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Build or rebuild the ADR index + dependency graph in AgentDB by running the in-process `agentdb index` command (one cold-start, all surfaces in one pass — no per-record npx round-trips). Handles v3-style and plugin-style ADR formats.

Create a new Architecture Decision Record with sequential numbering and AgentDB registration

Hive Mind orchestration patterns — queen-led multi-agent coordination with Byzantine/Raft/Gossip/CRDT consensus, typed collective memory, dialectic council, and session checkpoint/resume. Use for decision-bearing work; use swarm-advanced for parallel execution without consensus.

Analyze git diffs for risk scoring, reviewer recommendations, and change classification

Detect missing test coverage and generate test suggestions

Hive Mind orchestration patterns — queen-led multi-agent coordination with Byzantine/Raft/Gossip/CRDT consensus, typed collective memory, dialectic council, and session checkpoint/resume. Use for decision-bearing work; use swarm-advanced for parallel execution without consensus.

Transfer learned patterns from another project on disk into this one -- the project-to-project pattern transfer that hooks_transfer performs

Train SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

Web browser automation with AI-optimized snapshots for Codex-flow agents

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

Comprehensive GitHub code review with AI-powered swarm coordination

Convene a dialectic expert-panel review (the Linked Data Council Hive) on an ontology/ADR/ODR proposition or a set of outstanding questions, then produce a session record. Use when a modelling decision has a credible split, a contested mapping or identity-criterion question needs adjudicating, or a precedent needs recording. Spawns a SCOPED panel via the Agent tool (Queen + Devil's Advocate + only the relevant experts), collects per-question verdicts, writes the synthesis, and routes dispositions to the ODRs/ADRs.

Create professional diagrams using Mermaid or DOT/Graphviz. Mermaid for flowcharts, sequences, classes, ER, Gantt, architecture with semantic coloring and WCAG AA accessibility. DOT/Graphviz for pure network graphs, semantic webs, and maximum layout control.

Lint ODR frontmatter, section structure, and references against the DCAP profile

Create a new Ontology Decision Record (ODR) with sequential numbering and AgentDB registration

Build or rebuild the ODR index and dependency graph in AgentDB (strict mode — unified MADR DCAP: 6 required + 3 optional frontmatter keys, MADR body spine, matching odr-create / odr-review)

Create a new Architecture Decision Record with sequential numbering (ADR- prefixed filename) and AgentDB registration

Build or rebuild the ADR index and dependency graph in AgentDB (strict mode — Phase 3 corpus, ADR- prefix filenames, no legacy fallbacks)

Export Mermaid diagrams from documents to PNG images. MUST be run immediately after creating or editing any Mermaid diagram, BEFORE committing. Processes .md, .html, .mdx, .rst, .adoc files.

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.