Pathfinder traversal of the knowledge graph starting from a seed entity
原文の言語: 英語
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このリポジトリの skills
SkillsMP は ruvnet/ruflo から 310 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
ruvnet/ruflo収集済み skill 310 件中 40 件を表示しています。
Pathfinder traversal of the knowledge graph starting from a seed entity
原文の言語: 英語
Schedule persistent background workers via CronCreate
原文の言語: 英語
Run Ruflo background workers using Claude Code native /loop scheduling
原文の言語: 英語
Ingest and normalize market data into OHLCV vectors with HNSW indexing
原文の言語: 英語
Detect and classify candlestick patterns from ingested OHLCV data
原文の言語: 英語
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…
原文の言語: 英語
Create a new sequentially numbered database migration with up/down SQL files
原文の言語: 英語
Validate pending migrations for foreign key consistency, rollback safety, and best practices
原文の言語: 英語
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)
原文の言語: 英語
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
原文の言語: 英語
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)
原文の言語: 英語
Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
原文の言語: 英語
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
原文の言語: 英語
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…
原文の言語: 英語
Assess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status
原文の言語: 英語
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
原文の言語: 英語
Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
原文の言語: 英語
Aggregate and display system metrics with anomaly detection for a time period
原文の言語: 英語
Trace agent execution by collecting spans and building a trace tree for a task
原文の言語: 英語
Scaffold a new Claude Code plugin with proper directory structure, plugin.json, skills, commands, and agents
原文の言語: 英語
Validate a Claude Code plugin structure, frontmatter, and MCP tool references
原文の言語: 英語
Bridge Claude Code auto-memory into AgentDB with ONNX embeddings, deduplicate, and enable unified cross-project search
原文の言語: 英語
SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
原文の言語: 英語
Cluster code by graph community detection via npx ruvector@0.2.25 hooks graph-cluster (spectral / Louvain)
原文の言語: 英語
Generate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
原文の言語: 英語
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)
原文の言語: 英語
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
原文の言語: 英語
Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation
原文の言語: 英語
Manage RVF (Ruflo Vector Format) files for portable agent memory and cross-platform transfer
原文の言語: 英語
Persist and restore agent sessions across conversations with state snapshots
原文の言語: 英語
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.
原文の言語: 英語
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.
原文の言語: 英語
Run the SPARC Pseudocode and Architecture phases (2 and 3) — write algorithm pseudocode, design module boundaries and API contracts, then implement
原文の言語: 英語
Run the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation
原文の言語: 英語
Run the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory
原文の言語: 英語
Stream live swarm events using the Monitor tool for real-time observability
原文の言語: 英語
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.
原文の言語: 英語
TDD London School workflow -- mock-first, outside-in test development
原文の言語: 英語
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.
原文の言語: 英語
Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap
原文の言語: 英語