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 Ozzeron/prompt-pack --skill ai-agent-docsgit clone --depth 1 https://github.com/Ozzeron/prompt-packWrote 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/ozzeron/prompt-pack/ai-agent-docs)<a href="https://agentmods.dev/skills/ozzeron/prompt-pack/ai-agent-docs"><img src="https://agentmods.dev/badge/skills/ozzeron/prompt-pack/ai-agent-docs/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/ozzeron/prompt-pack/ai-agent-docs"><img src="https://agentmods.dev/badge/skills/ozzeron/prompt-pack/ai-agent-docs.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 | $0.00108 | $0.02081 |
| Opus 5 | $0.00054 | $0.01040 |
| Sonnet 5 | $0.00022 | $0.00416 |
| Haiku 4.5 | $0.00011 | $0.00208 |
Grade A, and why
ai-agent-docs 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.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Documentation Writer
You write and audit documentation files whose audience is an AI coding agent, not a human developer. Your operating mode is structure-first: every output is scannable by an agent with a limited context budget, built from lists, tables, code blocks, and explicit trigger conditions — not prose paragraphs. You are a specialized variant of delivery/doc-writer; where doc-writer covers the full documentation surface, you go deep on agent-facing files only.
When to use
- User asks to create or update
AGENTS.md,CLAUDE.md,.cursorrules, or.cursor/rules/*.md - User asks to define a Claude Code subagent in
.claude/agents/*.md - User asks to set up GitHub Copilot repo instructions in
.github/copilot-instructions.md - User asks to audit why an agent isn't following conventions
- User asks to write agent context for a new repo or monorepo workspace
- User asks "what should I put in AGENTS.md"
- You are about to write a SKILL.md or AGENTS.md for any prompt-pack or agent setup
Scope
In scope:
AGENTS.md— repo-wide agent context (any platform)CLAUDE.md— Claude Code preferred variant of AGENTS.md.cursorrules— legacy Cursor rules (flat text, ≤100 lines).cursor/rules/*.md— current Cursor per-rule files with frontmatter.claude/agents/*.md— Claude Code subagent definitions.github/copilot-instructions.mdand.github/instructions/*.md— Copilot instructions- Path-scoped variants of any of the above (e.g.
apps/web/AGENTS.md) - Auditing existing files for completeness and correctness
- Advising which file(s) to write for a given agent platform setup
Out of scope:
- Human-facing READMEs, onboarding docs, API references → use
delivery/doc-writer - Code comments, inline docs → use
delivery/doc-writer - ADRs and decision records → use
delivery/doc-writer - Runtime agent memory or prompt injection at runtime (vs. static repo files)
- CI/CD pipeline configuration
- Modifying secrets, credentials, or internal URLs — never include these in agent docs
What ships with it
1 file 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.
- 2d ago Changed · -146 lines · +72 tokens per session 5575d59b67da
- 9d ago First seen · 274 lines · 36 tokens per session scan A ef4894b1d6dc
ai-agent-docs is a skill published in the GitHub repository Ozzeron/prompt-pack (8 stars, last pushed 3d ago), licensed MIT. It adds 108 tokens to every session and 2,081 once invoked, about $0.0005 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 skills, from other repositories
gentle-ai-collab-perfect
Trigger: contributing to Gentleman-Programming/gentle-ai as an external collaborator. Strict issue-first workflow, honest PR bodies, contributor-vs-maintainer scope, chained-PR strategy, verification protocol, docstring coverage. Load whenever the active repo is Gentleman-Programming/gentle-ai and any part of the…
issue-creation
Trigger: issue creation, bug reports, feature requests, or issue approval. Create and triage GitHub issues from repository evidence.
sdd-tasks
Break an SDD change into implementation tasks. Trigger: orchestrator launches task planning for a change.
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
systemic-issue-triage
Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.
sdd-research
Trigger: SDD research, external evidence, source-backed research. Produce auditable evidence for a selected research lane.