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/commands/airbone42/360-data-athlete/muscleoverview)<a href="https://agentmods.dev/commands/airbone42/360-data-athlete/muscleoverview"><img src="https://agentmods.dev/badge/commands/airbone42/360-data-athlete/muscleoverview.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 | $0.00000 | $0.00544 |
| Opus 5 | $0.00000 | $0.00272 |
| Sonnet 5 | $0.00000 | $0.00109 |
| Haiku 4.5 | $0.00000 | $0.00054 |
Grade A, and why
muscleoverview scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/muscleoverview — Muscle fatigue overview
Shows the current fatigue status of all sport-relevant muscles, grouped by body part.
Workflow
Step 1: Run the overview
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/muscle_overview.py
Emits a terminal table with:
- Last load — when the muscle was last loaded
- Load — cumulative load over the last 30 days (S = strength, C = cardio)
- Fatigue — estimated residual fatigue in % (exponential decay)
- Next OK — when the muscle is trainable again (fatigue ≤ 30 %)
Colour code: 🟢 ≤30 % trainable | 🟡 30–65 % moderate | 🔴 >65 % rest
Step 2: Interpretation
Explain to the athlete:
- Which muscle groups are most fatigued (🔴)
- Which are available for the next session (🟢)
- Notable patterns (e.g. one-sided overload, under-training of certain groups)
Keep it short: max 3–5 points, direct and actionable.
Step 3 (optional): Review unmapped exercises
If the athlete wants to address unmapped exercises:
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/muscle_overview.py --review-unmapped
For each unknown exercise: research which muscles are involved and add to the mapping:
# Add the mapping manually to config/exercise_muscle_mapping.json
# Then re-run backfill:
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/log_muscle_load.py --backfill 30 --silent
Step 4 (optional): Backfill
If data is missing (e.g. after a longer pause or a new setup):
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/muscle_overview.py --backfill 30
Notes
- Phase A: The system collects data but does NOT enforce hard blocks in planning. Data is informational.
- Scales: Strength load (S) and cardio load (C) use different units and MUST NOT be summed.
- Recovery times are based on muscle size and the peak RPE of the
last session (lookup table in
config/muscle_db.md). - New exercise not in DB? → add it to
config/exercise_muscle_mapping.json, then run backfill again.
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 · 68 lines · 0 tokens per session scan A 1af28905bfc2
muscleoverview is a command published in the GitHub repository airbone42/360-data-athlete (22 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 544 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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