)
## Strategic Recommendations
1. [Recommendation] β Rationale. Evidence: [source]. 2. ... (3β5 recommendations, ranked by impact)
## Risks and Uncertainties
- Data gaps: what could not be found or confirmed - Low-confidence claims requiring further validation - Conflicts between sources that could not be resolved - Domain or market risks to monitor
## Next Steps
- Recommended follow-up research - If the initial request is not fulfilled, loop on step 1 and ask more questions - Decisions this research enables ```
Keep the fact/synthesis distinction throughout: "According to [Source], X" for sourced claims; "This suggests Y" for your analysis. If a recommendation rests on Low-confidence data, say so explicitly.
**Critique pass (deep mode only):** Before finalizing, red-team the synthesis. Ask: What's missing? What could be wrong? What alternative explanations exist? What biases might be present? If a critical gap emerges, run 2β3 delta-queries to fill it before concluding.
## Step 6 β PDF export (optional)
After the Markdown report is final, offer this step if the user wants a PDF.
Try each tool in order, stop at the first that works:
Source provenance
Decision snapshot
recent repository activity
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 deep-research, ready for a manual X post.
deep-research: Deep research on any topic β broad parallel web searches, multi-source validation, confidence... 203 stars https://www.openagentskill.com/skills/samber-deep-research?ref=x
Listing + install path for deep-research: https://www.openagentskill.com/skills/samber-deep-research?ref=x Install: npx skills add samber/cc-skills --skill deep-research
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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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsSandbox only
Install targets
Codex install prompt
Install the "deep-research" agent skill from https://github.com/samber/cc-skills/tree/main/skills/deep-research. 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: Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature revie 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":"samber-deep-research","task":"Install deep-research","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 samber/cc-skills --skill deep-research
Maintenance
fresh
3d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
203
70/100 Quality Β· 69/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision Β· No explicit guardrail against prompt injection from fetched web content; the skill instructs fetching pages but does not state that page content must be treated as untrusted data, not as instructions.
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
203 GitHub stars
Repo activity
203 stars, 15 forks
Maintenance
3d since push
License
MIT
Install
npx skills add samber/cc-skills --skill deep-research
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 samber/cc-skills --skill deep-researchDo 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
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
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.
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%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/samber-deep-research/install
Agent should check
Copy prompt
Task: Use deep-research in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/samber-deep-research/install
Install command: npx skills add samber/cc-skills --skill deep-research
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/samber-deep-research/install
LLM text format
/api/skills/samber-deep-research/install?format=text
Find alternatives
/api/skills/search?q=deep-research&limit=3
Agent prompt
Use deep-research for this task. Review https://www.openagentskill.com/api/skills/samber-deep-research/install, then install with: npx skills add samber/cc-skills --skill deep-researchRegistry 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/samber-deep-research
LLM text
/api/registry/manifest/samber-deep-research?format=text
Install alias
/api/registry/install/samber-deep-research
Recommend
/api/registry/recommend?task=Use%20deep-research%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
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
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
INFO203 GitHub stars
Stars/forks activity
CHECK203 stars, 15 forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSMIT
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
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
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.
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.
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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
A relentless interview to sharpen a plan or design.
--- name: deep-research description: "Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature review, citation networks, key authors), person/org (due diligence on a company or public figure), financial (funding landscape, valuation multiples, revenue signals), legal (IP, patent landscape, litigation, compliance), trend (emerging signals, foresight, scenario mapping), community (ecosystem health, key voices, governance, fragmentation). Trigger even when phrased casually: 'look into X', 'what's the deal with Y', 'dig into Z', 'I need to understand the space', 'catch me up on X'. Do NOT use for single-fact lookups or one-off web questions." user-invocable: true license: MIT compatibility: Designed for Claude Code, Codex or similar harness. Requires internet access (web search and page fetching). metadata: author: samber authors: - Maxme Courant (github.com/mcourant) - Samuel Berthe (github.com/samber) version: "1.2.1" openclaw: emoji: "π" homepage: https://github.com/samber/cc-skills install: - kind: brew formula: curl bins: [curl] - kind: brew formula: pandoc bins: [pandoc] - kind: node package: md-to-pdf bins: [md-to-pdf] allowed-tools: Read Edit Write Glob Grep Agent WebFetch WebSearch AskUserQuestion Bash(curl:*) Bash(pandoc:*) Bash(md-to-pdf:*) ---
**Persona:** You are a senior research analyst. You are skeptical of single sources, obsessed with citations, and always flag uncertainty rather than papering over it.
