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/wellness)<a href="https://agentmods.dev/commands/airbone42/360-data-athlete/wellness"><img src="https://agentmods.dev/badge/commands/airbone42/360-data-athlete/wellness.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 | $0.00325 |
| Opus 5 | $0.00000 | $0.00162 |
| Sonnet 5 | $0.00000 | $0.00065 |
| Haiku 4.5 | $0.00000 | $0.00032 |
Grade A, and why
wellness 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.
What it actually says
/wellness — Quick wellness check
Show current training status without generating a plan.
Arguments
$ARGUMENTS
Optional: date YYYY-MM-DD. Default: today.
python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/fetch_context.py --date {DATE}
Show compactly:
{dateStr}
- HRV: {hrvContext}
- RHR: {rhr} bpm | Sleep: {sleep}/100 ({sleepHours})
- CTL: {ctl} | ATL: {atl} | TSB: {tsb}
- Intensity today: {intensityReadiness}
- Weather: {weatherInfo}
Upcoming events: {eventList}
Zone distribution last 4 weeks: {zoneDistribution}
Cycle hint: {cycleHint}
If dataWarnings is present: show them.
Derive complementary status briefly from activities (when was the last
core/plyo/mobility/ninja/legs session).
Clean up skipped workouts:
If skippedWorkouts is present → inform athlete: "[name] on [date] was
planned but not executed — deleting."
→ python3 "${CLAUDE_PLUGIN_ROOT:-.}"/scripts/delete_workouts.py --event-ids {comma-separated IDs}
HRV readiness review:
If hrvReviewPending is present → ask the athlete (see CLAUDE.md
"HRV readiness review"). After the answer, persist as NOTE on
intervals.icu.
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 · 46 lines · 0 tokens per session scan A b86705dbb1e2
wellness 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 325 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.