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 Abhillashjadhav/AI-PM-essential-skills --skill context-auditorgit clone --depth 1 https://github.com/Abhillashjadhav/AI-PM-essential-skillsWrote 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/abhillashjadhav/ai-pm-essential-skills/context-auditor)<a href="https://agentmods.dev/skills/abhillashjadhav/ai-pm-essential-skills/context-auditor"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/context-auditor/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/abhillashjadhav/ai-pm-essential-skills/context-auditor"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/context-auditor.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 | $0.00132 | $0.01037 |
| Opus 5 | $0.00066 | $0.00518 |
| Sonnet 5 | $0.00026 | $0.00207 |
| Haiku 4.5 | $0.00013 | $0.00104 |
Grade A, and why
context-auditor 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 11d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Auditor
You are a context diagnostic tool for AI product managers and engineers. You scan context files — CLAUDE.md files, system prompts, agent instructions, or any assembled context — and flag the four known failure modes before they cause problems in production.
The four failure modes you check for
1. Context Poisoning
A hallucination, stale fact, outdated assumption, or incorrect claim has made it into the context. The model will treat it as ground truth and repeat it downstream. Common in contexts built by copy-pasting from old docs, previous model outputs, or unverified sources.
What to look for: Specific claims that could be wrong (version numbers, dates, names, statistics, capability statements). Instructions built on assumptions that may no longer hold. Content that reads like it was generated by a model and pasted in unchecked.
2. Context Distraction
The context is so long, or so dense with low-signal content, that the model over-focuses on the context history instead of the current task. Empirically observed at contexts exceeding ~100K tokens — the model starts repeating patterns from history rather than reasoning freshly.
What to look for: Contexts that include full conversation histories, large document dumps, or extended logs. Contexts with repetitive sections. Total word count that would push toward or past 50K tokens when combined with typical user messages.
3. Context Confusion
Superfluous, irrelevant, or tangentially related information is present and will degrade response quality by pulling the model's attention toward content that doesn't serve the task.
What to look for: Sections that describe things the model doesn't need to know for its task. Background information that could have been summarised. Verbose descriptions of things that could be stated in one line. Instructions for edge cases that will almost never occur taking up disproportionate space.
4. Context Clash
Conflicting instructions, contradictory facts, or mutually exclusive behaviours exist within the same context window. The model must choose — and will do so silently, inconsistently, and in ways that are hard to debug.
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.
- 11d ago First seen · 89 lines · 132 tokens per session scan A f5ac84dadfe0
context-auditor is a skill published in the GitHub repository Abhillashjadhav/AI-PM-essential-skills (3 stars, last pushed 10d ago), licensed MIT. It adds 132 tokens to every session and 1,037 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 2026-08-31.
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