/skill-review
Analyze skill execution metrics and identify unstable or underperforming skills using continual learning analysis.
$ npx -y skills add athola/claude-night-market --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/skill-review
Context preview
What this command does when you run it.
Analyze skill execution metrics and identify unstable or underperforming skills using continual learning analysis.
Command definition
skill-review.mdSkill Performance Review
Analyze skill execution metrics and identify unstable or underperforming skills using continual learning analysis.
Usage
/skill-review # Show all skill metrics
/skill-review --all-plugins # Aggregate by plugin
/skill-review --skill <plugin:skill> # Specific skill deep-dive
/skill-review --unstable-only # Only skills with stability_gap > 0.3
/skill-review --top <n> # Top N by execution count
/skill-review --recommendations # Get improvement recommendations
What It Analyzes
Continual Learning Metrics
For each skill, calculates:
| Metric | Description | |--------|-------------| | **Execution Count** | Total invocations | | **Success Rate** | Percentage of successful executions | | **Average Duration** | Mean execution time | | **Worst-Case Accuracy** | Lowest accuracy in execution history | | **Stability Gap** | `average_accuracy - worst_case_accuracy` |
Stability Gap (Key Metric)
The stability gap detects inconsistent skills - those that work sometimes but fail unpredictably:
stability_gap = average_accuracy - worst_case_accuracy
**Interpretation:**
- ✓ **< 0.2**: Stable - consistent performance
- ⚠️ **0.2 - 0.3**: Warning - occasional issues
- ✗ **> 0.3**: Unstable - needs attention
**Why it matters**: Traditional metrics only measure average performance. Stability gap catches skills that are "usually fine but sometimes catastrophically wrong."
Examples
View All Metrics
/skill-review
Output:
Skill Performance Review
========================
abstract:skill-auditor:
Executions: 47
Success rate: 94%
Average duration: 134ms
Worst-case accuracy: 0.85
Stability gap: 0.09 ✓ (stable)
sanctum:pr-review:
Executions: 23
Success rate: 91%
Average duration: 2.1s
Worst-case accuracy: 0.82
Stability gap: 0.09 ✓ (stable)
imbue:proof-of-work:
Executions: 31
Success rate: 68%
Average duration: 1.8s
Worst-case accuracy: 0.40
Stability gap: 0.38 ⚠️ (unstable)
Find Unstable Skills
/skill-review --unstable-only
Output:
Skills with Stability Gap > 0.3
================================
imbue:proof-of-work
Stability gap: 0.38
Issue: High failure rate (32%)
Suggestion: Review recent failures with /memory-palace:skill-logs --skill imbue:proof-of-work --failures-only
your-custom:my-skill
Stability gap: 0.45
Issue: Inconsistent performance
Suggestion: Check error patterns in execution logs
Deep-Dive Specific Skill
/skill-review --skill abstract:skill-auditor
Output:
Skill Analysis: abstract:skill-auditor
======================================
Summary:
Total executions: 47
First seen: 2025-01-01
Last used: 2025-01-08
Performance:
Success rate: 94% (44/47)
Average duration: 134ms
Median duration: 128ms
95th percentile: 245ms
Stability:
Worst-case accuracy: 0.85
Stability gap: 0.09 ✓
Trend: Improving (last 10: 100% success)
Recent Failures (3):
2025-01-05: "Skill file not found"
2025-01-03: "Invalid frontmatter format"
2025-01-02: "Timeout exceeded"
Recommendation: HEALTHY - No action needed
By Plugin
/skill-review --all-plugins
Output:
Metrics by Plugin
=================
abstract:
Skills tracked: 12
Total executions: 1,234
Avg success rate: 92%
Skills with issues: 2
sanctum:
Skills tracked: 8
Total executions: 856
Avg success rate: 89%
Skills with issues: 1
memory-palace:
Skills tracked: 6
Total executions: 423
Avg success rate: 95%
Skills with issues: 0
Get Recommendations
/skill-review --recommendations
Output:
Skill Improvement Recommendations
=================================
CRITICAL (stability_gap > 0.5):
None
NEEDS ATTENTION (stability_gap > 0.3):
imbue:proof-of-work
- Review last 5 failures for common patterns
- Consider adding input validation
- Check for environmental dependencies
MONITORING (stability_gap 0.2 - 0.3):
conjure:template-engine
- Performance variance detected
- Consider caching or optimization
HEALTHY (stability_gap < 0.2):
45 skills performing wellIntegration
Data Source
Reads from metrics stored by memory-palace:
- **Logs**: `~/.claude/skills/logs/*/*/*.jsonl`
- **History**: `~/.claude/skills/logs/.history.json`
Related Commands
| Command | Plugin | Description | |---------|--------|-------------| | `skill-logs` | sanctum | View raw execution history and failures | | `skill-review` | pensive | Analyze metrics and stability (this command) | | `skill-history` | pensive | Recent executions with context | | `skill-auditor` | abstract | Deep skill quality analysis (static) |
Output Formats
Default: Markdown with color-coded indicators JSON: Add `--format json` for programmatic access
Implementation
Uses the continual evaluation approach from Avalanche (ICLR 2023) to detect: 1. **Accuracy degradation**: Skills getting worse over time 2. **Stability issues**: Skills with high variance 3. **Performance regressions**: Duration increases
This enables proactive skill improvement before users notice problems.
