Agentdb

/============================================================================/ /* AGENTDB SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: agentdb version: 1.0.0 description: | [assert|neutral] High-performance vector search and semantic memory for AI agents. Use when implementing RAG systems, semantic document retrieval, or persistent agent memory. Provides 150x faster vector search vs trad [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 workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic platforms processes"

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

[define|neutral] SKILL := { name: "agentdb", 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", "platforms", "workflow"], context: "user needs agentdb 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 - Vector Search & Semantic Memory

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Ultra-fast vector database for AI agent memory, RAG systems, and semantic search applications.

When to Use This Skill

Use when implementing retrieval-augmented generation (RAG), building semantic search engines, creating persistent agent memory systems, or optimizing vector similarity searches for production workloads.

Core Capabilities

Vector Search

• 150x faster than traditional databases
• HNSW (Hierarchical Navigable Small World) indexing
• 384-dimensional sentence embeddings
• Sub-millisecond query latency

Semantic Memory

• Persistent cross-session storage
• Automatic embedding generation
• Similarity-based retrieval
• Metadata filtering and ranking

Memory Patterns

• Short-term: Recent context (1-100 items)
• Long-term: Persistent knowledge (unlimited)
• Episodic: Timestamped experiences
• Semantic: Concept relationships

Process

1. Initialize vector store

   • Configure embedding model (sentence-transformers)
   • Set up HNSW index parameters
   • Define metadata schema
   • Allocate storage backend

2. Store information

   • Generate embeddings automatically

/----------------------------------------------------------------------------/ /* 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/{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-{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] AGENTDB_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]