Borrowing it
Nothing to install: this file belongs to Morningstar202604/awesome-skillkit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Morningstar202604/awesome-skillkit/main/AGENTS.mdgit clone --depth 1 https://github.com/Morningstar202604/awesome-skillkitWrote 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/instructions/morningstar202604/awesome-skillkit/agents-md)<a href="https://agentmods.dev/instructions/morningstar202604/awesome-skillkit/agents-md"><img src="https://agentmods.dev/badge/instructions/morningstar202604/awesome-skillkit/agents-md/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/instructions/morningstar202604/awesome-skillkit/agents-md"><img src="https://agentmods.dev/badge/instructions/morningstar202604/awesome-skillkit/agents-md.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 | $0.01158 | $0.01158 |
| Opus 5.5 | $0.00463 | $0.00463 |
| Sonnet 5.5 | $0.00232 | $0.00232 |
| Haiku 4.5 | $0.00116 | $0.00116 |
Grade A, and why
awesome-skillkit AGENTS.md 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 25d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — 使用本仓库的全局规则（AI Agent 必读）
本文件是 awesome-skillkit 面向 AI agent 的全局指令。opencode / Codex 等宿主会自动加载本文件；Claude Code 用户可将本文件内容复制为 CLAUDE.md。人类贡献者请看 CONTRIBUTING.md。
规则 0：先查技能，再动手（skill-first）
本规则只在本仓库内生效。技能安装到用户工具目录后的仓库外版本（含粘贴即用的全局规则块）见 GLOBAL-RULES.md。
先跑判别器：python3 skills/meta/skill-router/scripts/skill_router.py "<任务>" --skills-dir skills，按结论分流（USE→用技能 / WEAK→自己看 SKILL.md / NO→直接干活）。
每个任务开始时，先确认本仓库是否已有能完成该任务的技能；任务进行中每进入一个新阶段、或遇到新的子问题，再查一次。查到匹配的就必须用上；查不到就正常干活，不许硬套。
三个查法（任一命中即可）：
- 扫描述：浏览
skills/<域>/<子类>/<技能名>/SKILL.md的 frontmatterdescription（另有面向中文的description_zh）——其中的触发词（「当用户…」「Use when…」「Do NOT…」）就是匹配依据。skills/顶级目录即域（27 个，与skills/skill_chains.json的域一一对应）。 - 关键词检索（在仓库根目录运行，中英文均可）：
数据实时读自python3 skills/meta/skill-finder/scripts/find_skill.py search <关键词>manifest.json与真实SKILL.md，可加--json、--top N、--category、--group（按 8 大场景库过滤，如--group software/--group 软件开发）。 - 多步任务查链：
skills/skill_chains.json里domains[].entry是该域的编排器（入口技能），chains是既有工序链。从编排器进入，按链的 step 顺序推进。
命中后的执行纪律：
- 先读完整个
SKILL.md再开工，遵守它的流程与边界（尤其是 Do NOT）； - 技能写明「交给 XX 技能」（chain / sibling 引用）时，把那个技能也查出来一起用；
SKILL.md的约定与你的默认习惯冲突时，以SKILL.md为准。
中途需要回到规则 0 再查一次的信号：产出要进入下一阶段（写作→发布、数据→图表、草稿→审计）、当前技能明确点名了下游技能、或你正准备手搓一个技能描述里已经写好的流程。
专家团队资产（expert-teams/）
仓库内 expert-teams/ 子目录是一套平台中立的专家团队资产（18 支领域团队 / 219 位专家 agent / 100 个技能，原独立仓库 ai-expert-teams，2026-09-27 整体迁入）。跨领域的大任务（论文、全栈应用、数学建模、设计、写作、视频、数据分析、营销、电商、产品、财务、HR、法务、翻译、教育、音频、游戏）先读 expert-teams/project-director.md 按场景路由，或把 expert-teams/teams/<team>/agents/*.md 作为子 agent 定义派发。
- 该子目录文档里的相对路径以
expert-teams/为根；在仓库根引用或派发时加前缀（如expert-teams/teams/hr-team/agents/hr-team-lead）。 - 子目录自带门禁，改动用它自己的命令验证：
python3 expert-teams/verify.py # 需 pyyaml
python3 -m unittest discover -s expert-teams/tests
python3 expert-teams/build-site.py --check
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.
- 25d ago Changed · +4 lines · +104 tokens per session 56b71ab8ec1f
- 27d ago Changed · +44 tokens per session 34bcb24b8d02
- 28d ago First seen · 49 lines · 1,010 tokens per session scan A 7f99dde44eaa
awesome-skillkit AGENTS.md is an instructions file published in the GitHub repository Morningstar202604/awesome-skillkit (0 stars, last pushed 25d ago), licensed Apache-2.0. It adds 1,158 tokens to every session, about $0.0046 per session on Opus 5.5. 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 2026-09-27.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.