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 gabrielmoreira/agent-skills-mirror --skill agent-context-auditgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/agent-context-audit)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/agent-context-audit"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/agent-context-audit/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/gabrielmoreira/agent-skills-mirror/agent-context-audit"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/agent-context-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00139 | $0.02084 |
| Opus 5 | $0.00069 | $0.01042 |
| Sonnet 5 | $0.00028 | $0.00417 |
| Haiku 4.5 | $0.00014 | $0.00208 |
Grade A, and why
agent-context-audit 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 9d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-context-audit — unhobble this repo's agent context
Goal: find where this repo's context (CLAUDE.md, docs, skills, tool designs) hobbles a Claude 5-generation model — overconstrains it, contradicts itself, repeats itself, or hides context the model actually needs — and leave behind a findings report plus approved fixes.
Background: Anthropic removed over 80% of Claude Code's system prompt for Claude 5 models with no measurable loss on coding evals. Older context was written for models that needed rules; newer models need judgment, good interfaces, and the facts they can't infer. This skill audits against that shift, plus the "finding your unknowns" framework (the gap between the map — your prompts/docs — and the territory — the actual codebase).
You are auditing first, fixing second. Do not edit anything until Step 4.
The six shifts (the audit rubric)
Every finding maps to one of these. Cite the shift number in the report.
- Rules → Judgment. Hard rules ("NEVER…", "ALWAYS…", "do not add comments", "one-line docstrings max") that encode a preference, not a real constraint, should become judgment framing ("write code that reads like the surrounding code") — or be deleted if the model would infer it anyway. Keep hard rules only where violation is genuinely costly (security, prod data, irreversible actions, legal/billing).
- Examples → Interface design. Long tool-usage examples and few-shot
transcripts constrain exploration. Prefer expressive interfaces: good
parameter names, enums that hint at valid states, tight descriptions.
In tool/MCP definitions, an enum of
pending | in_progress | completedteaches more than three worked examples. - Upfront context → Progressive disclosure. Anything long that's only sometimes needed (review checklists, deploy runbooks, style deep-dives) should move out of CLAUDE.md into a skill or linked file loaded on demand. CLAUDE.md is loaded every session — it should carry only what every session needs.
- Repetition → Concise, single-home instructions. The same instruction appearing in CLAUDE.md and a skill and a tool description is a bug: copies drift and eventually conflict. Each instruction gets exactly one home — tool-usage guidance lives in the tool description, repo gotchas in CLAUDE.md, team opinions in skills.
- Manual memory → Automatic memory. Sections telling the agent to hand-maintain notes/changelogs in CLAUDE.md, or accumulated session-specific trivia, are obsolete where auto-memory exists. Flag CLAUDE.md content that is really memory (per-user, per-incident, time-bound) rather than repo truth.
- Simple specs → Rich references. Where docs describe behavior in loose
prose, prefer pointing at the real thing:
@-referenced source files, a test suite, an HTML mockup, a rubric a verifier can score against. Code-based specs beat prose paraphrases of code.
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.
- 9d ago First seen · 167 lines · 139 tokens per session scan A cee32f8ae591
agent-context-audit is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 139 tokens to every session and 2,084 once invoked, about $0.0007 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 2026-09-03.
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