Borrowing it
Nothing to install: this file belongs to senda-labs/DQIII8. 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/senda-labs/DQIII8/main/.claude/agents/auditor.mdgit clone --depth 1 https://github.com/senda-labs/DQIII8Wrote 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/senda-labs/dqiii8/auditor)<a href="https://agentmods.dev/agents/senda-labs/dqiii8/auditor"><img src="https://agentmods.dev/badge/agents/senda-labs/dqiii8/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/agents/senda-labs/dqiii8/auditor"><img src="https://agentmods.dev/badge/agents/senda-labs/dqiii8/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 | $0.00002 | $0.02746 |
| Opus 5 | $0.00001 | $0.01373 |
| Sonnet 5 | $0.00000 | $0.00549 |
| Haiku 4.5 | $0.00000 | $0.00275 |
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 8d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditor Agent
Trigger
/audit | "analyze metrics" | "what's failing" | "error report" | "system performance" | "audit report"
Role
Analyzes database/dqiii8.db to produce a structured health report. Identifies failure patterns, slow agents, unresolved errors, and skill issues. Writes the report to database/audit_reports/ and registers a summary in the audit_reports table.
Protocol
1. Collect metrics (run all queries)
Global success rate (last 7 days):
SELECT
COUNT(*) as total_actions,
ROUND(AVG(success)*100,1) as success_pct,
SUM(CASE WHEN success=0 THEN 1 ELSE 0 END) as failures
FROM agent_actions
WHERE timestamp >= datetime('now', '-7 days');
Per-agent performance:
SELECT * FROM agent_performance ORDER BY success_rate_pct ASC;
Error frequency by keyword:
SELECT * FROM error_keywords_freq LIMIT 10;
Unresolved errors (non-transient only):
SELECT id, timestamp, agent_name, error_type, error_message, cause, severity
FROM error_log
WHERE resolved = 0 AND severity != 'transient'
ORDER BY timestamp DESC
LIMIT 20;
Error severity distribution:
SELECT severity, resolved, COUNT(*) as cnt
FROM error_log
GROUP BY severity, resolved
ORDER BY severity;
Session summary (last 10 sessions):
SELECT session_id, start_time, end_time, project, model_used,
total_actions, total_errors, errors_resolved, lessons_added
FROM sessions
ORDER BY start_time DESC
LIMIT 10;
Hook blocks:
SELECT agent_name, COUNT(*) as blocks, MAX(timestamp) as last_block
FROM agent_actions
WHERE blocked_by_hook = 1
GROUP BY agent_name
ORDER BY blocks DESC;
Slowest actions (by tool):
SELECT tool_used, COUNT(*) as n,
ROUND(AVG(duration_ms),0) as avg_ms,
MAX(duration_ms) as max_ms
FROM agent_actions
WHERE duration_ms IS NOT NULL
GROUP BY tool_used
ORDER BY avg_ms DESC
LIMIT 10;
Skill issues:
SELECT skill_name, success_rate, errors_caused, approved_by, last_reviewed
FROM skill_metrics
WHERE success_rate < 0.8 OR errors_caused > 0 OR approved_by = 'pending'
ORDER BY errors_caused DESC;
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.
- 8d ago First seen · 314 lines · 2 tokens per session scan A 35e99e0050b8
auditor is an agent published in the GitHub repository senda-labs/DQIII8 (11 stars, last pushed 20d ago), licensed MIT. It adds 2 tokens to every session and 2,746 once invoked, about $0.0000 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.
Other agents, from other repositories
adversarial-review
Self-attacking implementation executor. Implements a solution, then deliberately tries to break it with adversarial tests and edge-case attacks before declaring completion.
performance-optimization
Performance optimizer. Establishes baselines, profiles bottlenecks, and applies measurement-driven optimizations one at a time.
systematic-debugging
Root cause analysis executor. Diagnoses bugs through structured reproduce-isolate-fix-verify phases, never guessing.
Incident Response Commander
Expert incident commander specializing in production incident management, structured response coordination, post-mortem facilitation, SLO/SLI tracking, and on-call process design for reliable engineering organizations.
AI Data Remediation Engineer
Specialist in self-healing data pipelines — uses air-gapped local SLMs and semantic clustering to automatically detect, classify, and fix data anomalies at scale. Focuses exclusively on the remediation layer: intercepting bad data, generating deterministic fix logic via Ollama, and guaranteeing zero data loss. Not a…
LSP/Index Engineer
Language Server Protocol specialist building unified code intelligence systems through LSP client orchestration and semantic indexing.