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Purpose (PCTF: Purpose)\n\nEnable competitive code generation where three isolated AI agents implement the same functionality, evaluate each other objectively, and deliver the optimal solution through data-driven selection.\n\n---\n\n## 2. Task Definition (PCTF: Task)\n\n### Input\n- **task_description**: String describing the coding task\n- **constraints**: Optional constraints (time/space complexity, language, etc.)\n\n### Output\n- **final_solution**: Directory containing the winning implementation\n- **comparison_report**: Markdown analysis of all three approaches\n- **decision_rationale**: Explanation of why the winner was selected\n\n### Success Criteria\n```yaml\nassertions:\n  - final_solution/implementation exists and is runnable\n  - comparison_report.md exists with objective metrics\n  - decision_rationale.md explains selection logic\n  - all three agent implementations are documented\n  - evaluation scores are numeric and justified\n```\n\n---\n\n## 3. Chain Flow (PCTF: Chain)\n\n```mermaid\ngraph TD\n    A[User Task] --> B[Phase 1: Parallel Spawn]\n    B --> C[Agent A: Simplicity]\n    B --> D[Agent B: Speed]\n    B --> E[Agent C: Robustness]\n    C --> F[Phase 2: Cross-Evaluation]\n    D --> F\n    E --> F\n    F --> G[6 Evaluation Reports]\n    G --> H[Phase 3: Self-Scoring]\n    H --> I[3 Scorecards]\n    I --> J[Phase 4: Final Delivery]\n    J --> K[Best Solution]\n```\n\n### Phase 1: Parallel Implementation\n**Agent Prompt Template**:\n```yaml\nrole: \"Expert Software Engineer\"\nfocus: \"{{agent_focus}}\"  # Simplicity / Speed / Robustness\ntask: \"{{task_description}}\"\nconstraints:\n  - Complete runnable code in implementation/\n  - Checklist.md with ALL items checked\n  - SUMMARY.md with competitive advantages\n  - Must differ from other agents' approaches\n\nlinter_rules:\n  - code_compiles: true\n  - tests_pass: true\n  - no_todos: true\n  - documented: true\n\nassertions:\n  - implementation/main.* exists\n  - tests exist and pass\n  - Checklist.md is complete\n  - SUMMARY.md explains unique approach\n```\n\n### Phase 2: Cross-Evaluation\n**Evaluation Prompt Template**:\n```yaml\nevaluator: \"Agent {{from}}\"\ntarget: \"Agent {{to}}\"\ntask: \"Objectively prove your solution is superior\"\n\ndimensions:\n  simplicity:\n    weight: 20\n    metrics:\n      - lines_of_code: count\n      - cyclomatic_complexity: calculate\n      - readability_score: 1-10\n  \n  speed:\n    weight: 25\n    metrics:\n      - time_complexity: big_o\n      - space_complexity: big_o\n      - benchmark_results: run_if_possible\n  \n  stability:\n    weight: 25\n    metrics:\n      - error_handling_coverage: percentage\n      - resource_cleanup: check\n      - fault_tolerance: test\n  \n  corner_cases:\n    weight: 20\n    metrics:\n      - input_validation: comprehensive\n      - boundary_conditions: covered\n      - edge_cases: tested\n  \n  maintainability:\n    weight: 10\n    metrics:\n      - documentation_quality: 1-10\n      - code_structure: logical\n      - extensibility: easy/hard\n\nassertions:\n  - evaluation is objective with data\n  - specific code snippets cited\n  - numeric scores provided\n  - persuasion argument is data-driven\n```\n\n### Phase 3: Objective Scoring\n**Scoring Prompt Template**:\n```yaml\nagent: \"Agent {{name}}\"\ntask: \"Fairly score yourself and competitors\"\n\nself_evaluation:\n  - dimension: simplicity\n    max: 20\n    score: \"{{self_score}}\"\n    justification: \"{{why}}\"\n  \n  - dimension: speed\n    max: 25\n    score: \"{{self_score}}\"\n    justification: \"{{why}}\"\n  \n  - dimension: stability\n    max: 25\n    score: \"{{self_score}}\"\n    justification: \"{{why}}\"\n  \n  - dimension: corner_cases\n    max: 20\n    score: \"{{self_score}}\"\n    justification: \"{{why}}\"\n  \n  - dimension: maintainability\n    max: 10\n    score: \"{{self_score}}\"\n    justification: \"{{why}}\"\n\npeer_evaluation:\n  - target: \"Agent {{other}}\"\n    scores: \"{{numeric_scores}}\"\n    comparison: \"{{objective_comparison}}\"\n\nfinal_conclusion:\n  best_implementation: \"[A/B/C/Mixed]\"\n  reasoning: \"{{data_driven_justification}}\"\n  recommendation: \"{{delivery_strategy}}\"\n\nassertions:\n  - all scores are numeric\n  - justifications are specific\n  - no inflation or bias\n  - conclusion is evidence-based\n```\n\n### Phase 4: Final Delivery\n**Decision Logic**:\n```python\ndef select_winner(scores):\n    \"\"\"\n    Select final solution based on competitive scores\n    \"\"\"\n    margins = calculate_score_margins(scores)\n    \n    if