home / skills / microck / ordinary-claude-skills / training-log-analyzer

training-log-analyzer skill

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This skill analyzes training logs to reveal progress trends, plateaus, recovery needs, peak windows, and injury risk, guiding data-driven adjustments.

npx playbooks add skill microck/ordinary-claude-skills --skill training-log-analyzer

Review the files below or copy the command above to add this skill to your agents.

Files (2)

SKILL.md

1.5 KB

---
name: training-log-analyzer
description: Track workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.
---

# 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

```markdown
# 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!

Overview

This skill analyzes training logs to track workouts, stats, and progress over time. It highlights improvement trends, detects plateaus, flags recovery needs and injury risk indicators, and suggests timing for peak performance. The goal is to turn raw training data into concise, actionable guidance for athletes and coaches.

How this skill works

The analyzer ingests structured training logs (dates, session type, load, volume, intensity, subjective measures, and sleep/recovery metrics) and computes trends, rolling averages, and variability. It detects plateaus by comparing recent performance to baseline trends, identifies recovery deficits from cumulative load and subjective fatigue, and scores injury risk using sudden-load spikes and persistent fatigue signals. Outputs include visual-ready summaries, prioritized issues, and concrete training adjustments.

When to use it

After 4–8 weeks of logged training to evaluate trends
When performance improvements stall or regress
To plan peaking phases before competition
If subjective fatigue, sleep changes, or niggles appear
When returning from injury or changing training load

Best practices

Log consistent, structured data: session type, load, RPE, sleep, and soreness
Use a minimum 4-week window to establish baseline trends
Combine objective metrics (power, pace, weight) with subjective inputs (RPE, sleep quality)
Prioritize one or two changes at a time: small, measurable adjustments
Re-run analysis weekly or after major blocks to validate responses

Example use cases

Identify a 3-week plateau in 5K pace and recommend tempo and recovery adjustments
Detect early injury risk after a 30% training load spike and suggest deload and monitoring steps
Recommend an 8–10 day taper plan to align peak form with race day
Show strength training load trends and propose volume progression to break a stagnation
Evaluate return-to-run plans by comparing pre-injury and current load tolerance

FAQ

What input format works best?

CSV or JSON with date, session type, objective load (distance, time, weight), intensity (RPE, pace, power), and recovery metrics (sleep, soreness) yields the most actionable results.

How often should I analyze logs?

Weekly reviews give good cadence; re-run after any major block, illness, or sudden load change to reassess risk and progress.

Can it predict injury with certainty?

No tool predicts injury with 100% certainty. It flags elevated risk based on load spikes, accumulated fatigue, and symptom patterns to guide preventive action.