**Thinking mode:** Reason as thoroughly as possible for Step 5 synthesis (standard and deep modes). Reconciling conflicting multi-source data and ranking recommendations requires deep reasoning β shallow inference produces wrong conclusions. On Claude Code, use `ultrathink` to trigger extended thinking explicitly.
**Modes:**
| Mode | When | Execution | | --- | --- | --- | | **Interview** | Step 1 β scope | Sequential; ask questions, confirm before proceeding | | **Parallel research** | Steps 2β4 β evidence gathering | Fan out 3β20 sub-agents per step; each owns one axis | | **Synthesis** | Step 5 β conclusions | Sequential + ultrathink; reconcile conflicts before recommending |
**Research depth** β select automatically based on the request:
| Depth | When | Steps | | --- | --- | --- | | **Quick** | Narrow, time-sensitive question; user says "brief" or "quick" | Steps 1 (auto-scope), 2, 5 | | **Standard** | Typical research request [default] | Steps 1β5 | | **Deep** | Comprehensive review, critical decision; user says "thorough", "exhaustive", "comprehensive" | Steps 1β5 + 4.5 (outline refinement) + critique pass |
**Autonomy:** For specific, well-scoped prompts, state assumptions and proceed without a full interview β surface them in the report header instead. Reserve the full scope interview for genuinely vague prompts (e.g., "Research blockchain", "Tell me about AI").
**Questions:** Ask the user through the environment's question tool β never as plain-text prose. One question at a time, 2β4 tappable options, wait for the answer. If the environment has no question tool, ask in prose with the same options, one at a time.
## Critical rules
- Web search is the core capability of this skill. If the environment has no web access, halt immediately and tell the user. - **Every claim must cite a source URL.** Unsourced assertions are not findings β they are guesses. - Critical claims (market size, growth rates, competitive positioning...) require **2+ independent sources** or get `confidence: Low`. - Write findings to the output file **immediately after each step** β do not batch at the end. - Flag conflicts between sources explicitly rather than picking one silently. - **Prose-first:** Write in full sentences and paragraphs (aim for β₯80% prose). Use bullets only for true lists β never as the primary content delivery. "The market reached $4.2B in 2024 [Source]" is better than "\* Market: $4.2B". - **Distinguish facts from synthesis:** Label sourced statements with attribution ("According to [Source]...") and analytical conclusions with hedges ("This suggests...", "The pattern across sources indicates..."). Never present inference as fact. - **Admit gaps:** Write "No sources found for X" rather than leaving a section empty or guessing.
## Reference files
Load these files at the steps indicated only β not all upfront.
| File | Load at | | ------------------------------- | ----------------------------------- | | `references/citations.md` | Step 2 (before first search) | | `references/parallel-search.md` | Step 2 (before spawning sub-agents) | | `references/market.md` | Step 2, if type == market | | `references/domain.md` | Step 2, if type == domain | | `references/technical.md` | Step 2, if type == technical | | `references/competitive.md` | Step 2, if type == competitive | | `references/product.md` | Step 2, if type == product | | `references/academic.md` | Step 2, if type == academic | | `references/org.md` | Step 2, if type == person/org | | `references/financial.md` | Step 2, if type == financial | | `references/legal.md` | Step 2, if type == legal | | `references/trend.md` | Step 2, if type == trend | | `references/community.md` | Step 2, if type == community |
## Step 1 β Scope
First, get today's date: `date +%Y-%m-%d`. Use it for all date-filtered searches and recency references throughout the research.
**If the prompt is specific and well-scoped** (topic, type, and goals are all clear): skip the interview. Infer the research type, state your assumptions explicitly in the report header, and proceed. Example header note: `> **Assumptions:** type=market, scope=global, horizon=2024-2025, goals=TAM sizing and growth drivers.`
**If the prompt is vague or ambiguous** (e.g., "Research blockchain", "Tell me about AI"): ask the user:
1. What type? (see list below) 2. What specific questions or goals should the research answer? 3. Any geographic, time, or segment constraints?
