Claude Skills by ruvnet

Build a graph-structured dossier on a seed entity via parallel fan-out + recursive expansion across web, memory, knowledge-graph, codebase, ADR index, and git intel

Create and execute Goal-Oriented Action Plans (GOAP) with precondition analysis, cost optimization, and adaptive replanning

Track long-horizon objectives across multiple sessions with milestone checkpoints, progress persistence, and drift detection

Synthesize research findings from memory into structured reports with evidence grading, contradiction resolution, and actionable recommendations

Route tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooks_explain

Publish or fetch learned patterns across projects via IPFS (Pinata) -- the cross-project pattern transfer that hooks_transfer enables

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

Detect and classify telemetry anomalies on Cognitum Seed devices

Orchestrate firmware rollouts with canary deployment and anomaly-gated advancement

Create and manage Cognitum Seed device fleets with firmware policies

Register a Cognitum Seed device by endpoint and establish agent bridge

Verify witness chain integrity and detect provenance gaps

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

Advanced git workflows with branch management, conflict resolution, and PR lifecycle

Extract entities and relations from source files to build a knowledge graph

Pathfinder traversal of the knowledge graph starting from a seed entity

Schedule persistent background workers via CronCreate

Run Ruflo background workers using Claude Code native /loop scheduling

Ingest and normalize market data into OHLCV vectors with HNSW indexing

Detect and classify candlestick patterns from ingested OHLCV data

Create a new sequentially numbered database migration with up/down SQL files

Validate pending migrations for foreign key consistency, rollback safety, and best practices

Run 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)

Run 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

Regulator-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)

Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)

Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan

Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy

Assess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status

Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction

Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals

Aggregate and display system metrics with anomaly detection for a time period

Trace agent execution by collecting spans and building a trace tree for a task

Scaffold a new Claude Code plugin with proper directory structure, plugin.json, skills, commands, and agents

Validate a Claude Code plugin structure, frontmatter, and MCP tool references

Bridge Claude Code auto-memory into AgentDB with ONNX embeddings, deduplicate, and enable unified cross-project search

SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting

Cluster code by graph community detection via npx ruvector@0.2.25 hooks graph-cluster (spectral / Louvain)

Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index

Embed hierarchical data via npx ruvector@0.2.25 embed text and project into the Poincare ball in user code (no --model poincare flag in 0.2.25)

First-run setup for ruvector@0.2.25 — installs ONNX/Brain/SONA add-ons, registers the MCP server, and verifies the install via `doctor`

Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval

Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation

Manage RVF (Ruflo Vector Format) files for portable agent memory and cross-platform transfer

Persist and restore agent sessions across conversations with state snapshots

Scan project dependencies for known vulnerabilities and CVEs

Run full security scans on the codebase using Ruflo security tools

Run the SPARC Architecture and Implementation phases — design module boundaries, write pseudocode, implement code, and run tests

Run the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation

Run the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory