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    "article": "\n# MaTriXy/Skillachi\n\n- **Archetype**: benchmark-evaluation-harness\n- **Stars**: 1\n- **Last pushed**: 2026 (recent)\n- **License**: MIT (implied)\n- **Discovered**: 2026-04-13\n- **Skills found**: 0 (benchmark dataset, not skill collection)\n\n## Summary\nA comprehensive benchmark dataset for evaluating AI coding skills across 39 engineering roles. Contains 121 real-world coding assignments extracted from merged GitHub pull requests. Each benchmark task includes a base commit state, implementation request description, specific scoring criteria (5 per task), relevant skill identifiers, and references to original issues. Supports evaluation across seven domains with coverage ranging from 3-10 benchmarks per role (average quality: 7.6/10).\n\n## Assessment\nHIGH VALUE for validation methodology. The \"base commit checkpoint\" design enables reproducible testing by pinpointing exact repository states, distinguishing this from synthetic benchmarks. The framework provides multi-model scoring (Claude, Codex, Gemini evaluation), resume-safe distributed execution for 30-85 hour full runs, role-based task stratification, and automated leaderboard generation with GitHub Pages publishing. The augmented catalog contains ~4,700 skill definitions and skills marketplace metadata.\n\n## Extraction Priority\n- Medium\n- Rationale: No extractable skills or processes, but the benchmark methodology and role-based evaluation framework could inform babysitter's own testing and validation processes. The reproducible testing pattern and multi-model evaluation approach are transferable.\n\n## Processes\n- **AI Agent Benchmark Framework**: Create reproducible benchmarks with base commit states, implementation requests, and scoring criteria for agent evaluation\n- **Role-Based Task Stratification**: Organize evaluation tasks by engineering roles and domains for targeted capability assessment\n- **Multi-Model Evaluation Process**: Coordinate evaluation across multiple AI models with automated scoring and leaderboard generation\n\n## Plugin Ideas\nNone - this is a benchmarking framework, not a tool integration opportunity.\n\n## Patterns\n- Base commit checkpoint for reproducible state restoration\n- Real-world task extraction from merged PRs\n- Role taxonomy across 7 domains (39 distinct engineering roles)\n- Multi-model scoring with resume-safe distributed execution\n- Quality scoring (average 7.6/10) with explicit criteria\n\n## Library Mapping\n\n| Extractable Process | Library Status | Action | Existing Path | Target Placement |\n|-------------------|----------------|--------|---------------|------------------|\n| AI Agent Benchmark Framework | NEW | Reproducible benchmark creation with base commit states and scoring criteria | - | methodologies/ai-agent-benchmark-framework/ |\n| Role-Based Task Stratification | NEW | Engineering role taxonomy and task organization for capability assessment | - | specializations/shared/role-based-task-stratification.js |\n| Multi-Model Evaluation Process | NEW | Coordinated evaluation across multiple AI models with automated scoring | - | specializations/shared/multi-model-evaluation-process.js |\n| Reproducible Testing Methodology | NEW | Base commit checkpoint pattern for consistent evaluation environments | - | specializations/shared/reproducible-testing-methodology.js |\n| Quality Assessment Framework | NEW | Scoring criteria development and quality evaluation for benchmark tasks | - | specializations/shared/quality-assessment-framework.js |\n\n## Plugin Marketplace Mapping\n\n| Plugin Idea | Marketplace Status | Action | Existing Plugin | Target Placement |\n|-------------|-------------------|--------|-----------------|------------------|\n| Benchmark Runner Integration | NEW | External benchmark execution and scoring integration | - | plugins/a5c/marketplace/blueprints/benchmark-runner-integration/ |\n",
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