Skills

18,283 skills, most installed first.

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iot-registerruvnetRegister a Cognitum Seed device by endpoint and establish agent bridge iot-witness-verifyruvnetVerify witness chain integrity and detect provenance gaps kg-extractruvnetExtract entities and relations from source files to build a knowledge graph kg-traverseruvnetPathfinder traversal of the knowledge graph starting from a seed entity llm-configruvnetConfigure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation loop-workerruvnetRun Ruflo background workers using Claude Code native /loop scheduling managed-agentruvnetRun an Anthropic Claude Managed Agent — a cloud agent harness (container + filesystem + tools), the cloud counterpart of the local wasm-agent runtime market-ingestruvnetIngest and normalize market data into OHLCV vectors with HNSW indexing market-patternruvnetDetect and classify candlestick patterns from ingested OHLCV data memory-bridgeruvnetBridge Claude Code auto-memory into AgentDB with ONNX embeddings, deduplicate, and enable unified cross-project search memory-managementruvnetAgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.memory-searchruvnetSOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting migrate-createruvnetCreate a new sequentially numbered database migration with up/down SQL files migrate-validateruvnetValidate pending migrations for foreign key consistency, rollback safety, and best practices monitor-streamruvnetStream live swarm events using the Monitor tool for real-time observability neural-trainruvnetTrain SONA + MicroLoRA neural patterns from successful task completions; runs the DISTILL + CONSOLIDATE phases of the 4-step pipeline neural-trainingruvnetNeural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation. Use when: pattern learning, model optimization, knowledge transfer, adaptive routing. Skip when: simple tasks, no learning required, one-off operations.observe-metricsruvnetAggregate and display system metrics with anomaly detection for a time period observe-traceruvnetTrace agent execution by collecting spans and building a trace tree for a task Pair ProgrammingruvnetAI-assisted pair programming with multiple modes (driver$navigator$switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.performance-analysisruvnetComprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms pii-detectruvnetDetect and flag personally identifiable information (PII) in text, code, and configurations. Use before committing code, writing logs, storing data, or sending model responses that might contain emails, phone numbers, SSNs, API keys, or passwords.ReasoningBank IntelligenceruvnetImplement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.ReasoningBank with AgentDBruvnetImplement 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.research-synthesizeruvnetSynthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations ruflo-doctorruvnetRun health checks on the Ruflo installation and fix common issues rvf-manageruvnetManage RVF (Ruflo Vector Format) files for portable agent memory and cross-platform transfer safety-scanruvnetScan inputs for prompt injection, unsafe content, and adversarial attacks using AIDefence. Use when processing untrusted input (user submissions, API payloads, webhook data, tool outputs) before passing it to a model or executing it.security-scanruvnetRun full security scans on the codebase using Ruflo security tools. Use when reviewing PRs for security regressions, auditing auth/input-handling code, before production deploys, or when the user asks for a security check at quick/standard/deep depth.session-persistruvnetPersist and restore agent sessions across conversations with state snapshots Skill BuilderruvnetCreate new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.sparc-implementruvnetRun the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement sparc-methodologyruvnetSPARC development workflow: Specification, Pseudocode, Architecture, Refinement, Completion. A structured approach for complex implementations that ensures thorough planning before coding. Use when: new feature implementation, complex implementations, architectural changes, system redesign, integration work, unclear requirements. Skip when: simple bug fixes, documentation updates, configuration changes, well-defined small tasks, routine maintenance.sparc-refineruvnetRun the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation sparc-specruvnetRun the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory stream-chainruvnetStream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows swarm-advancedruvnetAdvanced swarm orchestration patterns for research, development, testing, and complex distributed workflows swarm-initruvnetInitialize a multi-agent swarm with anti-drift configuration. Use when starting a complex multi-file task that needs 3+ coordinated agents (feature implementation, refactor across modules, security audit). Skip for single-file edits or quick questions.tdd-workflowruvnetTDD London School workflow -- mock-first, outside-in test development test-gapsruvnetDetect missing test coverage and generate test suggestions. Use when the user asks about coverage gaps, untested code, or what tests to write next; also after adding a feature to find what still needs tests.trader-backtestruvnetRun a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation; Ed25519-sign the result for paper→live tamper evidence (ADR-126 Phase 4)trader-cloud-backtestruvnetRun a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally trader-explainruvnetRegulator-grade feature attribution for any LSTM/Transformer signal — single-entry PageRank ranks the top-K features that drove the prediction (ADR-126 Phase 6, ADR-123 single-entry PR)trader-portfolioruvnetOptimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan trader-portfolio-cgruvnetMean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)trader-regimeruvnetDetect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy. Use when the user asks about market conditions, wants to pick a strategy for current conditions, or before running a backtest/signal that should be regime-aware.trader-riskruvnetAssess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status trader-signalruvnetGenerate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction trader-trainruvnetTrain neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals V3 CLI ModernizationruvnetCLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.