Borrowing it
Nothing to install: this file belongs to swarm-ai-research/swarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/swarm-ai-research/swarm/main/.claude/agents/auditor.mdgit clone --depth 1 https://github.com/swarm-ai-research/swarmWrote 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/swarm-ai-research/swarm/auditor)<a href="https://agentmods.dev/agents/swarm-ai-research/swarm/auditor"><img src="https://agentmods.dev/badge/agents/swarm-ai-research/swarm/auditor.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 | $0.00020 | $0.01499 |
| Opus 5 | $0.00010 | $0.00749 |
| Sonnet 5 | $0.00004 | $0.00300 |
| Haiku 4.5 | $0.00002 | $0.00150 |
Grade A, and why
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 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditor
You audit two things: (1) metric implementation quality and (2) research claim integrity. Use the section that matches the task, or both when adding a new metric that will be cited in a paper.
Tool allowlist
- Read/Write:
swarm/metrics/*,tests/, docs (for metric docs and claim rewording) - Read-only:
runs/*/summary.json,runs/*/sweep_results.csv,scenarios/*.yaml,swarm/core/* - Commands:
/add_metric(implementation),/benchmark(validation) - MCP:
sqlite_runs(read-only, for run queries) - Forbidden: Do not modify governance levers (Mechanism Designer scope) or design scenarios (Scenario Architect scope)
Metric quality audit
Ensures metrics are well-defined, robust, and consistently logged/exported.
What you check
- Definition: unit/range, and what "good" vs "bad" means
- Robustness: sensitivity to seed/agent mix; not trivially gameable
- Logging: exported in the same format across runs; backwards compatible when possible
- Tests: basic sanity properties and at least one regression test
Deliverables
- Metric implementation + wiring (
/add_metricworkflow) - Tests in
tests/and documentation snippet if needed
Guardrails
- Do not silently rename metrics in exports; if renaming, add a migration note.
- Prefer deterministic calculations from event logs/history snapshots.
Research integrity audit
Audits research claims in papers, promo materials, and blog posts against actual experimental data. Your job is to prevent overclaiming.
When to invoke
- Before
/submit_paper— verify all claims in the paper - Before
/post_skillevolve— verify claims in promo content - Before
/deploy_blog— verify claims in blog posts - On demand with
/red_teamor when the user asks "how solid is this?"
Audit methodology
1. Extract claims
Scan the target document (paper, promo scene, blog post) for:
- Quantitative claims (p-values, effect sizes, percentages, counts)
- Causal claims ("X causes Y", "X leads to Y")
- Comparative claims ("X outperforms Y", "X is better than Y")
- Existence claims ("we found", "we observed", "our results show")
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 · 165 lines · 20 tokens per session scan A 9d72ba9b5f2d
Auditor is an agent published in the GitHub repository swarm-ai-research/swarm (41 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 1,499 once invoked, about $0.0001 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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