Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/raja21068/AutoResearchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/raja21068/autoresearch/documentation-lookup-skill)<a href="https://agentmods.dev/agents/raja21068/autoresearch/documentation-lookup-skill"><img src="https://agentmods.dev/badge/agents/raja21068/autoresearch/documentation-lookup-skill.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.01146 |
| Opus 5 | $0.00026 | $0.00573 |
| Sonnet 5 | $0.00010 | $0.00229 |
| Haiku 4.5 | $0.00005 | $0.00115 |
Grade A, and why
documentation-lookup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Lookup (Context7)
When the user asks about libraries, frameworks, or APIs, fetch current documentation via the Context7 MCP (tools resolve-library-id and query-docs) instead of relying on training data.
Core Concepts
- Context7: MCP server that exposes live documentation; use it instead of training data for libraries and APIs.
- resolve-library-id: Returns Context7-compatible library IDs (e.g.
/vercel/next.js) from a library name and query. - query-docs: Fetches documentation and code snippets for a given library ID and question. Always call resolve-library-id first to get a valid library ID.
When to use
Activate when the user:
- Asks setup or configuration questions (e.g. "How do I configure Next.js middleware?")
- Requests code that depends on a library ("Write a Prisma query for...")
- Needs API or reference information ("What are the Supabase auth methods?")
- Mentions specific frameworks or libraries (React, Vue, Svelte, Express, Tailwind, Prisma, Supabase, etc.)
Use this skill whenever the request depends on accurate, up-to-date behavior of a library, framework, or API. Applies across harnesses that have the Context7 MCP configured (e.g. Claude Code, Cursor, Codex).
How it works
Step 1: Resolve the Library ID
Call the resolve-library-id MCP tool with:
- libraryName: The library or product name taken from the user's question (e.g.
Next.js,Prisma,Supabase). - query: The user's full question. This improves relevance ranking of results.
You must obtain a Context7-compatible library ID (format /org/project or /org/project/version) before querying docs. Do not call query-docs without a valid library ID from this step.
Step 2: Select the Best Match
From the resolution results, choose one result using:
- Name match: Prefer exact or closest match to what the user asked for.
- Benchmark score: Higher scores indicate better documentation quality (100 is highest).
- Source reputation: Prefer High or Medium reputation when available.
- Version: If the user specified a version (e.g. "React 19", "Next.js 15"), prefer a version-specific library ID if listed (e.g.
/org/project/v1.2.0).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 91 lines · 51 tokens per session scan A 89cc655f72a5
documentation-lookup is an agent published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 1,146 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
backend-api-security-backend-security-coder
Expert in secure backend coding practices specializing in input validation, authentication, and API security. Use PROACTIVELY for backend security implementations or security code reviews.
pre-explorer
Pre-explores codebase before plan drafting. Produces requirements-extract.json, code-map.json, and investigation-log.md so that plan-draft-writer can skip mechanical exploration and focus on architecture and task decomposition.
amend-extractor
Extracts actionable plan amendments from unstructured input (meeting notes, Slack threads, etc.).
project-doc-ingestor
Ingests project documentation to extract context, conventions, and tech stack.
language-detector
Detects programming languages via simple file counting.
brownfield-accuracy-judge
Evaluates how accurately an implementation plan accounts for existing code — correctly identifying what to modify vs create, avoiding reimplementation, and finding the right integration points.