# When Optimizing Agent Learning Use Reasoningbank Intelligence > When Optimizing Agent Learning Use Reasoningbank Intelligenc - Skill: `dnyoussef/when-optimizing-agent-learning-use-reasoningbank-intelligenc` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-optimizing-agent-learning-use-reasoningbank-intelligenc` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-optimizing-agent-learning-use-reasoningbank-intelligenc/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-optimizing-agent-learning-use-reasoningbank-intelligenc --- /*============================================================================*/ /* WHEN-OPTIMIZING-AGENT-LEARNING-USE-REASONINGBANK-INTELLIGENCE SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: when-optimizing-agent-learning-use-reasoningbank-intelligence version: 1.0.0 description: | [assert|neutral] Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement [ground:given] [conf:0.95] [state:confirmed] category: utilities tags: - machine-learning - adaptive-learning - pattern-recognition - optimization author: ruv cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute when-optimizing-agent-learning-use-reasoningbank-intelligence workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic utilities processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "when-optimizing-agent-learning-use-reasoningbank-intelligence", category: "utilities", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Aspectual", source: "Russian", force: "Complete or ongoing?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["when-optimizing-agent-learning-use-reasoningbank-intelligence", "utilities", "workflow"], context: "user needs when-optimizing-agent-learning-use-reasoningbank-intelligence capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ ## When to Use This Skill - **Tool Usage**: When you need to execute specific tools, lookup reference materials, or run automation pipelines - **Reference Lookup**: When you need to access documented patterns, best practices, or technical specifications - **Automation Needs**: When you need to run standardized workflows or pipeline processes ## When NOT to Use This Skill - **Manual Processes**: Avoid when manual intervention is more appropriate than automated tools - **Non-Standard Tools**: Do not use when tools are deprecated, unsupported, or outside standard toolkit ## Success Criteria - [assert|neutral] *Tool Executed Correctly**: Verify tool runs without errors and produces expected output [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] *Reference Accurate**: Confirm reference material is current and applicable [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] *Pipeline Complete**: Ensure automation pipeline completes all stages successfully [ground:acceptance-criteria] [conf:0.90] [state:provisional] ## Edge Cases - **Tool Unavailable**: Handle scenarios where required tool is not installed or accessible - **Outdated References**: Detect when reference material is obsolete or superseded - **Pipeline Failures**: Recover gracefully from mid-pipeline failures with clear error messages ## Guardrails - [assert|emphatic] NEVER: use deprecated tools**: Always verify tool versions and support status before execution [ground:policy] [conf:0.98] [state:confirmed] - [assert|neutral] ALWAYS: verify outputs**: Validate tool outputs match expected format and content [ground:policy] [conf:0.98] [state:confirmed] - [assert|neutral] ALWAYS: check health**: Run tool health checks before critical operations [ground:policy] [conf:0.98] [state:confirmed] ## Evidence-Based Validation - **Tool Health Checks**: Execute diagnostic commands to verify tool functionality before use - **Output Validation**: Compare actual outputs against expected schemas or patterns - **Pipeline Monitoring**: Track pipeline execution metrics and success rates # ReasoningBank Intelligence - Adaptive Agent Learning ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## Overview Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing decision-making, or implementing meta-cognitive systems. ## When to Use - Agent performance needs improvement - Repetitive tasks require optimization - Need pattern recognition from experience - Strategy refinement through learning - Building self-improving systems - Meta-cognitive capabilities needed ## Theoretical Foundation ### ReasoningBank Architecture 1. **Trajectory Tracking**: Record decision paths and outcomes 2. **Verdict Judgment**: Evaluate success/failure of strategies 3. **Memory Distillation**: Extract patterns from experience 4. **Pattern Recognition**: Identify successful approaches 5. **Strategy Optimization**: Apply learned patterns to new situations ### AgentDB Integration (Optional) - 150x faster vector operations - HNSW indexing for similarity search - Quantization for memory efficiency - Batch operations for performance ## Phase 1: Initialize Learning System (10 min) ### Objective Set up ReasoningBank with trajectory tracking ### Agent: ML-Developer **Step 1.1: Initialize ReasoningBank** ```javascript const ReasoningBank = require('reasoningbank'); const learningSystem = new ReasoningBank({ storage: { type: 'agentdb', // Or 'memory', 'disk' path: './reasoning-bank-data', quantization: 'int8' // 4-32x memory reduction }, indexing: { enabled: true, type: 'hnsw', // 150x faster search dimensions: 768 }, learning: { algorithm: 'decision-transformer', learningRate: 0.001, batchSize: 32 } }); await learningSystem.init(); await memory.store('reaso /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/utilities/when-optimizing-agent-learning-use-reasoningbank-intelligence/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "when-optimizing-agent-learning-use-reasoningbank-intelligence-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] WHEN_OPTIMIZING_AGENT_LEARNING_USE_REASONINGBANK_INTELLIGENCE_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]