Files
Profa b6fe4abec8 fix: resolve MCP path, stale v3/ refs, CRLF line endings, and rewrite README
- 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>
2026-03-05 15:46:53 +00:00

137 lines
5.3 KiB
JSON

{
"name": "ADR-051 Pattern Memory Store",
"version": "1.0",
"description": "Persistent cross-session learning patterns for Agent Booster, Model Router, ONNX Embeddings, and ReasoningBank",
"created": "2026-01-20",
"namespace_index": {
"adr-051": {
"booster-patterns": {
"file": "adr-051-booster-patterns.json",
"patterns": [
"booster-transform-eligibility",
"booster-batch-optimization",
"booster-wasm-fallback"
],
"description": "Agent Booster WASM transforms and fallback strategies"
},
"router-patterns": {
"file": "adr-051-router-patterns.json",
"patterns": [
"router-5tier-complexity",
"router-budget-enforcement",
"router-booster-integration"
],
"description": "Model Router complexity scoring and budget enforcement"
},
"embedding-patterns": {
"file": "adr-051-embedding-patterns.json",
"patterns": [
"embeddings-local-generation",
"embeddings-lru-cache",
"embeddings-hyperbolic-space",
"embeddings-similarity-metrics"
],
"description": "ONNX embeddings generation, caching, and similarity computation"
},
"reasoning-patterns": {
"file": "adr-051-reasoning-patterns.json",
"patterns": [
"reasoning-trajectory-tracking",
"reasoning-pattern-quality-gates",
"reasoning-experience-replay",
"reasoning-cross-agent-sharing"
],
"description": "ReasoningBank trajectory learning and pattern consolidation"
},
"summary": {
"file": "adr-051-integration-summary.json",
"description": "Complete ADR-051 integration architecture overview"
}
}
},
"pattern_statistics": {
"total_patterns": 15,
"namespaces": 5,
"avg_success_rate": 0.915,
"implementation_status": "Production Ready"
},
"access_patterns": [
{
"pattern_type": "Simple Lookup",
"operation": "Load specific pattern by key",
"time_complexity": "O(1)",
"use_case": "Quick eligibility checks, cache lookup"
},
{
"pattern_type": "Namespace Query",
"operation": "Load all patterns in namespace",
"time_complexity": "O(n) where n=patterns per namespace",
"use_case": "Multi-pattern decision making, agent coordination"
},
{
"pattern_type": "Semantic Search",
"operation": "Find patterns by similarity",
"time_complexity": "O(n) with embeddings",
"use_case": "Pattern discovery, cross-session learning"
}
],
"session_initialization": {
"steps": [
"Load index.json to discover available patterns",
"Initialize embedding cache from embeddings-patterns",
"Load router configuration from router-patterns",
"Initialize booster eligibility rules from booster-patterns",
"Setup ReasoningBank trajectory database from reasoning-patterns",
"Configure quality gates and thresholds",
"Register pattern sharing channels"
],
"initialization_time": "<100ms"
},
"cross_session_learning": {
"enabled": true,
"persistence_layer": "JSON files in .agentic-qe/patterns/",
"synchronization": "File-based with timestamps",
"conflict_resolution": "Latest timestamp wins",
"archival_strategy": "Keep 30-day history in patterns-archive/",
"memory_coordination": {
"agent_communication": "Via shared pattern namespace",
"knowledge_sharing": "Gossip protocol simulation via file watching",
"learning_integration": "Apply new patterns on each session start"
}
},
"usage_examples": {
"example_1_booster_check": {
"query": "Is 'var-to-const' transform eligible for Agent Booster?",
"lookup": "patterns['adr-051/booster-patterns']['booster-transform-eligibility'].eligibility_criteria.simple_transforms",
"result": "YES - found in eligibility list"
},
"example_2_router_selection": {
"query": "Route a 5-file refactoring task",
"lookup": "patterns['adr-051/router-patterns']['router-5tier-complexity'].scoring_factors",
"result": "Complexity score 6 → Route to Sonnet tier"
},
"example_3_embedding_cache": {
"query": "Check embedding cache for similarity query",
"lookup": "patterns['adr-051/embedding-patterns']['embeddings-lru-cache'].cache_key_strategy",
"result": "Check SHA256(text) in cache, 70% expected hit rate"
},
"example_4_pattern_quality": {
"query": "Is new trajectory eligible for ReasoningBank?",
"lookup": "patterns['adr-051/reasoning-patterns']['reasoning-pattern-quality-gates'].gate_thresholds",
"result": "Check composite score >= 0.80 for silver tier"
}
},
"maintenance": {
"review_frequency": "Monthly",
"update_triggers": [
"New successful pattern discovered",
"Pattern success rate drops below threshold",
"New component integration requires patterns",
"Performance optimization identified"
],
"archival_directory": ".agentic-qe/patterns-archive/",
"rollback_capability": "Restore from archive by date"
},
"notes": "This index enables fast pattern discovery and multi-session learning without requiring external MCP tools or CLI. Patterns are loaded on session initialization and used throughout agent coordination."
}