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 agentmods add commands/airbone42/360-data-athlete/auditgit clone --depth 1 https://github.com/airbone42/360-data-athleteWrote 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/commands/airbone42/360-data-athlete/audit)<a href="https://agentmods.dev/commands/airbone42/360-data-athlete/audit"><img src="https://agentmods.dev/badge/commands/airbone42/360-data-athlete/audit.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 | $0.00000 | $0.00890 |
| Opus 5 | $0.00000 | $0.00445 |
| Sonnet 5 | $0.00000 | $0.00178 |
| Haiku 4.5 | $0.00000 | $0.00089 |
Grade A, and why
audit 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 5d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/audit — Consistency audit of the knowledge base
Scans the coach system for contradictions between config/ files,
sub-agents, prompts, the exercise mapping, and external sources
(intervals.icu NOTEs). Findings are refined by the
config-auditor (fresh context) and written as a markdown report to
data/audits/. Fixes go through the config-fixer (fresh context)
after athlete approval.
Arguments
$ARGUMENTS
Optional: --offline (skip intervals.icu roundtrip — faster,
but NOTE drift and shoes are not checked).
Workflow
Step 1: Run the mechanical scanner
Default is online — the most important drift sources (NOTE-vs-static, intervals.icu gear/shoes) need API access.
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/audit_consistency.py > /tmp/audit_raw.json
For --offline in arguments:
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/audit_consistency.py --offline > /tmp/audit_raw.json
Check online_error in the JSON. If present → inform the athlete and
continue with the available findings.
Step 2: Launch config-auditor as subagent
Launch the config-auditor agent as a subagent (Task tool) to
guarantee fresh context — not inside the active coach pane.
In the prompt, pass:
- Path
/tmp/audit_raw.json(or the JSON content directly, if small) - Current date for the output path
- Mode (online/offline)
The auditor:
- Reads the scanner JSON
- Refines each finding semantically (drop, severity adjustment, context enrichment)
- Adds its own semantic checks (phase-vs-restriction, LTHR drift, mapping plausibility, equipment match, exercise-log drift, recovery week activities)
- Writes a report to
data/audits/YYYY-MM-DD-HHMM-audit.md - Returns a compact summary (HIGH/MEDIUM/LOW counts + top HIGH findings)
Step 3: Present summary
Show the athlete the auditor summary 1:1, plus the report path. Ask:
"Which findings should I fix? (e.g. 'F001, F003' or 'all HIGH' or 'nothing')"
Step 4: Fixes via config-fixer (fresh context)
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.
- 5d ago First seen · 109 lines · 0 tokens per session scan A 63ffe817900f
audit is a command published in the GitHub repository airbone42/360-data-athlete (22 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 890 tokens. 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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