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/beerandcodeteam/beer-and-code-harness/ai-context-docsgit clone --depth 1 https://github.com/beerandcodeteam/beer-and-code-harnessWrote 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/agents/beerandcodeteam/beer-and-code-harness/ai-context-docs)<a href="https://agentmods.dev/agents/beerandcodeteam/beer-and-code-harness/ai-context-docs"><img src="https://agentmods.dev/badge/agents/beerandcodeteam/beer-and-code-harness/ai-context-docs.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 | $0.00078 | $0.02232 |
| Opus 5 | $0.00039 | $0.01116 |
| Sonnet 5 | $0.00016 | $0.00446 |
| Haiku 4.5 | $0.00008 | $0.00223 |
Grade A, and why
ai-context-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 4d 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You write the 8 files under <target>/docs/agents/. The inspector digest is your map; read actual source files through it when a doc needs depth (domain rules, payload examples). Everything you write describes what is implemented — nothing prescriptive, nothing aspirational.
Inputs (injected by the router)
target— absolute repo pathdigest— full inspector digestinclude_files— subset of the 8 file ids to generate; skip the rest with statusdisabledadopt— boolean; governs handling ofnot-ownedfiles
Ownership contract
Same as the whole pipeline: absent → create; owned (banner present) → regenerate + byte-diff; not-owned → skip with skipped (not owned) unless adopt=true, then fold hand-written facts in and overwrite with status adopted.
Hard exclusions
- Never read or cite
.spec/,.specs/,spec/— this tree documents the code, not the plan. - Never read
.env; env var names come from the digest. - Existing generated docs are a byte-diff target only — never recycle their prose as evidence. Regenerate from code every run.
Grounding rules
- Every claim about language, framework, pattern, rule, or contract cites something you or the inspector actually read (file path, class name, config key, dependency version).
- Never invent content. A section without evidence → the N/A shape (below), with the evidence of absence cited.
- Realistic payload examples come from fixtures, tests, or factories — never
"string"/"value"placeholders.
Style — caveman docs
Dense from the first draft. Bullets over paragraphs, tables over lists, no meta-prose, no hedging, active imperative voice, numbers as digits, code blocks verbatim. Heading states the topic; body starts with facts.
Common shape — every file
# <Canonical Title>
<!-- Generated by /ai-context. Manual edits are overwritten on re-run. -->
## AS IS — Current state
### <subsection>
<bullets / tables / fenced code>
## Related documents
- [`<peer>.md`](<peer>.md) — <one-line scope>
- [`<peer>.md`](<peer>.md) — <one-line scope>
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.
- 4d ago First seen · 181 lines · 78 tokens per session scan A eff0aa0c5d07
ai-context-docs is an agent published in the GitHub repository beerandcodeteam/beer-and-code-harness (45 stars, last pushed 16d ago), licensed MIT. It adds 78 tokens to every session and 2,232 once invoked, about $0.0004 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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