margins.winner - margins.second > 15:\n        # Clear winner\n        return SingleWinner(scores.winner)\n    elif margins.winner - margins.second > 5:\n        # Close competition, consider hybrid\n        return HybridSolution(scores.top_two)\n    else:\n        # Very close, pick simplest\n        return SimplestImplementation(scores.all)\n\nassertions:\n  - final_solution is runnable\n  - comparison_report explains all approaches\n  - decision_rationale is transparent\n  - attribution is given to winning agent\n```\n\n---\n\n## 4. Format Specifications (PCTF: Format)\n\n### Directory Structure\n```\nworkspace/\n├── run_a/\n│   ├── implementation/      # Agent A code\n│   ├── Checklist.md         # Completion checklist\n│   ├── SUMMARY.md           # Approach summary\n│   ├── evaluation/          # Evaluations of B, C\n│   └── SCORECARD.md         # Self-scoring\n├── run_b/                   # Same structure\n├── run_c/                   # Same structure\n├── final/                   # Winning solution\n├── COMPARISON_REPORT.md     # Full analysis\n└── DECISION_RATIONALE.md    # Why winner selected\n```\n\n### File Formats\n- **Checklist.md**: Markdown with `- [x]` checkboxes\n- **SUMMARY.md**: Markdown with sections\n- **EVALUATION_*.md**: Markdown with tables\n- **SCORECARD.md**: Markdown with score tables\n- **Implementation**: Runnable code files\n\n---\n\n## 5. Linter & Validation\n\n### Pre-commit Checks\n```bash\n#!/bin/bash\n# scripts/lint.sh\n\nlint_agent_output() {\n    local agent_dir=\"$1\"\n    local errors=0\n    \n    # Check required files exist\n    for file in Checklist.md SUMMARY.md implementation/main.*; do\n        if [[ ! -f \"${agent_dir}/${file}\" ]]; then\n            echo \"ERROR: Missing ${file}\"\n            ((errors++))\n        fi\n    done\n    \n    # Check Checklist is complete\n    if grep -q \"\\[ \\]\" \"${agent_dir}/Checklist.md\"; then\n        echo \"ERROR: Checklist has unchecked items\"\n        ((errors++))\n    fi\n    \n    # Check code compiles (language-specific)\n    # ... implementation-specific checks\n    \n    return $errors\n}\n\n# Run on all agents\nfor agent in a b c; do\n    lint_agent_output \"workspace/run_${agent}\" || exit 1\ndone\n```\n\n### Runtime Assertions\n```python\ndef assert_phase_complete(phase_name):\n    \"\"\"Assert that a phase has completed successfully\"\"\"\n    assertions = {\n        \"phase1\": [\n            \"workspace/run_a/implementation exists\",\n            \"workspace/run_b/implementation exists\", \n            \"workspace/run_c/implementation exists\",\n            \"All Checklist.md are complete\"\n        ],\n        \"phase2\": [\n            \"6 evaluation reports exist\",\n            \"All evaluations have numeric scores\"\n        ],\n        \"phase3\": [\n            \"3 scorecards exist\",\n            \"All scores are numeric\",\n            \"Conclusions are provided\"\n        ],\n        \"phase4\": [\n            \"final/solution exists\",\n            \"COMPARISON_REPORT.md exists\",\n            \"DECISION_RATIONALE.md exists\"\n        ]\n    }\n    \n    for assertion in assertions[phase_name]:\n        assert evaluate(assertion), f\"Assertion failed: {assertion}\"\n```\n\n---\n\n## 6. Configuration\n\n```yaml\nb3ehive:\n  # Agent configuration\n  agents:\n    count: 3\n    model: openai-proxy/gpt-5.3-codex\n    thinking: high\n    focuses:\n      - simplicity\n      - speed\n      - robustness\n  \n  # Evaluation weights (must sum to 100)\n  evaluation:\n    dimensions:\n      simplicity: 20\n      speed: 25\n      stability: 25\n      corner_cases: 20\n      maintainability: 10\n  \n  # Delivery strategy\n  delivery:\n    strategy: auto  # auto / best / hybrid\n    threshold: 15   # Point margin for clear winner\n  \n  # Quality gates\n  quality:\n    lint: true\n    test: true\n    coverage_threshold: 80\n```\n\n---\n\n## 7. Usage\n\n```bash\n# Basic usage\nb3ehive \"Implement a thread-safe rate limiter\"\n\n# With constraints\nb3ehive \"Implement quicksort\" --lang python --max-lines 50\n\n# Using OpenClaw CLI\nopenclaw skills run b3ehive --task \"Your task\"\n```\n\n---\n\n## 8. License\n\nMIT © Weiyang ([@weiyangzen](https://github.com/weiyangzen))\n","readmeExcerpt":"b3ehive Skill Specification PCTF-Compliant Multi-Agent Competition System --- 1. Purpose (PCTF: Purpose) Enable competitive code generation where three isolated AI agents implement the same functionality, evaluate each other objectively, and deliver the optimal solution through data-driven selection. --- 2. 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