# When Implementing Adaptive Learning Use Reasoningbank Agentdb > When Implementing Adaptive Learning Use Reasoningbank Agentd - Skill: `dnyoussef/when-implementing-adaptive-learning-use-reasoningbank-agentd` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-implementing-adaptive-learning-use-reasoningbank-agentd` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-implementing-adaptive-learning-use-reasoningbank-agentd/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-implementing-adaptive-learning-use-reasoningbank-agentd --- /*============================================================================*/ /* REASONINGBANK ADAPTIVE LEARNING WITH AGENTDB SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: ReasoningBank Adaptive Learning with AgentDB version: 1.0.0 description: | [assert|neutral] ReasoningBank Adaptive Learning with AgentDB skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags: - general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute ReasoningBank Adaptive Learning with AgentDB workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "ReasoningBank Adaptive Learning with AgentDB", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [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: ["ReasoningBank Adaptive Learning with AgentDB", "agentdb", "workflow"], context: "user needs ReasoningBank Adaptive Learning with AgentDB capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # ReasoningBank Adaptive Learning with AgentDB ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## Overview Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database for trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Build self-learning agents that improve decision-making through experience. ## SOP Framework: 5-Phase Adaptive Learning ### Phase 1: Initialize ReasoningBank (1-2 hours) - Setup AgentDB with ReasoningBank - Configure trajectory tracking - Initialize verdict system ### Phase 2: Track Trajectories (2-3 hours) - Record agent decisions - Store reasoning paths - Capture context and outcomes ### Phase 3: Judge Verdicts (2-3 hours) - Evaluate decision quality - Score reasoning paths - Identify successful patterns ### Phase 4: Distill Memory (2-3 hours) - Extract learned patterns - Consolidate successful strategies - Prune ineffective approaches ### Phase 5: Apply Learning (1-2 hours) - Use learned patterns in decisions - Improve future reasoning - Measure improvement ## Quick Start ```typescript import { AgentDB, ReasoningBank } from 'reasoningbank-agentdb'; // Initialize const db = new AgentDB({ name: 'reasoning-db', dimensions: 768, features: { reasoningBank: true } }); const reasoningBank = new ReasoningBank({ database: db, trajectoryWindow: 1000, verdictThreshold: 0.7 }); // Track trajectory await reasoningBank.trackTrajectory({ agent: 'agent-1', decision: 'action-A', reasoning: 'Because X and Y', context: { state: currentState }, timestamp: Date.now() }); // Judge verdict const verdict = await reasoningBank.judgeVerdict({ trajectory: trajectoryId, outcome: { success: true, reward: 10 }, criteria: ['efficiency', 'correctness'] }); // Learn patterns const patterns = await reasoningBank.distillPatterns({ minSupport: 0.1, confidence: 0.8 }); // Apply learning const decision = await reasoningBank.makeDecision({ context: currentContext, useLearned: true }); ``` ## ReasoningBank Components ### Trajectory Tracking ```typescript const trajectory = { agent: 'agent-1', steps: [ { state: s0, action: a0, reasoning: r0 }, { state: s1, action: a1, reasoning: r1 } ], outcome: { success: true, reward: 10 } }; await reasoningBank.storeTrajectory(trajectory); ``` ### Verdict Judgment ```typescript const verdict = await reasoningBank.judge({ trajectory: trajectory, criteria: { efficiency: 0.8, correctness: 0.9, novelty: 0.6 } }); ``` ### Memory Distillation ```typescript const distilled = await reasoningBank.distill({ trajectories: recentTrajectories, method: 'pattern-mining', compression: 0.1 // Keep top 10% }); ``` ### Pattern Application ```typescript const enhanced = await reasoningBank.enhance({ query: newProblem, patterns: learnedPatterns, strategy: 'case-based' }); ``` ## Success Metrics - [assert|neutral] Trajectory tracking accuracy > 95% [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Verdict judgment accuracy > 90% [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Pattern learning efficiency [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Decision quality improvement over time [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] 150x faster than traditional approaches [ground:acceptance-criteria] [conf:0.90] [state:provisional] ## MCP Requirements This skill operates using AgentDB's npm package and API only. No additional MCP servers required. All AgentDB/ReasoningBank operations are performed through: - npm CLI: `npx agentdb@latest` - TypeScript/JavaScript API: `import { AgentDB, ReasoningBank } from 'reasoningbank-agentdb'` ## Additional Resources - Full docs: SKILL.md - ReasoningBank Guide: https://reasoningbank.dev - AgentDB Integration: https://agentdb.dev/docs/reasoningbank --- ## Core Principl /*----------------------------------------------------------------------------*/ /* 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/agentdb/ReasoningBank Adaptive Learning with AgentDB/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "ReasoningBank Adaptive Learning with AgentDB-{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] REASONINGBANK ADAPTIVE LEARNING WITH AGENTDB_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]