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Profa
b6fe4abec8
- Fix MCP entry path resolution in mcp.ts (1 level up, not 2) - Remove stale v3/ directory references in 10-workers.ts - Fix CRLF line endings in 170+ skill files breaking frontmatter parsing - Add CRLF→LF safety net in prepare-assets.sh - Rewrite README.md for clarity (1097→280 lines, outcome-focused) - Sync assets from .claude/skills via prepare-assets.sh Fixes issues reported in v3.7.9 upgrade (skill-lint failures, MCP errors) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
167 lines
6.9 KiB
JSON
167 lines
6.9 KiB
JSON
{
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"namespace": "adr-051/reasoning-patterns",
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"description": "ReasoningBank Integration Patterns from ADR-051",
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"created": "2026-01-20",
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"patterns": [
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{
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"key": "reasoning-trajectory-tracking",
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"pattern": "ReasoningBank Trajectory Tracking",
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"description": "Track decision-making trajectories for learning and replay",
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"trajectory_structure": {
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"trajectory_id": "Unique identifier for sequence",
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"task_description": "Original task prompt",
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"steps": [
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{
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"step_number": 1,
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"action": "Initial assessment",
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"result": "Identified key issues",
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"quality": 0.8
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},
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{
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"step_number": 2,
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"action": "Proposed solution",
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"result": "Specific implementation",
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"quality": 0.9
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}
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],
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"final_outcome": "Success/Failure with metrics",
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"completion_time": "Wall-clock execution time"
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},
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"tracking_mechanism": {
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"event_capture": "Record decision at each step",
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"context_preservation": "Store relevant state",
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"outcome_recording": "Final result and quality score",
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"metadata_annotation": "Tags, categories, keywords"
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},
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"learning_extraction": {
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"success_patterns": "Extract what worked well",
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"failure_analysis": "Understand what failed and why",
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"performance_correlation": "Link actions to outcomes",
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"decision_confidence": "Track decision quality signals"
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},
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"trajectory_replay": {
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"similar_task_detection": "Find analogous trajectories",
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"experience_adaptation": "Adjust for current context",
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"outcome_prediction": "Forecast success probability",
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"strategy_transfer": "Apply learned strategies"
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},
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"successRate": 0.91,
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"lastUpdated": "2026-01-20"
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},
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{
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"key": "reasoning-pattern-quality-gates",
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"pattern": "ReasoningBank Pattern Quality Gates",
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"description": "Ensure high-quality patterns enter ReasoningBank with quality gates",
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"quality_criteria": {
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"success_rate": ">0.85 for pattern approval",
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"outcome_consistency": "Low variance in results",
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"generalizability": "Works across multiple contexts",
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"edge_case_coverage": "Handles variations well",
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"documentation": "Clear explanation of pattern"
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},
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"quality_scoring": {
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"dimension_1_accuracy": "How often pattern produces correct results",
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"dimension_2_robustness": "Handles edge cases and variations",
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"dimension_3_efficiency": "Computational cost vs accuracy tradeoff",
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"dimension_4_clarity": "How understandable and transferable",
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"composite_score": "Weighted average of dimensions"
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},
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"gate_thresholds": {
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"bronze_tier": ">0.70 composite score - Limited recommendation",
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"silver_tier": ">0.80 composite score - Standard recommendation",
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"gold_tier": ">0.90 composite score - Highly recommended",
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"platinum_tier": ">0.95 composite score - Approved for critical tasks"
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},
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"pattern_validation": [
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"Run against test suite",
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"Cross-validation on held-out data",
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"Edge case stress testing",
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"Peer review by agents",
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"Historical performance comparison"
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],
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"rejection_reasons": [
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"Insufficient success rate",
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"High variance in outcomes",
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"Narrow applicability",
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"Poor documentation",
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"Security or safety concerns"
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],
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"successRate": 0.94,
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"lastUpdated": "2026-01-20"
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},
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{
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"key": "reasoning-experience-replay",
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"pattern": "Experience Replay for Continuous Learning",
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"description": "Replay past trajectories to reinforce successful patterns and prevent catastrophic forgetting",
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"replay_mechanism": {
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"sample_trajectory": "Randomly sample from trajectory buffer",
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"extract_experience": "Get action-outcome pairs",
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"analyze_decision": "Understand why that action succeeded",
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"reinforce_pattern": "Strengthen association in memory",
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"prevent_forgetting": "Consolidate knowledge with EWC++"
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},
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"buffer_management": {
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"trajectory_buffer_size": "100-1000 trajectories",
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"prioritized_sampling": "Over-sample high-value trajectories",
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"diversity_sampling": "Ensure coverage of different task types",
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"staleness_threshold": "Drop trajectories older than N days",
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"checkpointing": "Periodically save important trajectories"
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},
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"learning_integration": {
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"offline_replay": "Run during idle time",
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"online_replay": "Interleave with new experiences",
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"consolidation": "EWC++ protects old knowledge",
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"transfer_learning": "Apply patterns to new tasks"
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},
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"metrics_tracking": {
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"replay_frequency": "How often each trajectory replayed",
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"learning_gain": "Improvement from replay",
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"forgetting_rate": "How fast knowledge decays",
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"transfer_success": "Patterns working on new tasks"
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},
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"scaling_strategy": "Distributed replay across agent swarm",
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"successRate": 0.89,
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"lastUpdated": "2026-01-20"
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},
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{
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"key": "reasoning-cross-agent-sharing",
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"pattern": "Cross-Agent Knowledge Sharing",
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"description": "Share learned patterns and trajectories between agents for collective intelligence",
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"sharing_channels": {
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"pattern_broadcast": "Share high-quality patterns to all agents",
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"trajectory_publishing": "Publish successful trajectories",
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"failure_alerts": "Warn about patterns that failed",
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"performance_updates": "Share metric improvements"
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},
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"knowledge_transfer": {
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"sender_agent": "Identifies pattern worth sharing",
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"quality_assessment": "Verify pattern meets quality gates",
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"packaging": "Package with metadata and context",
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"distribution": "Broadcast to relevant agent types",
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"integration": "Receiving agents incorporate pattern"
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},
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"recipient_adaptation": {
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"context_adjustment": "Adapt pattern to local context",
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"validation": "Test pattern in new environment",
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"confidence_tracking": "Monitor transfer success rate",
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"feedback_loop": "Report back on effectiveness"
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},
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"knowledge_domains": [
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"Test generation strategies",
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"Code analysis patterns",
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"Error diagnosis techniques",
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"Optimization approaches",
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"Security checking methods"
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],
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"conflict_resolution": {
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"version_mismatch": "Prefer newer patterns with higher success rate",
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"contradictory_patterns": "Run both and compare empirically",
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"domain_specificity": "Context-weight patterns by relevance"
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},
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"scaling_approach": "Gossip protocol for decentralized propagation",
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"successRate": 0.87,
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"lastUpdated": "2026-01-20"
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}
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]
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}
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