Analyzes experimental results comparing code-execution vs direct-MCP approaches for any task. Extracts performance metrics, execution logs, and workspace outputs from zip archives to generate detailed comparison reports. Use when you have a results zip file and need to compare code-execution vs direct-MCP performance, cost, and output quality for any task.
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SKILL.md
MCP vs Code Execution Results Comparison Analyzer
Instructions
This skill analyzes zip files containing experimental comparison data between code-execution and direct-MCP approaches. It extracts performance metrics, execution logs, and workspace outputs to generate comprehensive comparison reports with actionable recommendations.
Usage
Provide the path to a results zip file
The skill extracts and discovers all files (adapts to varying structures)
Parses metrics, logs, and catalogs workspace outputs
Generates comparison report and saves data to ./workspace/
Features
Flexible Discovery: Automatically scans and adapts to any zip structure
Failed Run Detection: Identifies failed runs via FAILED__ prefix in metrics files, separates from successful runs
Output Quality Assessment: Compares completeness and accuracy of results
Hybrid Analysis: Script aggregates data, model performs semantic comparison
Actionable Recommendations: Clear guidance on when to use each approach
Comparison Dimensions
The skill compares both approaches across:
Performance: Execution time, number of turns, patterns
Cost: USD per run, token costs, cache utilization
Token Efficiency: Input/output/cache ratios
Output Quality: Completeness, accuracy, format (by reading workspace files)
Examples
import { compareResults } from './.claude/skills/results-comparison-analyzer/implementation';
// Analyze a results zip file
const zipPath = './example_results/task_results-2025-11-20T06-22-50-300Z.zip';
await compareResults(zipPath);
// The function will:
// 1. Extract and parse all metrics and logs
// 2. Catalog workspace outputs with excerpts
// 3. Aggregate statistics by approach
// 4. Save comparison-data.json
// 5. Return structured data for model analysis
Output Files
The skill automatically saves:
./workspace/comparison-data.json - Aggregated metrics, file catalog, raw data including failure summary
- Executive summary with key findings - Success/failure rates for each approach - Performance comparison (successful runs only) - Approach analysis (pros/cons) - Output quality assessment - Recommendations for when to use each approach
Changelog
2025-11-19: Initial version - flexible discovery and hybrid analysis