ruflo

Core Ruflo MCP tools, commands, and Claude Code orchestration patterns

Agent teams, swarm coordination, Monitor streams, and worktree isolation

Cache-aware /loop workers and CronCreate background automation

Security review, dependency scanning, policy gates, and CVE monitoring

RuVector memory with HNSW search, AgentDB, and semantic retrieval

Test gap detection, coverage analysis, and automated test generation

Documentation generation, drift detection, and API docs automation

Autonomous /loop-driven task completion with learning, prediction, and progress tracking

Self-learning neural intelligence with SONA patterns, trajectory learning, and model routing

AgentDB memory controllers with HNSW vector search, RuVector embeddings, and causal graphs

AI safety scanning, PII detection, prompt injection defense, and adaptive threat learning

Agentic browser automation with Playwright for testing, scraping, and UI interaction

Advanced git workflows with diff analysis, risk scoring, and reviewer recommendations

Agent runtimes — local WASM-sandboxed agents (rvagent) + Anthropic Claude Managed Agents (cloud); one interface, local-vs-cloud backends

Visual workflow automation with templates, orchestration, and lifecycle management

Dynamic Agentic Architecture with cognitive patterns, knowledge sharing, and adaptive agents

RuVLLM local inference with chat formatting, MicroLoRA fine-tuning, and SONA adaptation

RVF format for portable agent memory, session persistence, and cross-platform transfer

Scaffold, validate, and publish new Claude Code plugins with proper structure

Long-horizon goal planning, deep research orchestration, and adaptive replanning using GOAP

ADR lifecycle management — create, index, supersede, and link Architecture Decision Records to code

Token usage tracking, model cost attribution per agent, budget alerts, and optimization recommendations

Domain-Driven Design scaffolding — bounded contexts, aggregate roots, domain events, and anti-corruption layers

Cross-installation agent federation with zero-trust security, PII-gated data flow, and compliance-grade audit trails

Real-time graph intelligence — personalized PageRank, streaming delta updates, witness-signed reasoning, and federation-distributable vectors

IoT device lifecycle, telemetry anomaly detection, fleet management, and witness chain verification for Cognitum Seed hardware

Knowledge graph construction — entity extraction, relation mapping, and pathfinder graph traversal

Market data ingestion — feed normalization, OHLCV vectorization, and HNSW-indexed pattern matching

Schema migration management — generate, validate, dry-run, and rollback database migrations

Neural trading strategies — self-learning LSTM/Transformer/N-BEATS models with Rust/NAPI backtesting

Structured logging, distributed tracing, and metrics — correlate agent swarm activity with application telemetry

Self-learning vector database — HNSW, FlashAttention-3, Graph RAG, hybrid search, DiskANN, and Brain AGI

SPARC methodology — Specification, Pseudocode, Architecture, Refinement, Completion phases with quality gates