Research types:
- `market` β customers, competition, sizing, pricing, trends - `domain` β industry structure, regulatory landscape, ecosystem - `technical` β architecture, tools, benchmarks, integration - `competitive` β focused competitor teardown: positioning, reviews, win/loss signals - `product` β deep analysis of a specific product: features, UX, roadmap signals, changelog - `academic` β literature survey, citation networks, state of research, key authors - `person/org` β due diligence on a company or public figure: funding, leadership, press, controversies - `financial` β funding rounds, valuation multiples, revenue signals, investor patterns - `legal` β IP landscape, patents, litigation history, regulatory enforcement, contract norms - `trend` β emerging signals, weak signals, foresight, scenario mapping - `community` β ecosystem health, key voices, governance dynamics, fragmentation risks - If none fit, infer the type and design your own axis breakdown β the process (fan-out, citation discipline, write-as-you-go, synthesis) is the same regardless of type.
Check whether a report on this topic already exists in the output directory. If found, summarize what it covers and ask: extend or start fresh?
Set output path: `./research/{type}-{topic}-{YYYY-MM-DD}.md` (lowercase, hyphens). Ask if the user wants a different path. Load `assets/report-template.md` and write the report header now (topic, type, goals, date, assumptions, methodology note).
## Step 2 β Core research (parallel fan-out)
Load `references/citations.md` and `references/parallel-search.md`. Load the type-specific reference file.
Spawn **3β20 sub-agents in a single message** (one per axis from the type reference). Each agent:
- Searches its axis on the web and fetches the sources it cites - Writes findings as prose paragraphs with inline citations β not bullet lists - Returns URL, accessed date, and confidence level per claim - Tags each source: **Primary** (official docs, filings, peer-reviewed), **Established** (major publications, analyst firms), or **Low** (blogs, forums, single opinions). Flag Low-tier sources prominently. - Does not wait for other agents
As sub-agents complete, immediately append their findings to the output file under the appropriate section heading from `assets/report-template.md`. Do not wait for all agents to finish before writing.
## Step 3 β Competitive / landscape analysis (parallel fan-out)
Spawn 3β5 sub-agents covering the axes defined in the type reference file's landscape section. Same citation discipline. Append results to the output file immediately.
## Step 4 β Deep dive (parallel fan-out)
Spawn sub-agents covering the deep-dive axes for the chosen type (see type reference file). Append results immediately.
## Step 4.5 β Outline refinement (deep mode only)
After Steps 2β4, review whether the evidence warrants restructuring before synthesis. Ask:
- Did findings contradict the initial scope assumptions? - Did an important angle emerge that wasn't in the original plan? - Are any sections underpowered by evidence β or overloaded?
If yes: adapt the outline. Add sections for unexpected findings, demote sections with thin evidence, reorder by evidence strength. Run 2β3 targeted gap-fill searches for newly identified angles (time-box to 5 minutes). Document what changed and why in the report's methodology note.
Skip in quick and standard modes.
## Step 5 β Synthesis
**Use `ultrathink` here** (standard and deep modes).
Read the full output file. Write the synthesis section:
```md ## Key Findings
(5 critical insights written as prose paragraphs, each with a source reference)
## Strategic Recommendations
1. [Recommendation] β Rationale. Evidence: [source]. 2. ... (3β5 recommendations, ranked by impact)
## Risks and Uncertainties
- Data gaps: what could not be found or confirmed - Low-confidence claims requiring further validation - Conflicts between sources that could not be resolved - Domain or market risks to monitor
## Next Steps
- Recommended follow-up research - If the initial request is not fulfilled, loop on step 1 and ask more questions - Decisions this research enables ```
Keep the fact/synthesis distinction throughout: "According to [Source], X" for sourced claims; "This suggests Y" for your analysis. If a recommendation rests on Low-confidence data, say so explicitly.
**Critique pass (deep mode only):** Before finalizing, red-team the synthesis. Ask: What's missing? What could be wrong? What alternative explanations exist? What biases might be present? If a critical gap emerges, run 2β3 delta-queries to fill it before concluding.
## Step 6 β PDF export (optional)
After the Markdown report is final, offer this step if the user wants a PDF.
