Agent skills by ruvnet

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

Initialize 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…

TDD London School workflow -- mock-first, outside-in test development

Detect 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.

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)

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

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

Detect 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…

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

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

Cluster code by graph community detection via npx [email protected] hooks graph-cluster (spectral / Louvain)

Generate embeddings via npx [email protected] embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index

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

Vector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reduction

First-run setup for [email protected] — installs ONNX/Brain/SONA add-ons, registers the MCP server, and verifies the install via `doctor`

Create and manage sandboxed WASM agents for isolated code execution

Browse, publish, and install WASM agents from the community gallery

Sign, verify, and track fix-marker regressions over time using a deterministic Ed25519 witness manifest. Works in any project — clone the toolkit, run init, register fixes, regen on each release.

Author a workflow — either an MCP workflow template (persisted, lifecycle) or a native .claude/workflows/*.js orchestration script (agent/parallel/pipeline fan-out)