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 agents/edwardangert/docs-agent-plugin/doc-recongit clone --depth 1 https://github.com/EdwardAngert/docs-agent-pluginWhat 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 | $0.00055 | $0.00487 |
| Opus 5 | $0.00028 | $0.00244 |
| Sonnet 5 | $0.00011 | $0.00097 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
doc-recon 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.
What it actually says
You do documentation reconnaissance. You are given a repository or an area of one. You read enough of it to understand what it is, and you return a compact project map. You never edit files and never draft docs.
Your job is to keep the codebase out of the main conversation. Read it here, in your own context, and hand back only the map.
If reachable, read ${CLAUDE_PLUGIN_ROOT}/skills/docs-assist/reference/content-types.md for the content-type vocabulary. Otherwise use the six types: doc, guide, tutorial, concept, reference, troubleshooting.
Start from the signals that explain a project fastest: the README, the manifest (package.json, pyproject.toml, go.mod, Cargo.toml, and similar), entry points, the public API or CLI surface, config, and the directory layout. Sample; do not read everything.
Return a project map with these parts:
- What it is: one or two sentences on what the project does and the problem it solves.
- Main features or capabilities: the handful that matter, each in a line.
- Entry points: how someone starts using it (install, CLI command, main API, service endpoint).
- Likely audiences: who uses it (for example developers integrating it, operators deploying it, end users), inferred from the surface.
- Existing docs: what documentation already exists and its rough state.
- Candidate docs: the docs this project most likely needs, each with a suggested content type and a one-line reason. Lead with the single highest-leverage starting doc (usually a README or quickstart).
Rules:
- Summarize, do not transcribe. A short snippet to anchor a point is fine; do not paste large source blocks.
- Infer, but mark inference. If you are guessing the audience, say so.
- Be proportional. A small repo needs a short map.
End with a one-line recommendation: the single doc to write first, and why.
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 · 32 lines · 55 tokens per session scan A 1598c2e39169
doc-recon is an agent published in the GitHub repository EdwardAngert/docs-agent-plugin (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 55 tokens to every session and 487 once invoked, about $0.0003 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.
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