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.
npx skills add BenedictKing/benedictking-skills --skill context7-auto-researchgit clone --depth 1 https://github.com/BenedictKing/benedictking-skillsWrote 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/skills/benedictking/benedictking-skills/context7-auto-research)<a href="https://agentmods.dev/skills/benedictking/benedictking-skills/context7-auto-research"><img src="https://agentmods.dev/badge/skills/benedictking/benedictking-skills/context7-auto-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benedictking/benedictking-skills/context7-auto-research"><img src="https://agentmods.dev/badge/skills/benedictking/benedictking-skills/context7-auto-research.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00046 | $0.02373 |
| Opus 5 | $0.00023 | $0.01187 |
| Sonnet 5 | $0.00009 | $0.00475 |
| Haiku 4.5 | $0.00005 | $0.00237 |
Grade A, and why
context7-auto-research 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 10d 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 — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context7 Auto Research Skill
This skill automatically fetches current documentation from Context7 API when detecting library/framework-related queries, ensuring responses use up-to-date information instead of potentially outdated training data.
Automatic Activation Triggers
This skill should activate proactively when the user's message contains:
Implementation Queries (实现相关)
- "如何实现" / "怎么写" / "怎么做"
- "How do I..." / "How to..." / "How can I..."
- "Show me how to..." / "Write code for..."
Configuration & Setup (配置相关)
- "配置" / "设置" / "安装"
- "configure" / "setup" / "install"
- "初始化" / "initialize"
Documentation Requests (文档相关)
- "文档" / "参考" / "API"
- "documentation" / "docs" / "reference"
- "查看" / "look up"
Library/Framework Mentions (库/框架提及)
- React, Vue, Angular, Svelte, Solid
- Next.js, Nuxt, Remix, Astro
- Express, Fastify, Koa, Hono
- Prisma, Drizzle, TypeORM
- Supabase, Firebase, Clerk
- Tailwind, shadcn/ui, Radix
- Any npm package or GitHub repository
Code Generation Requests (代码生成)
- "生成代码" / "写一个" / "创建"
- "generate" / "create" / "build"
- "implement" / "add feature"
Research Process
When triggered, follow this workflow:
Step 1: Extract Library Information
Identify the library/framework from the user's query:
- Library name (e.g., "react", "next.js", "prisma")
- Version if specified (e.g., "React 19", "Next.js 15")
- Specific feature/API mentioned (e.g., "useEffect", "middleware", "relations")
Step 2: Search for Library
Use Task tool to call context7-fetcher sub-skill:
Task parameters:
- subagent_type: Bash
- description: "Search Context7 for library"
- prompt: node scripts/context7-api.cjs search "<library-name>" "<user-query>"
Example:
Task: Search for Next.js
Prompt: node scripts/context7-api.cjs search "next.js" "How to configure middleware in Next.js 15"
Response format:
{
"libraries": [
{
"id": "/vercel/next.js",
"name": "Next.js",
"description": "The React Framework",
"trustScore": 95,
"versions": ["v15.1.8", "v14.2.0", "v13.5.0"]
}
]
}
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 344 lines · 46 tokens per session scan A ae24c3837ad2
context7-auto-research is a skill published in the GitHub repository BenedictKing/benedictking-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 2,373 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
My Skill
Content here.
task-generation
Reference material with the canonical task-format grammar and decomposition rules for plan-to-tasks expansion. Loaded on demand by generate-tasks; not directly invokable.
implementation-standards
Reference material with coding standards (defensive coding, error handling, testing patterns). Loaded on demand by the Developer sub-agent (.github/agents/developer.md); not directly invokable.
spec-authoring
Reference material for writing product, technical, and operational specifications (work-item priorities, requirement families, success criteria). Loaded on demand by specify-feature; not directly invokable.
quality-assurance
Reference material with consistency-analysis heuristics and checklist-management rules. Loaded on demand by analyze-compliance and quality-control; not directly invokable.