Pathfinder traversal of the knowledge graph starting from a seed entity
Skills in this repository
ruvnet/ruflo - Page 3
SkillsMP has collected 310 skills from ruvnet/ruflo. Open a skill to review its source and details.
ruvnet/rufloShowing 40 of 310 collected skills.
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
ADR-152 — weighted similarity between two harness fingerprints (genome + score JSON). Returns overall score in [0,1] plus per-component breakdown (cosine over 9 numerics, categorical agreement over 4 enums, jaccard over agent_topology). Unblocks ADR-151 §3.2…
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. Use when the user asks about market conditions, wants to pick a strategy for current conditions, or before running a backtest/signal…
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)
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. Use when auditing third-party packages, before releases, after `npm install`/lockfile changes, or when investigating reported CVE advisories.
Run 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.
Run the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement
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
Stream live swarm events using the Monitor tool for real-time observability
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 single-file edits or quick questions.
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.
Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap