Training Log Analyzer
- 249 installs
- 237 repo stars
- Updated July 15, 2026
- onewave-ai/claude-skills
Parse workout and training logs to surface volume trends, recovery gaps, plateau signals, and progression insights for athletes or coaching programs.
About
Analyzes athletic training logs to extract volume trends, recovery patterns, plateau signals, and progression metrics, turning raw workout history into actionable coaching and program-adjustment insights.
- Volume trends
- Recovery gaps
- Plateau detection
- Progression metrics
- Log parsing
Training Log Analyzer by the numbers
- 249 all-time installs (skills.sh)
- +6 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #612 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/onewave-ai/claude-skills --skill training-log-analyzerAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 249 |
|---|---|
| repo stars | ★ 237 |
| Last updated | July 15, 2026 |
| Repository | onewave-ai/claude-skills ↗ |
What it does
Parse workout and training logs to surface volume trends, recovery gaps, plateau signals, and progression insights for athletes or coaching programs.
Files
Training Log Analyzer
Track workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.
Instructions
You are an expert sports scientist and performance analyst. Analyze training logs to identify: improvement trends, plateau periods, rest/recovery needs, peak performance windows, injury risk indicators, and data-driven training adjustments.
Output Format
# Training Log Analyzer Output
**Generated**: {timestamp}
---
## Results
[Your formatted output here]
---
## Recommendations
[Actionable next steps]
Best Practices
1. Be Specific: Focus on concrete, actionable outputs 2. Use Templates: Provide copy-paste ready formats 3. Include Examples: Show real-world usage 4. Add Context: Explain why recommendations matter 5. Stay Current: Use latest best practices for fitness
Common Use Cases
Trigger Phrases:
- "Help me with [use case]"
- "Generate [output type]"
- "Create [deliverable]"
Example Request:
"[Sample user request here]"
Response Approach: 1. Understand user's context and goals 2. Generate comprehensive output 3. Provide actionable recommendations 4. Include examples and templates 5. Suggest next steps
Remember: Focus on delivering value quickly and clearly!
Related skills
Microsoft FoundryDeploy, evaluate, and continuously improve Microsoft Foundry agents from a single agent interface.490k1.3k
Ai Research ReproductionOrchestrate trustworthy, auditable reproduction of deep learning repositories directly from their READMEs.176k511Run TrainSafely execute selected deep learning training commands with standardized evidence capture.176k511Explore RunSafely run isolated exploratory experiments with clear recording and conservative selection before committing changes.176k511Paper Context ResolverFetch precise reproduction-critical details like dataset splits, preprocessing steps, or evaluation protocols from the original academic paper when the repo README leav141k511Repo Intake And PlanScan unfamiliar AI research repositories and receive a minimal, trustworthy reproduction target before investing significant time.140k511