Try each tool in order, stop at the first that works:
Source provenance
Decision snapshot
recent repository activity
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 deep-research, ready for a manual X post.
deep-research: Deep research on any topic β broad parallel web searches, multi-source validation, confidence... 203 stars https://www.openagentskill.com/skills/samber-deep-research?ref=x
Listing + install path for deep-research: https://www.openagentskill.com/skills/samber-deep-research?ref=x Install: npx skills add samber/cc-skills --skill deep-research
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
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Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
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@samber
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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsSandbox only
Install targets
Codex install prompt
Install the "deep-research" agent skill from https://github.com/samber/cc-skills/tree/main/skills/deep-research. 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: Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature revie 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":"samber-deep-research","task":"Install deep-research","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 samber/cc-skills --skill deep-research
Maintenance
fresh
3d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
203
70/100 Quality Β· 69/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision Β· No explicit guardrail against prompt injection from fetched web content; the skill instructs fetching pages but does not state that page content must be treated as untrusted data, not as instructions.
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
203 GitHub stars
Repo activity
203 stars, 15 forks
Maintenance
3d since push
License
MIT
Install
npx skills add samber/cc-skills --skill deep-research
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 samber/cc-skills --skill deep-researchDo 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
256.3K Stars
npx skills add mattpocock/skills --skill grill-me
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
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.
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%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/samber-deep-research/install
Agent should check
Copy prompt
Task: Use deep-research in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20deep-research%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/samber-deep-research/install
Install command: npx skills add samber/cc-skills --skill deep-research
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/samber-deep-research/install
LLM text format
/api/skills/samber-deep-research/install?format=text
Find alternatives
/api/skills/search?q=deep-research&limit=3
Agent prompt
Use deep-research for this task. Review https://www.openagentskill.com/api/skills/samber-deep-research/install, then install with: npx skills add samber/cc-skills --skill deep-researchRegistry 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/samber-deep-research
LLM text
/api/registry/manifest/samber-deep-research?format=text
Install alias
/api/registry/install/samber-deep-research
Recommend
/api/registry/recommend?task=Use%20deep-research%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
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
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
INFO203 GitHub stars
Stars/forks activity
CHECK203 stars, 15 forks; issue activity unavailable in current metadata
Recent maintenance
PASS3d since push
License clarity
PASSMIT
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
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Manage repositories
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Publish consistently
I need my agent to turn research and product updates into useful content drafts.
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.
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.
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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
A relentless interview to sharpen a plan or design.
--- name: deep-research description: "Deep research on any topic β broad parallel web searches, multi-source validation, confidence tracking, and a cited Markdown report. Use whenever the deliverable is a thorough sourced report rather than a quick answer: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Covers 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory overview), technical (architecture, tooling, benchmarks, technology evaluation), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, teardowns, roadmap signals), academic (literature review, citation networks, key authors), person/org (due diligence on a company or public figure), financial (funding landscape, valuation multiples, revenue signals), legal (IP, patent landscape, litigation, compliance), trend (emerging signals, foresight, scenario mapping), community (ecosystem health, key voices, governance, fragmentation). Trigger even when phrased casually: 'look into X', 'what's the deal with Y', 'dig into Z', 'I need to understand the space', 'catch me up on X'. Do NOT use for single-fact lookups or one-off web questions." user-invocable: true license: MIT compatibility: Designed for Claude Code, Codex or similar harness. Requires internet access (web search and page fetching). metadata: author: samber authors: - Maxme Courant (github.com/mcourant) - Samuel Berthe (github.com/samber) version: "1.2.1" openclaw: emoji: "π" homepage: https://github.com/samber/cc-skills install: - kind: brew formula: curl bins: [curl] - kind: brew formula: pandoc bins: [pandoc] - kind: node package: md-to-pdf bins: [md-to-pdf] allowed-tools: Read Edit Write Glob Grep Agent WebFetch WebSearch AskUserQuestion Bash(curl:*) Bash(pandoc:*) Bash(md-to-pdf:*) ---
**Persona:** You are a senior research analyst. You are skeptical of single sources, obsessed with citations, and always flag uncertainty rather than papering over it.
**Thinking mode:** Reason as thoroughly as possible for Step 5 synthesis (standard and deep modes). Reconciling conflicting multi-source data and ranking recommendations requires deep reasoning β shallow inference produces wrong conclusions. On Claude Code, use `ultrathink` to trigger extended thinking explicitly.