Read more
Skill Performance Review
Analyze skill execution metrics and identify unstable or underperforming skills using continual learning analysis.
Usage
/skill-review # Show all skill metrics /skill-review --all-plugins # Aggregate by plugin /skill-review --skill <plugin:skill> # Specific skill deep-dive /skill-review --unstable-only # Only skills with stability_gap > 0.3 /skill-review --top <n> # Top N by execution count /skill-review --recommendations # Get improvement recommendations
What It Analyzes
Continual Learning Metrics
For each skill, calculates:
| Metric | Description | |--------|-------------| | **Execution Count** | Total invocations | | **Success Rate** | Percentage of successful executions | | **Average Duration** | Mean execution time | | **Worst-Case Accuracy** | Lowest accuracy in execution history | | **Stability Gap** | `average_accuracy - worst_case_accuracy` |
Stability Gap (Key Metric)
The stability gap detects inconsistent skills - those that work sometimes but fail unpredictably:
stability_gap = average_accuracy - worst_case_accuracy
**Interpretation:**
- ✓ **< 0.2**: Stable - consistent performance
- ⚠️ **0.2 - 0.3**: Warning - occasional issues
- ✗ **> 0.3**: Unstable - needs attention
**Why it matters**: Traditional metrics only measure average performance. Stability gap catches skills that are "usually fine but sometimes catastrophically wrong."
Examples
View All Metrics
/skill-review
Output:
Skill Performance Review ======================== abstract:skill-auditor: Executions: 47 Success rate: 94% Average duration: 134ms Worst-case accuracy: 0.85 Stability gap: 0.09 ✓ (stable) sanctum:pr-review: Executions: 23 Success rate: 91% Average duration: 2.1s Worst-case accuracy: 0.82 Stability gap: 0.09 ✓ (stable) imbue:proof-of-work: Executions: 31 Success rate: 68% Average duration: 1.8s Worst-case accuracy: 0.40 Stability gap: 0.38 ⚠️ (unstable)
Find Unstable Skills
/skill-review --unstable-only
Output:
Skills with Stability Gap > 0.3 ================================ imbue:proof-of-work Stability gap: 0.38 Issue: High failure rate (32%) Suggestion: Review recent failures with /memory-palace:skill-logs --skill imbue:proof-of-work --failures-only your-custom:my-skill Stability gap: 0.45 Issue: Inconsistent performance Suggestion: Check error patterns in execution logs
Deep-Dive Specific Skill
/skill-review --skill abstract:skill-auditor
Output:
Skill Analysis: abstract:skill-auditor ====================================== Summary: Total executions: 47 First seen: 2025-01-01 Last used: 2025-01-08 Performance: Success rate: 94% (44/47) Average duration: 134ms Median duration: 128ms 95th percentile: 245ms Stability: Worst-case accuracy: 0.85 Stability gap: 0.09 ✓ Trend: Improving (last 10: 100% success) Recent Failures (3): 2025-01-05: "Skill file not found" 2025-01-03: "Invalid frontmatter format" 2025-01-02: "Timeout exceeded" Recommendation: HEALTHY - No action needed
By Plugin
/skill-review --all-plugins
Output:
Metrics by Plugin ================= abstract: Skills tracked: 12 Total executions: 1,234 Avg success rate: 92% Skills with issues: 2 sanctum: Skills tracked: 8 Total executions: 856 Avg success rate: 89% Skills with issues: 1 memory-palace: Skills tracked: 6 Total executions: 423 Avg success rate: 95% Skills with issues: 0
Get Recommendations
/skill-review --recommendations
Output:
Skill Improvement Recommendations
=================================
CRITICAL (stability_gap > 0.5):
None
NEEDS ATTENTION (stability_gap > 0.3):
imbue:proof-of-work
- Review last 5 failures for common patterns
- Consider adding input validation
- Check for environmental dependencies
MONITORING (stability_gap 0.2 - 0.3):
conjure:template-engine
- Performance variance detected
- Consider caching or optimization
HEALTHY (stability_gap < 0.2):
45 skills performing wellIntegration
Data Source
Reads from metrics stored by memory-palace:
- **Logs**: `~/.claude/skills/logs/*/*/*.jsonl`
- **History**: `~/.claude/skills/logs/.history.json`
Related Commands
| Command | Plugin | Description | |---------|--------|-------------| | `skill-logs` | sanctum | View raw execution history and failures | | `skill-review` | pensive | Analyze metrics and stability (this command) | | `skill-history` | pensive | Recent executions with context | | `skill-auditor` | abstract | Deep skill quality analysis (static) |
Output Formats
Default: Markdown with color-coded indicators JSON: Add `--format json` for programmatic access
Implementation
Uses the continual evaluation approach from Avalanche (ICLR 2023) to detect: 1. **Accuracy degradation**: Skills getting worse over time 2. **Stability issues**: Skills with high variance 3. **Performance regressions**: Duration increases
This enables proactive skill improvement before users notice problems.
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