Agent runtimes — local WASM-sandboxed agents (rvagent) + Anthropic Claude Managed Agents (cloud); one interface, local-vs-cloud backends
Plugin ruflo-agent from marketplace moaa in ahmadhammoud56/MOAA. Commands read from the repo's manifest — 5 days ago.
managed-agentRun an Anthropic Claude Managed Agent — a cloud agent harness (container + filesystem + tools), the cloud counterpart of the local wasm-agent runtime
managed-workflowDiscover and run bounded remote dynamic workflows with Ruflo mod policy, worker inspection and independent acceptance gates
nested-subagentsSpawn nested sub-agents (agents that spawn sub-agents, up to depth=5) via Claude Code's native Task tool — for context-managed deep delegation
wasm-agentCreate and manage sandboxed WASM agents for isolated code execution
wasm-galleryBrowse, publish, and install WASM agents from the community gallery
/ruflo-agent:managed-agentAnthropic Claude Managed Agents (cloud runtime) — list cloud sessions, check status, fetch a transcript, clean up
/ruflo-agent:wasmWASM agent management -- list running agents, check status, browse gallery
nested-coordinatorOrchestrator that spawns nested sub-agents (up to depth=5) via Claude Code's native Task tool — for deep delegation where context isolation matters more than throughput
nested-leafLeaf-worker template for nested spawn trees — performs one focused task and returns a structured summary. Deliberately does NOT have the Task tool (least-privilege boundary)
nested-queen-leafTier-2 leaf — bottom of a queen-led tree. Deliberately no Task tool (least-privilege), but DOES record trajectory steps, AIDefence-scan its own inbound prompt, and report cost — so the queen's intelligence pipeline learns from every leaf outcome
nested-queen-researcherTier-2 recursive researcher — nested-researcher's role with HNSW pattern retrieval, AIDefence-gated web content, hive-mind consensus on which followups to pursue, and full trajectory recording
nested-queen-reviewerTier-2 recursive reviewer — find-and-verify like nested-reviewer, but with hive-mind byzantine consensus on findings (replaces inline majority voting), AIDefence-screened evidence, and trajectory learning across review runs
nested-queenHeavyweight nested orchestrator — wires Claude Code's depth=5 nesting onto ruflo's hive-mind, swarm, intelligence pipeline, claims/AuthScope, AIDefence, and cost-budget machinery. Use when depth alone isn't enough.
nested-researcherRecursive research orchestrator — fans out into sub-research branches when an investigation deepens, keeping each branch in its own context window
nested-reviewerRecursive review orchestrator — each finding can spawn an adversarial verifier in its own context, so review remains thorough without bloating the top-level reviewer
wasm-specialistWASM sandbox specialist for creating, managing, and sharing isolated agent environments
Harness-native ECC operator layer - 68 agents, 286 skills, 94 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses
200 skills · 94 commands · 68 agents · 1 MCP server · hooks
Expose the plugin-safe Claude Code subset of Agentic Awesome Skills through a single marketplace entry.
200 skills
Specialist Council: 60 role agents with per-role model/effort and arena-forged members; assembles teams and delivers one verified result.
2 skills · 60 agents · hooks
Matt Pocock's agent skills for real engineering (grilling, spec/ticket flows, TDD, code review, domain modelling and more).
37 skills
Token usage tracking, model cost attribution per agent, budget alerts, and optimization recommendations
24 skills · 1 command · 1 agent · hooks
Talk like caveman. Cut 65% output tokens vs unprompted baseline (measured). Keep all technical accuracy.
20 skills · 1 command · 3 agents · hooks
A four-tier memory ladder for Claude Code where promotion is earned by recurrence, not asserted by confidence. L0 raw transcripts are never injected; L1 candidate atoms are gitignored and recalled on lexical relevance per prompt; L2 project context loads each session start; L3 stable persona is always loaded. L1 to L2 needs three distinct sessions spanning two distinct calendar days (a stated claim needs two; a verified claim is the only single-observation path); L2 to L3 needs two projects and thirty days. Two gates refuse rather than guess: a claim altered by the redaction pass never promotes on evidence alone, and a contradicted claim freezes until a human resolves it. Three hooks run the loop unattended and every one fails open. Nothing reaches a committed CLAUDE.md without an explicit human adopt. Ships 5 stdlib scripts, 3 hooks, a cs-memory-curator agent, /cs-memory, a JSON schema, and a 69-check validator. Concept from TencentCloud/TencentDB-Agent-Memory (MIT); no upstream code included.
1 skill · 1 command · 1 agent · hooks
Audit a project's npm, PyPI, and Go dependencies for supply-chain risk: version-matched advisories for direct dependencies and the full lockfile tree, abandoned upstreams, npm publisher concentration, and install scripts
1 skill
Ships pull requests to open-source projects you don't maintain — CONTRIBUTING compliance, PR-size/scope checks, minimal-diff implementation, AI-disclosure PR descriptions, and rebase or bot-review replies. Use for 提 PR / 上游 PR / 贡献代码, "open a PR", "fix this upstream", "rebase against main", "respond to the bot review". Not for reviewing PRs as maintainer (use github-review-pr) or general GitHub CRUD (use github-ops).
1 skill
Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689), Articles 51-56. Covers GPAI model identification (Art. 3(63)), systemic risk model designation (Art. 3(65), Art. 51, 10^25 FLOP training compute threshold), upstream provider obligations under Art. 53 (transparency, technical documentation, copyright policy, training data summary), additional systemic risk obligations under Art. 55 (model evaluation, adversarial testing, AI Office incident notification, cybersecurity), systemic-risk notification and Commission designation under Art. 52, downstream Art. 25 quasi-provider duties, and Art. 53(2) open-source exemption. Built for in-house counsel and AI governance teams reviewing LLM or foundation model deployments, drafting GPAI compliance plans, or assessing systemic risk thresholds. DACH: BaFin, BSI, BNetzA. Pairs with the EU AI Act System Classifier, High-Risk Readiness, FRIA, and Serious Incident Reporting skills.
200 skills
BDD-first engineering skills forked from mattpocock/skills (synced 2026-11-04, upstream v1.3.2 + 3 commits)
50 skills
Research-augmented fork of obra/superpowers. Ships 13 workflow skills (brainstorming, planning, code review, debugging, verification, worktrees, parallel agents) standalone, with no dependency on the upstream superpowers plugin.
13 skills
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