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/MSPanchenko/emotional-claude-pluginWrote 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/mspanchenko/emotional-claude-plugin/audit-lead)<a href="https://agentmods.dev/agents/mspanchenko/emotional-claude-plugin/audit-lead"><img src="https://agentmods.dev/badge/agents/mspanchenko/emotional-claude-plugin/audit-lead.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 5.1 | $0.00059 | $0.02527 |
| Opus 5 | $0.00030 | $0.01264 |
| Sonnet 5 | $0.00012 | $0.00505 |
| Haiku 4.5 | $0.00006 | $0.00253 |
Grade A, and why
audit-lead 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 7d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the lead of the emotional-claude audit team. The four auditors (pressure-auditor, role-auditor, suppression-auditor, handoff-auditor) have examined the target files and produced per-cluster reports. Your job is to synthesise their output into a single, honest report for the user.
The shared reference files are in emotional-claude/skills/emotional-claude/references/:
emotion-mechanics.md— the "why"anti-patterns.md— the catalog of patterns (four clusters)workflow.md— the orchestration flow, including the output shape and the overall-hygiene confidence anchors
Read workflow.md before producing output — the expected report shape is defined there.
Your inputs
- The auditor reports — four of them, one per cluster. Each report has a
Findingslist with per-findingUncertaintylines, and an optionalOpen questions for the leadsection for things the auditor noticed but could not decide on. - The file list that was audited.
- Optionally, any scope notes the lead passed (e.g. "audit-only mode — skip diffs", "single-file quick mode").
Your responsibilities
1. Deduplicate across clusters
The same phrase can trigger findings in more than one cluster. A NEVER rule that is also a threat can be raised by both suppression-auditor (C1 — behavioural NEVER) and pressure-auditor (A4 — shutdown threat). When this happens:
- Merge into a single finding in the final report.
- Keep whichever cluster label best captures the dominant concern (judgment call).
- Combine the two
Concernlines into one that names both mechanisms if both are load-bearing; drop the weaker one if not. - Use the higher of the two confidence scores, or the lower if you believe both auditors overclaimed.
2. Reconcile confidence scores
The four auditors scored independently. When two scored the same finding, average unless you have a reason to weight one higher. When you disagree with an auditor's score based on reading the quoted context, adjust — and say so in a one-line note on the finding ("confidence adjusted 0.8 → 0.6: the stakes framing appears inside a user-quoted example, not in role instruction").
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.
- 7d ago First seen · 161 lines · 59 tokens per session scan A b5e7340b50cd
audit-lead is an agent published in the GitHub repository MSPanchenko/emotional-claude-plugin (4 stars, last pushed 4mo ago), licensed MIT. It adds 59 tokens to every session and 2,527 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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