.claude/skills/readiness-today/SKILL.md
Tell the user whether they should train hard, train easy, or rest today, based on their current TSB, ACWR and recent load. Use when the user asks "can I train hard today", "am I ready", "should I rest".
npx skillsauth add AlvaroLaraFF/strava-coach readiness-todayInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Single-number readiness recommendation derived from current form (TSB), acute:chronic ratio and the load of the last 48 hours.
Token must exist (run strava-setup wizard otherwise).
python3 .claude/skills/athlete-snapshot/scripts/read_snapshot.py
Extract ftp_w, hr_max_bpm, hr_rest_bpm from the snapshot and pass as
--ftp, --hr-max, --hr-rest. If no snapshot, use defaults.
python3 .claude/skills/readiness-today/scripts/readiness.py
Optional: --ftp, --hr-max, --hr-rest to override defaults.
Show the recommendation prominently (GO HARD / MODERATE / EASY / REST), plus the three numbers that drove it. End with one sentence of justification — DON'T hedge: pick one verdict.
python3 .claude/skills/athlete-snapshot/scripts/update_snapshot.py --source readiness-today --acwr <ACWR> --tsb <TSB>
Replace placeholders with values from script output. Show alerts if any.
| Error contains | Action |
|---|---|
| No token / StravaAuthError | Invoke strava-setup, retry |
| No activities / Sync first | Invoke strava-sync --level summary, retry |
| anything else | Surface |
Chain at most ONCE.
Save a qualitative observation to memory — opinions, patterns, coaching notes. Never store raw numeric values (those are recomputable from the DB). Only write if the observation is NEW or CHANGED vs existing memory. See CLAUDE.md → Memory protocol.
data-ai
Show a weekly training log: activities grouped by ISO week and sport, with totals for distance, time and elevation. Use when the user asks "what did I do this week", "weekly summary", "training log".
tools
Cross-reference recent runs/rides with historical weather (temperature, humidity, wind) from Open-Meteo to find correlations with performance. Use when the user asks "do I run worse in the heat", "weather impact", "temperature vs pace".
testing
Show how the user's training time and volume distribute across run, ride and swim, and flag underweighted disciplines. Use when the user asks "am I balanced", "discipline balance", "which sport am I neglecting".
data-ai
Compute a combined CTL/ATL/TSB across run, ride and swim for triathletes. Use when the user asks "my combined load", "triathlon training load", "total TSS across sports".