By edisplay
Workflow automation across two surfaces: the 10 workflow_* MCP tools (create/run/execute/status/list/pause/resume/cancel/delete/template) with full state-machine lifecycle (created → running ↔ paused → completed/cancelled), and native Claude Code Workflow JS orchestration (.claude/workflows/*.js — agent/parallel/pipeline/phase fan-out). Includes GAIA benchmark component for Princeton HAL leaderboard submissions.
GAIA benchmark dispatcher — run, submit, validate, and track leaderboard scores against the Princeton HAL benchmark
Report cumulative GAIA API spend and project cost for planned configurations
Show measured benchmark runs stored across sessions in the gaia-runs memory namespace
Fetch and display current HAL GAIA leaderboard scores and our positioning
Execute a GAIA benchmark run — shells out to gaia-bench run, streams progress, and writes JSON results
Specialized agent for executing GAIA benchmark runs, monitoring progress, and analyzing results
Specialized agent for packaging, signing, and coordinating HAL leaderboard submission of GAIA benchmark results
Workflow automation specialist for creating, executing, and managing multi-step processes
Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap
Diagnose why a GAIA question failed — extract trace, classify failure mode, and propose a fix. Use when a GAIA benchmark run reports a failed/incorrect task_id and you need to root-cause it before resubmitting.
Walk through a complete GAIA benchmark→submit flow — from key resolution through HAL-compatible package generation
Author a workflow — either an MCP workflow template (persisted, lifecycle) or a native .claude/workflows/*.js orchestration script (agent/parallel/pipeline fan-out)
Run a workflow — drive an MCP workflow lifecycle (execute/pause/resume/cancel) or invoke + resume a native .claude/workflows/*.js orchestration via the Workflow tool
Uses power tools
Uses Bash, Write, or Edit tools
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Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work. Ruflo is the harness — the execution layer around Claude Code and Codex that adds 100+ specialized agents, coordinated swarms, self-learning memory, federated comms across machines, and enterprise security guardrails. So agents don't just run, they collaborate.
One npx ruflo init gives Claude Code a nervous system: agents self-organize into swarms, learn from every task, remember across sessions, and — with federation — securely talk to agents on other machines without leaking data. You keep writing code. Ruflo handles the coordination.
Self-Learning / Self-Optimizing Agent Architecture
npx claudepluginhub edisplay/ruflo --plugin ruflo-workflowsAutonomous /loop-driven task completion with learning, prediction, and progress tracking — wraps 10 autopilot_* MCP tools (status/enable/disable/config/reset/log/progress/learn/history/predict)
Session-as-skill browser automation: Playwright + RVF cognitive containers + ruvector trajectories + AgentDB selector memory + AIDefence PII/injection gates
Substrate plugin for Ruflo memory: AgentDB controller bridge (15 agentdb_* MCP tools), RuVector ONNX embeddings (10 embeddings_* tools incl. RaBitQ 32x quantization), and WASM HNSW pattern router (3 ruvllm_hnsw_* tools)
Documentation generation, API docs (JSDoc/TSDoc/OpenAPI), and drift detection — drives the `document` background worker via hooks_worker-dispatch; uses Haiku model for cost-efficient docs work
Test gap detection, coverage analysis, and automated test generation — drives the testgaps background worker via hooks_worker-dispatch; SPARC Refinement-phase canonical owner
v9.54.0 — Reliability wave: tangle contextual review correction loop with hard round ceiling, progress-supervised review rounds (per-agent stall watch, descendant-tree kills), council diversity and agy pin fixes, marketplace generator source-of-truth fix, provider troubleshooting runbook and cost-expectations docs. Run /octo:setup.
Harness-native ECC operator layer - 60 agents, 232 skills, 75 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
Persistent file-based planning for AI coding agents. Crash-proof markdown plans (task_plan.md, findings.md, progress.md) that survive context loss and /clear, with an opt-in completion gate and multi-agent shared state. Manus-style. Works with Claude Code, Codex CLI, Cursor, Kiro, OpenCode and 60+ agents via the SKILL.md standard. Includes Arabic, German, Spanish, and Chinese (Simplified and Traditional).
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
The Claude Code knowledge system — 380+ skills, 182+ agents, 100+ commands, 40 hooks, 32 rules, and workflows.
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review