ai-modules AGENTS.md

Repository instructions for ai-modules, a collection of AI components such as skills, agents, commands, and hooks packaged as plugins.

In plain words
What is it for?
Use them when creating, editing, packaging, or versioning plugins and their published instruction files.
Why use it?
They clarify the repository’s layout, terminology, and publishing conventions so changes affect the right component.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/theafh/ai-modules/agents-md
Clone the repo
git clone --depth 1 https://github.com/theafh/ai-modules

Made for: Codex, OpenCode.

Per session 2,579 This file is loaded in full into every session.
When invoked 2,579 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 928ffa9de511, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

AGENTS.md · 91 lines

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-rich Use when trigger 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_implement beside task_implement). A spawned agent leads with auto_ and ends with the family token (auto_<role>_<family>, e.g. auto_implementer_task or auto_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 its SKILL.md by the skill-relative path scripts/<name>. The plugin is the unit of distribution, so a script placed at the repo root or wired into the root Makefile is invisible to every deployment path except an in-place checkout. Add a repo-root scripts/ 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 as task_fix carries a surface-and-propose advisory that points back at the base rule.

Read the full file on GitHub · 91 lines

Changes

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.

  1. 2d ago First seen · 91 lines · 2,579 tokens per session scan A 928ffa9de511

Subscribe to this mod's changes

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.

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