9.2 KiB
9.2 KiB
Operation: Full Quality Analysis
Execute comprehensive quality analysis orchestrating all sub-operations to generate complete assessment.
Parameters from $ARGUMENTS
Extract these parameters from $ARGUMENTS:
- path: Target path to analyze (required)
- context: Path to validation context JSON file with prior results (optional)
- format: Report output format - markdown|json|html (default: markdown)
- output: Output file path for report (optional)
Full Analysis Workflow
This operation orchestrates all quality-analysis sub-operations to provide a complete quality assessment.
1. Load Validation Context
IF context parameter provided:
Read validation results from JSON file
Extract:
- Errors count
- Warnings count
- Missing fields count
- Validation layer results
- Detailed issue list
ELSE:
Use default values:
- errors: 0
- warnings: 0
- missing: 0
2. Calculate Base Score
Read calculate-score.md operation instructions
Execute scoring with validation results:
python3 .scripts/scoring-algorithm.py \
--errors $errors \
--warnings $warnings \
--missing $missing \
--format json
Capture:
- Quality score (0-100)
- Rating (Excellent/Good/Fair/Needs Improvement/Poor)
- Star rating (⭐⭐⭐⭐⭐)
- Publication readiness status
3. Prioritize All Issues
Read prioritize-issues.md operation instructions
IF context has issues:
Write issues to temporary JSON file
Execute issue prioritization:
bash .scripts/issue-prioritizer.sh $temp_issues_file
Capture:
- P0 (Critical) issues with details
- P1 (Important) issues with details
- P2 (Recommended) issues with details
ELSE:
Skip (no issues to prioritize)
4. Generate Improvement Suggestions
Read suggest-improvements.md operation instructions
Generate actionable recommendations:
Target score: 90 (publication-ready)
Current score: $calculated_score
Generate suggestions for:
- Quick wins (< 30 min, high impact)
- This week improvements (< 2 hours)
- Long-term enhancements
Include:
- Score impact per suggestion
- Effort estimates
- Priority assignment
- Detailed fix instructions
5. Generate Comprehensive Report
Read generate-report.md operation instructions
Execute report generation:
python3 .scripts/report-generator.py \
--path $path \
--format $format \
--context $aggregated_context \
--output $output
Report includes:
- Executive summary
- Quality score and rating
- Validation layer breakdown
- Prioritized issues (P0/P1/P2)
- Improvement recommendations
- Detailed findings
6. Aggregate and Display Results
Combine all outputs into unified assessment:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
COMPREHENSIVE QUALITY ANALYSIS
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Target: <path>
Type: <marketplace|plugin>
Analyzed: <timestamp>
QUALITY SCORE: <0-100>/100 <⭐⭐⭐⭐⭐>
Rating: <rating>
Publication Ready: <Yes|No|With Changes>
CRITICAL ISSUES: <P0 count>
IMPORTANT ISSUES: <P1 count>
RECOMMENDATIONS: <P2 count>
[Executive Summary - 2-3 sentences on readiness]
[If not publication-ready, show top 3 quick wins]
[Report file location if output specified]
Workflow Steps
-
Initialize Analysis
Validate path exists Load validation context if provided Set up temporary files for intermediate results -
Execute Operations Sequentially
Step 1: Calculate Score └─→ Invoke scoring-algorithm.py └─→ Store result in context Step 2: Prioritize Issues (if issues exist) └─→ Invoke issue-prioritizer.sh └─→ Store categorized issues in context Step 3: Generate Suggestions └─→ Analyze score gap └─→ Create actionable recommendations └─→ Store in context Step 4: Generate Report └─→ Invoke report-generator.py └─→ Aggregate all context data └─→ Format in requested format └─→ Output to file or stdout -
Present Summary
Display high-level results Show publication readiness Highlight critical blockers (if any) Show top quick wins Provide next steps
Examples
# Full analysis with validation context
/quality-analysis full-analysis path:. context:"@validation-results.json"
# Full analysis generating HTML report
/quality-analysis full-analysis path:. format:html output:quality-report.html
# Full analysis with JSON output
/quality-analysis full-analysis path:. context:"@results.json" format:json output:analysis.json
# Basic full analysis (no prior context)
/quality-analysis full-analysis path:.
Error Handling
- Missing path: Request target path parameter
- Invalid context file: Continue with limited data, show warning
- Script execution failures: Show which operation failed, provide fallback
- Output write errors: Fall back to stdout with warning
- No issues found: Congratulate on perfect quality, skip issue operations
Output Format
Terminal Output:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
COMPREHENSIVE QUALITY ANALYSIS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Target: /path/to/plugin
Type: Claude Code Plugin
Analyzed: 2025-10-13 14:30:00
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QUALITY SCORE
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85/100 ⭐⭐⭐⭐ (Good)
Publication Ready: With Minor Changes
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ISSUES SUMMARY
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Critical (P0): 0 ✅
Important (P1): 3 ⚠️
Recommended (P2): 5 💡
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
EXECUTIVE SUMMARY
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Your plugin is nearly ready for publication! No critical blockers
found. Address 3 important issues to reach excellent status (90+).
Quality foundation is solid with good documentation and security.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TOP QUICK WINS
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1. [+10 pts] Add CHANGELOG.md (15 minutes)
Impact: Improves version tracking
Fix: Create CHANGELOG.md with version history
2. [+3 pts] Add 2 more keywords (5 minutes)
Impact: Better discoverability
Fix: Add relevant keywords to plugin.json
3. [+2 pts] Add repository URL (2 minutes)
Impact: Professional appearance
Fix: Add repository field to plugin.json
After Quick Wins: 100/100 ⭐⭐⭐⭐⭐ (Excellent)
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DETAILED REPORT
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Full report saved to: quality-report.md
Next Steps:
1. Review detailed report for all findings
2. Implement quick wins (22 minutes total)
3. Re-run validation to verify improvements
4. Submit to OpenPlugins marketplace
Questions? Consult: docs.claude.com/plugins
Integration Notes
This operation is the primary entry point for complete quality assessment.
Invoked by:
validation-orchestratorafter comprehensive validationmarketplace-validatoragent for submission readiness- Direct user invocation for full assessment
Orchestrates:
calculate-score.md- Quality scoringprioritize-issues.md- Issue categorizationsuggest-improvements.md- Actionable recommendationsgenerate-report.md- Comprehensive reporting
Data Flow:
Validation Results
↓
Calculate Score → score, rating, stars
↓
Prioritize Issues → P0/P1/P2 categorization
↓
Suggest Improvements → actionable recommendations
↓
Generate Report → formatted comprehensive report
↓
Display Summary → user-friendly terminal output
Performance
- Execution Time: 2-5 seconds (depending on issue count)
- I/O Operations: Minimal (uses temporary files for large datasets)
- Memory Usage: Low (streaming JSON processing)
- Parallelization: Sequential (each step depends on previous)
Quality Assurance
Validation Steps:
- Verify all scripts are executable
- Check Python 3.6+ availability
- Validate JSON context format
- Verify write permissions for output
- Ensure scoring algorithm consistency
Testing:
# Test with perfect plugin
/quality-analysis full-analysis path:./test-fixtures/perfect-plugin
# Test with issues
/quality-analysis full-analysis path:./test-fixtures/needs-work
# Test report formats
/quality-analysis full-analysis path:. format:json
/quality-analysis full-analysis path:. format:html
/quality-analysis full-analysis path:. format:markdown
Request: $ARGUMENTS