菜单
Pulls recent run activities from the Strava MCP server, normalizes them to the workouts.json schema using strava_normalize.py helpers, deduplicates by strava_activity_id with preferred_source tiebreaker, and runs the same analyze-workout + adapt-plan pipeline as manual logging. Requires users.integrations.strava.connected = true.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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 | fetch-strava-activity |
| description | Pulls recent run activities from the Strava MCP server, normalizes them to the workouts.json schema using strava_normalize.py helpers, deduplicates by strava_activity_id with preferred_source tiebreaker, and runs the same analyze-workout + adapt-plan pipeline as manual logging. Requires users.integrations.strava.connected = true. |
Connects to the Strava MCP server, retrieves recent run activities, normalizes each to the internal workout schema via scripts/strava_normalize.py, deduplicates against existing workouts.json entries, and runs the same analyze-workout + adapt-plan pipeline as manual logging.
/run-sync.users.json.integrations.strava.connected == true (set by a successful /run-init --connect strava probe).Field mapping is implemented in scripts/strava_normalize.py — call normalize_activity(activity_dict) on each Strava activity. The function returns None for non-run activities, activities shorter than 5 minutes, or activities with zero distance; skip those silently.
Normalized workout records appended to storage/workouts.json (via upsert_workout from strava_normalize.py), analysis and adapt-plan signals applied, users.json.integrations.strava.last_sync_at and last_sync_status updated.
See body.md for the full step-by-step instructions, error handling, and deduplication semantics.