**Modes:**
| Mode | When | Execution | | --- | --- | --- | | **Interview** | Step 1 β scope | Sequential; ask questions, confirm before proceeding | | **Parallel research** | Steps 2β4 β evidence gathering | Fan out 3β20 sub-agents per step; each owns one axis | | **Synthesis** | Step 5 β conclusions | Sequential + ultrathink; reconcile conflicts before recommending |
**Research depth** β select automatically based on the request:
| Depth | When | Steps | | --- | --- | --- | | **Quick** | Narrow, time-sensitive question; user says "brief" or "quick" | Steps 1 (auto-scope), 2, 5 | | **Standard** | Typical research request [default] | Steps 1β5 | | **Deep** | Comprehensive review, critical decision; user says "thorough", "exhaustive", "comprehensive" | Steps 1β5 + 4.5 (outline refinement) + critique pass |
**Autonomy:** For specific, well-scoped prompts, state assumptions and proceed without a full interview β surface them in the report header instead. Reserve the full scope interview for genuinely vague prompts (e.g., "Research blockchain", "Tell me about AI").
**Questions:** Ask the user through the environment's question tool β never as plain-text prose. One question at a time, 2β4 tappable options, wait for the answer. If the environment has no question tool, ask in prose with the same options, one at a time.
## Critical rules
- Web search is the core capability of this skill. If the environment has no web access, halt immediately and tell the user. - **Every claim must cite a source URL.** Unsourced assertions are not findings β they are guesses. - Critical claims (market size, growth rates, competitive positioning...) require **2+ independent sources** or get `confidence: Low`. - Write findings to the output file **immediately after each step** β do not batch at the end. - Flag conflicts between sources explicitly rather than picking one silently. - **Prose-first:** Write in full sentences and paragraphs (aim for β₯80% prose). Use bullets only for true lists β never as the primary content delivery. "The market reached $4.2B in 2024 [Source]" is better than "\* Market: $4.2B". - **Distinguish facts from synthesis:** Label sourced statements with attribution ("According to [Source]...") and analytical conclusions with hedges ("This suggests...", "The pattern across sources indicates..."). Never present inference as fact. - **Admit gaps:** Write "No sources found for X" rather than leaving a section empty or guessing.
## Reference files
Load these files at the steps indicated only β not all upfront.
| File | Load at | | ------------------------------- | ----------------------------------- | | `references/citations.md` | Step 2 (before first search) | | `references/parallel-search.md` | Step 2 (before spawning sub-agents) | | `references/market.md` | Step 2, if type == market | | `references/domain.md` | Step 2, if type == domain | | `references/technical.md` | Step 2, if type == technical | | `references/competitive.md` | Step 2, if type == competitive | | `references/product.md` | Step 2, if type == product | | `references/academic.md` | Step 2, if type == academic | | `references/org.md` | Step 2, if type == person/org | | `references/financial.md` | Step 2, if type == financial | | `references/legal.md` | Step 2, if type == legal | | `references/trend.md` | Step 2, if type == trend | | `references/community.md` | Step 2, if type == community |
## Step 1 β Scope
First, get today's date: `date +%Y-%m-%d`. Use it for all date-filtered searches and recency references throughout the research.
**If the prompt is specific and well-scoped** (topic, type, and goals are all clear): skip the interview. Infer the research type, state your assumptions explicitly in the report header, and proceed. Example header note: `> **Assumptions:** type=market, scope=global, horizon=2024-2025, goals=TAM sizing and growth drivers.`
**If the prompt is vague or ambiguous** (e.g., "Research blockchain", "Tell me about AI"): ask the user:
1. What type? (see list below) 2. What specific questions or goals should the research answer? 3. Any geographic, time, or segment constraints?
Research types:
- `market` β customers, competition, sizing, pricing, trends - `domain` β industry structure, regulatory landscape, ecosystem - `technical` β architecture, tools, benchmarks, integration - `competitive` β focused competitor teardown: positioning, reviews, win/loss signals - `product` β deep analysis of a specific product: features, UX, roadmap signals, changelog - `academic` β literature survey, citation networks, state of research, key authors - `person/org` β due diligence on a company or public figure: funding, leadership, press, controversies - `financial` β funding rounds, valuation multiples, revenue signals, investor patterns - `legal` β IP landscape, patents, litigation history, regulatory enforcement, contract norms - `trend` β emerging signals, weak signals, foresight, scenario mapping - `community` β ecosystem health, key voices, governance dynamics, fragmentation risks - If none fit, infer the type and design your own axis breakdown β the process (fan-out, citation discipline, write-as-you-go, synthesis) is the same regardless of type.
