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.
git clone --depth 1 https://github.com/knitli/toolshedWrote 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/rules/knitli/toolshed/ctx-audit)<a href="https://agentmods.dev/rules/knitli/toolshed/ctx-audit"><img src="https://agentmods.dev/badge/rules/knitli/toolshed/ctx-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/rules/knitli/toolshed/ctx-audit"><img src="https://agentmods.dev/badge/rules/knitli/toolshed/ctx-audit.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 | $0.00000 | $0.00336 |
| Opus 5.5 | $0.00000 | $0.00134 |
| Sonnet 5.5 | $0.00000 | $0.00067 |
| Haiku 4.5 | $0.00000 | $0.00034 |
Grade A, and why
ctx-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 1mo 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
Context Hygiene Rule
When you edit or read any AI context file matched by this rule's globs, apply these checks:
Before trusting claims in a context file
Verify key factual claims against the actual codebase:
- Version numbers: Check
Cargo.toml,package.json,pyproject.toml - File paths: Confirm the referenced files/directories exist
- Dependency names: Confirm they appear in the manifest
- Symbol names: Search the codebase for referenced types, functions, modules
- Counts: Verify "N crates", "N languages", etc. by actually counting
After editing code that context files reference
If you renamed files, changed versions, restructured modules, or modified the architecture, check these files for stale references:
CLAUDE.mdandAGENTS.md(root-level agent memory).serena/memories/*(Serena tool memories).specify/memory/*(spec-kit memory)GEMINI.md(if not a symlink)
Update any claims that no longer match reality.
Authoritative source order
When files contradict each other: actual code > governance docs > CLAUDE.md/AGENTS.md > tool-specific memories > planning docs.
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.
- 1mo ago First seen · 33 lines · 0 tokens per session scan A 11ef56925097
ctx-audit is a cursor rule published in the GitHub repository knitli/toolshed (1 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 336 tokens. 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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