When Optimizing Agent Learning Use Reasoningbank Intelligence

/============================================================================/ /* 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

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] <promise>WHEN_OPTIMIZING_AGENT_LEARNING_USE_REASONINGBANK_INTELLIGENCE_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]