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/theafh/ai-modules/agents-mdgit clone --depth 1 https://github.com/theafh/ai-modulesWhat 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 | $0.02579 | $0.02579 |
| Opus 5 | $0.01290 | $0.01290 |
| Sonnet 5 | $0.00516 | $0.00516 |
| Haiku 4.5 | $0.00258 | $0.00258 |
Grade A, and why
ai-modules 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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ai-modules CLAUDE.md — 97% identical, 21 lines differ
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.
AGENTS.md
ai-modules is a meta-repository. It defines AI components — skills, agents, commands, hooks — and packages them as plugins. Here command means the legacy standalone command artefact the deployer can still install (for example a Claude slash-command file under commands/), not a shell command and not a skill that is merely slash-invocable. Treat every SKILL.md, plugin.json, and marketplace.json as a published artefact.
What this repo is not
The shipped skills are the product, not the workflow. When a user asks you to apply one while editing this repo, confirm whether they mean to invoke it or edit its definition.
Layout
.agents/plugins/marketplace.json # Codex marketplace registration
.claude-plugin/marketplace.json # Claude marketplace registration
plugins/<plugin>/
.codex-plugin/plugin.json # Codex plugin metadata (uses "skills": "./skills/")
.claude-plugin/plugin.json # Claude plugin metadata
README.md # plugin overview + skill list
skills/<skill>/SKILL.md # skill definition with YAML frontmatter
styles/ # tracked output styles (repo-root; not a plugin component)
deployment/ # deploy script + per-tool config
tests/ # local-only regression harnesses (gitignored)
Makefile # task entry point
.markdownlint.jsonc # markdown lint config (MD033 off — pseudo-XML is intentional)
Authoring conventions
- Use pseudo-XML inside skill prompts (
<role>,<objective>,<policy>,<output_contract>). Reference:plugins/ai_dev/skills/ai_instruction_formatting/SKILL.md. - Use positive, action-oriented language in skill prose and instructions. Reference:
plugins/ai_dev/skills/ai_instruction_writing/SKILL.md. - Write skill descriptions for both audiences. The
description:frontmatter is read before the body is loaded by an LLM router and by users browsing skills. Serve both needs deliberately: give the user a precise compact summary of what the skill is about and how it differs from neighbors, then give the router keyword-richUse whentrigger contexts, prompt phrases, artefacts, file types, and invocation boundaries. Keep implementation workflow details in the body. - Keep the toolchain to Make + shell + Markdown, with jq, git, and Python 3 as accepted standing dependencies. Add further languages, package managers, or build steps only when the user explicitly asks for them.
- Match snake_case naming for skill and plugin directories.
- Name skills and agents by invocation mode and collision risk. A skill that is the only entry point for its capability keeps the ordinary family-first name, even when it delegates to agents (
wiki_fix). Use<family>_auto_<rest>for an agent-delegating automation skill when it needs to sit beside a classical/manual skill with the same capability or subset (task_auto_implementbesidetask_implement). A spawned agent leads withauto_and ends with the family token (auto_<role>_<family>, e.g.auto_implementer_taskorauto_shaper_wiki) and is not intended to be invoked by the user. - Write deployment-agnostic cross-references. Reference sibling artefacts by name (
auto_shaper_wiki,format_markdown) rather than by plugin name, marketplace, or installed path. - Bundle a skill's helper scripts inside the skill. A script that supports a skill lives at
plugins/<plugin>/skills/<skill>/scripts/<name>and is referenced from itsSKILL.mdby the skill-relative pathscripts/<name>. The plugin is the unit of distribution, so a script placed at the repo root or wired into the rootMakefileis invisible to every deployment path except an in-place checkout. Add a repo-rootscripts/directory only for repo-wide tooling that belongs to no single skill, and only when explicitly asked. - Author a skill-family rule once in the family's base skill. When a rule should govern a whole skill family (for example the
task_*family), write it once in the base/hub skill so the front-end siblings inherit it through their<authority>reference instead of each carrying a copy. Pair enforcement with the canonical rule rather than restating it: a maintenance sibling such astask_fixcarries a surface-and-propose advisory that points back at the base rule.
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.
- 2d ago First seen · 91 lines · 2,579 tokens per session scan A 928ffa9de511
ai-modules AGENTS.md is an instructions file published in the GitHub repository theafh/ai-modules (38 stars, last pushed 2d ago), licensed MIT. It adds 2,579 tokens to every session, about $0.0129 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 instructions, from other repositories
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.
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).
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.