Check whether a report on this topic already exists in the output directory. If found, summarize what it covers and ask: extend or start fresh?
Set output path: `./research/{type}-{topic}-{YYYY-MM-DD}.md` (lowercase, hyphens). Ask if the user wants a different path. Load `assets/report-template.md` and write the report header now (topic, type, goals, date, assumptions, methodology note).
## Step 2 β Core research (parallel fan-out)
Load `references/citations.md` and `references/parallel-search.md`. Load the type-specific reference file.
Spawn **3β20 sub-agents in a single message** (one per axis from the type reference). Each agent:
- Searches its axis on the web and fetches the sources it cites - Writes findings as prose paragraphs with inline citations β not bullet lists - Returns URL, accessed date, and confidence level per claim - Tags each source: **Primary** (official docs, filings, peer-reviewed), **Established** (major publications, analyst firms), or **Low** (blogs, forums, single opinions). Flag Low-tier sources prominently. - Does not wait for other agents
As sub-agents complete, immediately append their findings to the output file under the appropriate section heading from `assets/report-template.md`. Do not wait for all agents to finish before writing.
## Step 3 β Competitive / landscape analysis (parallel fan-out)
Spawn 3β5 sub-agents covering the axes defined in the type reference file's landscape section. Same citation discipline. Append results to the output file immediately.
## Step 4 β Deep dive (parallel fan-out)
Spawn sub-agents covering the deep-dive axes for the chosen type (see type reference file). Append results immediately.
## Step 4.5 β Outline refinement (deep mode only)
After Steps 2β4, review whether the evidence warrants restructuring before synthesis. Ask:
- Did findings contradict the initial scope assumptions? - Did an important angle emerge that wasn't in the original plan? - Are any sections underpowered by evidence β or overloaded?
If yes: adapt the outline. Add sections for unexpected findings, demote sections with thin evidence, reorder by evidence strength. Run 2β3 targeted gap-fill searches for newly identified angles (time-box to 5 minutes). Document what changed and why in the report's methodology note.
Skip in quick and standard modes.
## Step 5 β Synthesis
**Use `ultrathink` here** (standard and deep modes).
Read the full output file. Write the synthesis section:
```md ## Key Findings
(5 critical insights written as prose paragraphs, each with a source reference)
## Strategic Recommendations
1. [Recommendation] β Rationale. Evidence: [source]. 2. ... (3β5 recommendations, ranked by impact)
## Risks and Uncertainties
- Data gaps: what could not be found or confirmed - Low-confidence claims requiring further validation - Conflicts between sources that could not be resolved - Domain or market risks to monitor
## Next Steps
- Recommended follow-up research - If the initial request is not fulfilled, loop on step 1 and ask more questions - Decisions this research enables ```
Keep the fact/synthesis distinction throughout: "According to [Source], X" for sourced claims; "This suggests Y" for your analysis. If a recommendation rests on Low-confidence data, say so explicitly.
**Critique pass (deep mode only):** Before finalizing, red-team the synthesis. Ask: What's missing? What could be wrong? What alternative explanations exist? What biases might be present? If a critical gap emerges, run 2β3 delta-queries to fill it before concluding.
## Step 6 β PDF export (optional)
After the Markdown report is final, offer this step if the user wants a PDF.
Try each tool in order, stop at the first that works:
Source provenance
Decision snapshot
recent repository activity
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 deep-research, ready for a manual X post.
deep-research: Deep research on any topic β broad parallel web searches, multi-source validation, confidence... 203 stars https://www.openagentskill.com/skills/samber-deep-research?ref=x
Listing + install path for deep-research: https://www.openagentskill.com/skills/samber-deep-research?ref=x Install: npx skills add samber/cc-skills --skill deep-research
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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 εθ²ζ΅·ζ₯γεθ²ε°ε·γεθ²θ°θ§θ§γθθ²/η»Ώθ²εηε°ε·γrisographγη½ηΉη §ηγε€ε€ζε½δ»£ηΌθΎζηγzine 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 β write β review β revise β finalize
38.4K Starsgrill-me
A relentless interview to sharpen a plan or design.
256.3K StarsPermission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness