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 agentmods add instructions/sanpingli/skills/agents-mdgit clone --depth 1 https://github.com/sanpingli/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/instructions/sanpingli/skills/agents-md)<a href="https://agentmods.dev/instructions/sanpingli/skills/agents-md"><img src="https://agentmods.dev/badge/instructions/sanpingli/skills/agents-md.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 | $0.00731 | $0.00731 |
| Opus 5 | $0.00365 | $0.00365 |
| Sonnet 5 | $0.00146 | $0.00146 |
| Haiku 4.5 | $0.00073 | $0.00073 |
Grade A, and why
skills 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 3d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Top-level agent routing file. AI agents (Claude Code / GitHub Copilot / Cursor / etc.) should read this file before doing anything else, locate the correct skill pack, then read that pack's internal
AGENTS.mdorCLAUDE.mdfor second-level routing.
Repository purpose
A multi-domain SKILL sharing repository. The repo itself contains no executable code — all domain knowledge and scripts are encapsulated inside the individual packs under packs/.
Routing table (level 1)
Pick a pack by task domain. After reading the pack's README.md + AGENTS.md, follow its internal routing to a specific SKILL.
| Task domain | Enter pack | Internal routing file |
|---|---|---|
| Building BREP modeling / IFC / DWG / compliance checklists / specifications / project documents | packs/aec-generation/ | packs/aec-generation/AGENTS.md |
PowerPoint generation: template profiling / brand-compliant decks / narrative composition / batch image generation / low-level .pptx operations |
packs/pptx-generation/ | packs/pptx-generation/AGENTS.md |
If the task does not fit any of the above, do not guess. Tell the user the repository does not yet cover that domain, and suggest creating a new pack via CONTRIBUTING.md.
Cross-pack tasks
When a task spans multiple domains (e.g. "read an IFC → generate a compliance report → emit it as a docx"), proceed as follows:
- Decompose. Split the task into single-pack subtasks.
- Run sequentially. Enter each pack independently and follow its
AGENTS.md. - Exchange data via files. Pass IFC / JSON / docx through disk between packs; do not assume in-memory sharing.
- Switch environments. Each pack has its own Conda env —
conda activatethe right one when switching.
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.
- 3d ago First seen · 57 lines · 731 tokens per session scan A 8271e67bd63d
skills AGENTS.md is an instructions file published in the GitHub repository sanpingli/skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 731 tokens to every session, about $0.0037 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 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).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
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 oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.