/autopilot-loop
Run an autonomous /loop iteration -- check progress, work on next task, schedule next wake
$ npx -y skills add ruvnet/ruflo --skill autopilot-loop --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/autopilot-loop
Context preview
The summary Claude sees to decide when to auto-load this skill.
Run an autonomous /loop iteration -- check progress, work on next task, schedule next wake
SKILL.md
autopilot-loop.SKILL.mdname: autopilot-loop
description: Run an autonomous /loop iteration -- check progress, work on next task, schedule next wake
argument-hint: ""
allowed-tools: mcp__plugin_ruflo-core_ruflo__autopilot_status mcp__plugin_ruflo-core_ruflo__autopilot_predict mcp__plugin_ruflo-core_ruflo__autopilot_log mcp__plugin_ruflo-core_ruflo__autopilot_progress mcp__plugin_ruflo-core_ruflo__autopilot_disable ScheduleWakeup Agent
Run one autopilot iteration using Claude Code's native /loop:
1. Check status: `mcp__plugin_ruflo-core_ruflo__autopilot_status` 2. If all tasks complete or max iterations reached, call `mcp__plugin_ruflo-core_ruflo__autopilot_disable` and stop 3. Get prediction: `mcp__plugin_ruflo-core_ruflo__autopilot_predict` for the optimal next action 4. Execute the predicted task (spawn agent, edit code, run tests, etc.) 5. Log via `mcp__plugin_ruflo-core_ruflo__autopilot_log` 6. Schedule next: `ScheduleWakeup({ delaySeconds: 270, reason: "next autopilot iteration" })`
Cache-Aware Scheduling
Always use delay 270s (under 300s cache TTL) to keep the prompt cache warm between iterations.
Task Sources
Autopilot discovers tasks from:
- **team-tasks**: Claude Code TaskList entries
- **swarm-tasks**: MCP task_list entries
- **file-checklist**: Markdown checkbox items in tracked files
Configure: `mcp__plugin_ruflo-core_ruflo__autopilot_config({ taskSources: ["team-tasks", "swarm-tasks"] })`
Read more
name: autopilot-loop description: Run an autonomous /loop iteration -- check progress, work on next task, schedule next wake argument-hint: "" allowed-tools: mcp__plugin_ruflo-core_ruflo__autopilot_status mcp__plugin_ruflo-core_ruflo__autopilot_predict mcp__plugin_ruflo-core_ruflo__autopilot_log mcp__plugin_ruflo-core_ruflo__autopilot_progress mcp__plugin_ruflo-core_ruflo__autopilot_disable ScheduleWakeup Agent
Run one autopilot iteration using Claude Code's native /loop:
1. Check status: `mcp__plugin_ruflo-core_ruflo__autopilot_status` 2. If all tasks complete or max iterations reached, call `mcp__plugin_ruflo-core_ruflo__autopilot_disable` and stop 3. Get prediction: `mcp__plugin_ruflo-core_ruflo__autopilot_predict` for the optimal next action 4. Execute the predicted task (spawn agent, edit code, run tests, etc.) 5. Log via `mcp__plugin_ruflo-core_ruflo__autopilot_log` 6. Schedule next: `ScheduleWakeup({ delaySeconds: 270, reason: "next autopilot iteration" })`
Cache-Aware Scheduling
Always use delay 270s (under 300s cache TTL) to keep the prompt cache warm between iterations.
Task Sources
Autopilot discovers tasks from:
- **team-tasks**: Claude Code TaskList entries
- **swarm-tasks**: MCP task_list entries
- **file-checklist**: Markdown checkbox items in tracked files
Configure: `mcp__plugin_ruflo-core_ruflo__autopilot_config({ taskSources: ["team-tasks", "swarm-tasks"] })`
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
Other skills on claude-flow.
- /agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Open skill - /agentdb-learning
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Open skill - /agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Open skill - /agentdb-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Open skill - /agentdb-vector-search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Open skill - /agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Open skill