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Computes adherence delta between actual and prescribed workout: pace deviation %, completion %, HR drift, RPE delta. Returns a structured signal dict. Called automatically after log-workout or fetch-strava-activity.
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Based on SOC occupation classification
Applies 1/2/3-strike adjustment rules to plan.json based on a single workout's analysis dict. Mutates plan only when thresholds fire. Called after every workout log.
Renders today's workout card from plan.json and daily_state.json as a Markdown block. Use for every /run-today invocation. No writes — pure read-and-render.
Conversationally captures a completed workout (duration, distance, RPE, HR, splits, notes) and writes it to workouts.json. Triggers analyze-workout and adapt-plan downstream. Use for /run-log.
Inserts a post-race or injury-flag recovery block into plan.json: 4 days rest + 3 easy jog days (post-race), or 3 rest + 1 recovery (red-flag-fatigue), or 7 rest days (injury). Use after /run-race-recap or when adapt-plan returns red_flag_recovery.
Web-search-backed Q&A for novel coaching questions outside the plan's standard logic: heat acclimation, recovery from illness, pacing strategies for unique courses. Use for /run-research and when the Coach cannot answer from training data alone.
Aggregates the past 7 days of completed workouts: total mileage, quality session adherence, fatigue indicators (RPE creep, HR drift). Decides hold/advance/recovery for the next week. Use for /run-week.
| name | analyze-workout |
| description | Computes adherence delta between actual and prescribed workout: pace deviation %, completion %, HR drift, RPE delta. Returns a structured signal dict. Called automatically after log-workout or fetch-strava-activity. |
Given an actual workout and its prescribed counterpart, computes a structured adherence signal dict using the threshold rules from DESIGN.md §4. The verdict (on-target, under, over, aborted) drives the downstream adapt-plan strike rules. This skill is never invoked directly by the user — it is always called programmatically by log-workout or fetch-strava-activity.
log-workout captures user input.fetch-strava-activity normalizes a Strava activity.A dict {completion_pct, pace_delta_pct, rpe_delta, hr_drift_bpm, verdict} stored in workouts.json[].analysis and returned in-memory to the caller for immediate use by adapt-plan.
See body.md for full instructions and scripts/analyze.py for the deterministic delta computation.