agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid…
Make a long-running or looped task list learn as it goes with agentic-flow - before each task recall similar past episodes and their critiques, after each task record the outcome, and re-engage failed tasks with what was learned. Use with /loop or autopilot-style runs, or when
Claude Code
$ npx -y skills add ruvnet/agentic-flow --skill autopilot-learning --agent claude-code
Codex
$ npx skills add ruvnet/agentic-flow --skill autopilot-learning -a codex -y
Cursor
$ npx skills add ruvnet/agentic-flow --skill autopilot-learning -a cursor -y
Gemini CLI
$ npx skills add ruvnet/agentic-flow --skill autopilot-learning -a gemini-cli -y
GitHub Copilot
$ npx skills add ruvnet/agentic-flow --skill autopilot-learning -a github-copilot -y
OpenCode
$ npx skills add ruvnet/agentic-flow --skill autopilot-learning -a opencode -y
Windsurf
$ npx skills add ruvnet/agentic-flow --skill autopilot-learning -a windsurf -y
name: autopilot-learning description: Make a long-running or looped task list learn as it goes with agentic-flow - before each task recall similar past episodes and their critiques, after each task record the outcome, and re-engage failed tasks with what was learned. Use with /loop or autopilot-style runs, or when the user asks for "self-learning" task completion.
agentic-flow's autopilot learning bridge (ADR-058) records each task episode and, when a task comes back, hands the agent what earlier attempts learned. In Claude Code you get the same loop with the ReasoningBank tools on the `agentic-flow` MCP server. Recording needs the `learning` or `admin` MCP profile (plugin setting `mcp_profile`); recall works under every profile.
1. **Pick** the next task (from the task list, a TODO file, or the user). 2. **Recall**: `agentdb_pattern_search` with the task text, `k: 5`. Read failures (`onlyFailures: true`) before successes when the task was attempted before. 3. **Plan** in one paragraph that cites what the recall showed, or says nothing matched. 4. **Do** the task and verify it with something checkable (tests, a build, a command's output). 5. **Record**: `agentdb_pattern_store` with `success`, an honest `reward`, the number of attempts in `critique`, and what to do differently. 6. **Re-engage** a failed task only with a changed approach; the same plan twice is a loop, not learning. After three failed attempts, stop and report.
At the end: tasks done, tasks failed, attempts per task, and the critiques worth keeping.
Production-ready AI agent orchestration with 66 self-learning agents, 213 MCP tools, and autonomous multi-agent swarms.
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You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.