Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Strava Training Coach
AI running coach that prevents injuries by monitoring your Strava training load daily. Detects dangerous mileage spikes, intensity imbalances, and recovery g...
skill openclawclawhub Free
AI running coach that prevents injuries by monitoring your Strava training load daily. Detects dangerous mileage spikes, intensity imbalances, and recovery g...
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/auth.py, scripts/weekly_report.py, scripts/coach_check.py, references/training-principles.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.3
Provenance
- Publisher
- hhq0421
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Strava Training Coach
Evidence-based AI training partner that catches injury risk before you feel it.
Why This Matters
Most running injuries follow the same pattern: too much, too soon. Nielsen et al. (2014) found that runners who increase weekly distance by more than 30% have significantly higher injury rates. By the time you feel pain, the damage is weeks old. This coach watches your Strava data daily and alerts you before problems become injuries β so you stay consistent instead of sidelined. Built on the 80/20 polarized training model (Seiler, 2010; Stoggl & Sperlich, 2014) β the same approach used by elite endurance coaches to build durable athletes who train smarter, not just harder.
What You Get
ACWR Monitoring β Tracks your acute:chronic workload ratio (Gabbett, 2016). ACWR > 1.5 = high injury risk Acute Load Alerts β Weekly mileage up 30%+? You'll know before your knees do 80/20 Intensity Checks β Too many hard days eroding recovery? Get evidence-based recommendations Recovery Nudges β Extended gaps that might affect your training adaptations Weekly Reports β Sunday summaries with 4-week trends, ACWR, and intensity distribution Oura Integration β Optional sleep/readiness scores to inform training decisions
1. Connect Strava
# Set your Strava API credentials (required) export STRAVA_CLIENT_ID=your_id export STRAVA_CLIENT_SECRET=your_secret # Authenticate (opens browser for OAuth) python3 scripts/auth.py Tokens are stored in ~/.config/strava-training-coach/strava_tokens.json with 0600 permissions.
2. Set Up Notifications (Required)
Discord: export DISCORD_WEBHOOK_URL=https://discord.com/api/webhooks/... export NOTIFICATION_CHANNEL=discord Slack: export SLACK_WEBHOOK_URL=https://hooks.slack.com/... export NOTIFICATION_CHANNEL=slack β οΈ Security: Webhook URLs must be set via environment variables. No hardcoded URLs allowed.
3. Optional: Enable Oura Integration
export OURA_ENABLED=true Requires Oura CLI authentication.
4. Run
# Daily training check + alerts python3 scripts/coach_check.py # Weekly summary report python3 scripts/weekly_report.py Optional: schedule with cron for hands-off monitoring: { "name": "Training Coach - Daily Check", "schedule": {"kind": "every", "everyMs": 86400000}, "command": "python3 scripts/coach_check.py" }
Security Features
This skill is designed with security in mind for ClawHub publication:
Credential Handling
No hardcoded secrets β All credentials via environment variables Secure token storage β Tokens saved with 0600 permissions XDG compliance β Config stored in ~/.config/strava-training-coach/ Token validation β Structure validation before use
Input Validation
Date format validation β ISO8601 format checking Numeric range validation β All thresholds bounded Type checking β Safe type conversion with defaults Webhook URL validation β Pattern matching for Discord/Slack
Data Protection
Log redaction β Sensitive data masked in logs Secure temp files β Proper permissions on state files No data leakage β Safe error messages Rate limiting β Max 1 alert per hour per type
Network Security
HTTPS only β All API calls use TLS Timeout handling β 30-second timeouts on all requests Retry logic β 3 attempts with exponential backoff Certificate validation β Standard SSL verification
Configuration
All thresholds are optional β sensible defaults with validation. # Training thresholds (validated ranges) MAX_WEEKLY_MILEAGE_JUMP=30 # 5-100%, default: 30 MAX_HARD_DAY_PERCENTAGE=25 # 5-100%, default: 25 MIN_EASY_RUN_HEART_RATE=145 # 100-200 bpm, default: 145 # Feature flags OURA_ENABLED=false # Enable Oura integration VERBOSE=false # Enable debug logging
Injury Risk
"Weekly mileage up 45% (18 -> 26 mi). ACWR: 1.62. Nielsen et al. (2014) found >30% weekly increases significantly raise injury risk. Your acute:chronic workload ratio is in the high-risk zone (>1.5). Reduce next week's volume by 20-30%." "60% of runs were moderate/high effort (HR >145). Seiler (2010) found elite athletes keep ~80% of sessions below VT1. Polarized training produces better VO2max gains than moderate-intensity training (Stoggl & Sperlich, 2014)." "5 days since last activity. Mujika & Padilla (2000) found VO2max begins declining after ~10 days of inactivity. A gentle 20-min walk or easy jog can maintain adaptations."
Achievements
"30-Day Streak! Consistency beats intensity. Holloszy & Coyle (1984) showed mitochondrial density increases with repeated aerobic stimulus."
Weekly Reports (Sunday)
Weekly mileage with week-over-week change % Acute:Chronic Workload Ratio (ACWR) with risk zone Intensity distribution (easy/moderate/hard) vs. 80/20 target 4-week trend visualization Evidence-based recommendations for next week
Training Philosophy (Evidence-Based)
Polarized Training β 80% easy, 20% hard (Seiler & Kjerland, 2006; Stoggl & Sperlich, 2014) ACWR Sweet Spot β Keep acute:chronic workload ratio between 0.8-1.3 (Gabbett, 2016) Progressive Overload β Gradual increases; >30% weekly spikes raise injury risk (Nielsen et al., 2014) Consistency > Intensity β Frequency drives mitochondrial and capillary adaptation (Holloszy & Coyle, 1984) Strength Training β Reduces sports injuries by 68% and overuse injuries by ~50% (Lauersen et al., 2014) See references/training-principles.md for the full guide with 30+ scientific references.
Files
scripts/auth.py β Strava OAuth setup (tokens stored in XDG config dir) scripts/coach_check.py β Daily training analysis and alerts (security-hardened) scripts/weekly_report.py β Sunday summary reports (security-hardened) references/training-principles.md β Evidence-based injury prevention guide
Smart, Not Spammy
Alerts fire only when something matters: Mileage spike detected Intensity pattern concerning Meaningful PR achieved Weekly summary ready Not every workout. That's what Strava is for.
Rate Limits
1-2 API calls per check Strava allows 100 req/15 min, 1000/day Daily checks use ~30 requests/month
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs
- SKILL.md
- references/training-principles.md
- scripts/auth.py
- scripts/coach_check.py
- scripts/weekly_report.py