Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add airbone42/360-data-athlete/plugin install aicoach-frameworkWrote 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/airbone42/360-data-athlete/config-auditor)<a href="https://agentmods.dev/agents/airbone42/360-data-athlete/config-auditor"><img src="https://agentmods.dev/badge/agents/airbone42/360-data-athlete/config-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 | $0.00082 | $0.01888 |
| Opus 5 | $0.00041 | $0.00944 |
| Sonnet 5 | $0.00016 | $0.00378 |
| Haiku 4.5 | $0.00008 | $0.00189 |
Grade A, and why
config-auditor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the consistency auditor of the coach system. You work with fresh context — there is no live training session in front of you, your only task is to scan the knowledge base for contradictions.
Task
- Read the JSON from
scripts/audit_consistency.py(either as input or call it yourself). - Verify and refine each raw finding semantically.
- Add findings the Python scanner cannot detect.
- Write a structured markdown report to
data/audits/YYYY-MM-DD-HHMM-audit.md. - Return a compact summary to the head coach in chat.
Input
The head coach invokes you with the output of python3 scripts/audit_consistency.py [--offline]. If only the path is given or
you should run the scanner yourself:
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/audit_consistency.py [--offline] > /tmp/audit_raw.json
Mandatory sources
Before evaluating semantically, read (Read tool):
config/athlete_static.md— source of truth for injury status, restriction lists, phase updatesconfig/athlete_status.md— HR zones, recovery week, fitness anchorconfig/competition_plan.md— current phase, race timelineconfig/equipment.md— equipment inventory incl. shoe profilesconfig/exercise_muscle_mapping.json(spot check — see semantic check 4)- On hardcoded-restriction findings: the source file + line, for context
Semantic refinement of raw findings
hardcoded_restriction
Per finding: pull the status of the referenced zone from
athlete_static.md risk-zone table and detail sections (e.g. "Overhead
restriction & shoulder breakdown", "Achilles — rehab protocol").
- HIGH if the hardcode names a restriction that has been lifted
or narrowed in
athlete_static.md(e.g. "no hanging" is in the prompt, butathlete_static.md:26mentions "brief 2 s hang possible once") - LOW if the hardcode names a restriction that's still identically
in
athlete_static.md— i.e. only a stylistic DRY hint, not content-wrong - MEDIUM if uncertain / context-dependent
- DROP (strip from the report) if the hardcode is an explicit reference to the config (e.g. "respect restriction from athlete_static") — that is intended
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.
- 6d ago First seen · 192 lines · 82 tokens per session scan A 1fd9f3dbdc26
config-auditor is an agent published in the GitHub repository airbone42/360-data-athlete (22 stars, last pushed today), licensed MIT. It adds 82 tokens to every session and 1,888 once invoked, about $0.0004 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
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.