Agentdb Learning

/============================================================================/ /* AGENTDB-LEARNING-PLUGINS SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: agentdb-learning-plugins version: 1.0.0 description: | [assert|neutral] Create AI learning plugins using AgentDB's 9 reinforcement learning algorithms. Train Decision Transformer, Q-Learning, SARSA, and Actor-Critic models. Deploy these plugins to build self-learning agen [ground:given] [conf:0.95] [state:confirmed] category: platforms tags:

• platforms
• integration
• tools author: ruv cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute agentdb-learning-plugins workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic platforms processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "agentdb-learning-plugins", category: "platforms", 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: ["agentdb-learning-plugins", "platforms", "workflow"], context: "user needs agentdb-learning-plugins capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

When NOT to Use This Skill

• Local-only operations with no vector search needs
• Simple key-value storage without semantic similarity
• Real-time streaming data without persistence requirements
• Operations that do not require embedding-based retrieval

Success Criteria

• [assert|neutral] Vector search query latency: <10ms for 99th percentile [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Embedding generation: <100ms per document [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Index build time: <1s per 1000 vectors [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Recall@10: >0.95 for similar documents [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Database connection success rate: >99.9% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Memory footprint: <2GB for 1M vectors with quantization [ground:acceptance-criteria] [conf:0.90] [state:provisional]

Edge Cases & Error Handling

• Rate Limits: AgentDB local instances have no rate limits; cloud deployments may vary
• Connection Failures: Implement retry logic with exponential backoff (max 3 retries)
• Index Corruption: Maintain backup indices; rebuild from source if corrupted
• Memory Overflow: Use quantization (4-bit, 8-bit) to reduce memory by 4-32x
• Stale Embeddings: Implement TTL-based refresh for dynamic content
• Dimension Mismatch: Validate embedding dimensions (384 for sentence-transformers) before insertion

Guardrails & Safety

• [assert|emphatic] NEVER: expose database connection strings in logs or error messages [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: validate vector dimensions before insertion [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: sanitize metadata to prevent injection attacks [ground:policy] [conf:0.98] [state:confirmed]
• [assert|emphatic] NEVER: store PII in vector metadata without encryption [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: implement access control for multi-tenant deployments [ground:policy] [conf:0.98] [state:confirmed]
• [assert|neutral] ALWAYS: validate search results before returning to users [ground:policy] [conf:0.98] [state:confirmed]

Evidence-Based Validation

• Verify database health: Check connection status and index integrity
• Validate search quality: Measure recall/precision on test queries
• Monitor performance: Track query latency, throughput, and memory usage
• Test failure recovery: Simulate connection drops and index corruption
• Benchmark improvements: Compare against baseline metrics (e.g., 150x speedup claim)

AgentDB Learning Plugins

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

What This Skill Does

Use this skill to create, train, and deploy learning plugins for autonomous agents using AgentDB's 9 reinforcement learning algorithms. Implement offline RL (Decision Transformer) for safe learning from logged experiences. Apply value-based learning (Q-Learning) for discrete actions. Deploy policy gradients (Actor-Critic) for continuous control. Enable agents to improve through experience with WASM-accelerated neural inference.

Performance: Train models 10-100x faster with WASM-accelerated neural inference.

Prerequisites

• Node.js 18+
• AgentDB v1.0.7+ (via agentic-flow)
• Basic understanding of reinforcement learning (recommended)

Quick Start with CLI

Create Learning Plugin

# Interactive wizard
npx agentdb@latest create-plugin

# Use specific template
npx agentdb@latest create-plugin -t decision-transformer -n my-agent

# Preview without creating
npx agentdb@latest create-plugin -t q-learning --dry-run

# Custom output directory
npx agentdb@latest create-plugin -t actor-critic -o ./plugins

List Available Templates

# Show all 

/*----------------------------------------------------------------------------*/
/* 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/platforms/agentdb-learning-plugins/{project}/{timestamp}",
  store: ["executions", "decisions", "patterns"],
  retrieve: ["similar_tasks", "proven_patterns"]
} [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := {
  WHO: "agentdb-learning-plugins-{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>AGENTDB_LEARNING_PLUGINS_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]