Context7 research
Deep library, dependency, framework, and API documentation research using Context7 MCP tools only. Use when you need up-to-date docs, version-specific API reference, migration guidance, or to verify code against official documentation. Triggers on requests like “look up docs”, “API reference”, “how do I use X”, “latest/current”, “deprecated”, “breaking changes”, “best practices”, or whenever correctness depends on primary documentation.From its SKILL.md
npx -y skills add BjornMelin/dev-skills --skill context7-researchAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
3.1 KB, 621 tokens by cl100k_base, as published. Nobody here has run it
Context7 Research
Use Context7 as the source of truth for library/framework documentation during implementation, reviews, and debugging.
Workflow (MUST FOLLOW)
- Extract:
libraryName: product/package/framework name (e.g.,next.js,zod,supabase)objective: what you need to do (e.g., “configure SSR auth”, “migrate v3->v4”, “use hook X”)version: only if the user specifies one or you can infer it from repo context (otherwise default to “latest”).
- If the user already provided a Context7 library ID in
/org/project(or/org/project/version) format, skip resolution. - Otherwise call
mcp__context7__resolve-library-idwith:libraryName: extracted namequery: the full user objective (not just a keyword)
- Select the best match using this rubric:
- Exact/closest name match
- Highest source reputation
- Highest snippet coverage (prefer more snippets when reputation is similar)
- Highest benchmark score
- Versions available that match the requested version (if any)
- Call
mcp__context7__query-docswith the selectedlibraryIdand a tight query (see Query Budget). - Synthesize an answer:
- Include the
libraryIdused (and version if applicable) - Quote or paraphrase only what’s needed; prefer code examples
- If docs are missing/ambiguous, label the gap as
UNVERIFIEDand ask for the smallest clarification needed.
- Include the
Query Budget (HARD LIMIT)
Use at most 3 mcp__context7__query-docs calls per user request. Plan the queries up-front:
- Query 1 (overview): “Explain the concept and where it lives in the docs; include minimal example.”
- Query 2 (API details): “Exact function/class/method signatures and usage examples for the user’s task.”
- Query 3 (edge cases): “Pitfalls, errors, version differences, and recommended patterns.”
If you can’t cover everything within 3 calls, ask the user to narrow scope (or pick the highest-impact subquestion).
Output (DEFAULT SHAPE)
- Start with the recommended approach (1–5 bullets).
- Include at least one doc-backed example/snippet when the question is about code.
- End with “Assumptions / Gaps” if anything is uncertain (
UNVERIFIED) or version-dependent.
Resources
- Playbook:
references/playbook.md - Troubleshooting:
references/troubleshooting.md - Design + scoring (maintainers):
references/design-spec.md - Report template:
assets/report-template.md - Generate a new report file:
python3 scripts/new_report.py --out ./context7-research.md --library-name "<name>" --question "<objective>"
What ships with it: 6 files
8.2 KB alongside SKILL.md, 1 of them executable
agents/
- openai.yaml293 B
assets/
- report-template.md415 B
references/
- design-spec.md2.8 KB
- playbook.md1.9 KB
- troubleshooting.md1.1 KB
scripts/
- new_report.pyruns1.7 KB