Use when you have a written implementation plan to execute in a separate session with review checkpoints
Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add https://github.com/oabdelmaksoud/Openclaw-skills-Compilations --skill executing-plansこのコマンドをClaude Codeにコピー＆ペーストしてスキルをインストール
Use when managing the Claude CLI Proxy (ban protector) — start, stop, restart, status, logs, backup, test, setup, or register the proxy in OpenClaw. Triggers on: 'proxy status', 'ban protector', 'start proxy', 'stop proxy', 'proxy health', 'antiban', 'cli proxy'.
Run stateful multi-agent graph workflows using LangGraph (linear, supervisor, parallel, conditional). Use when tasks need dynamic routing, branching logic, or a supervisor agent delegating work. All LLM calls use OpenClaw's existing provider config — no API keys needed. Choose over autogen-collab (debate) and crewai-collab (fixed pipelines) when you need conditional edges, a supervisor making routing decisions, or parallel fan-out with synthesis.
Run multi-agent AutoGen-style debates when tasks need consensus across specialists. Uses OpenClaw's existing provider configuration — no API keys or extra setup needed. Use when: architecture decisions, design reviews, root cause analysis, or complex research where multiple expert perspectives improve the answer. Triggered automatically by Cooper for high-complexity tasks or explicitly via [autogen] tag or "debate this" / "get consensus" instructions.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Run structured multi-agent workflows using CrewAI (sequential, hierarchical, consensus). Each agent has a defined role, goal, and backstory. Tasks have explicit expected outputs. Use when: structured pipelines (research → design → implement), manager-supervised workflows, or consensus-building with clear acceptance criteria. All LLM calls use OpenClaw's existing provider config — no API keys needed.
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
| name | executing-plans |
| description | Use when you have a written implementation plan to execute in a separate session with review checkpoints |
| metadata | {"openclaw":{"emoji":"🚀"}} |
Load plan, review critically, execute tasks in batches, report for review between batches.
Core principle: Batch execution with checkpoints for architect review.
Announce at start: "I'm using the executing-plans skill to implement this plan."
Default: First 3 tasks
For each task:
When batch complete:
Based on feedback:
After all tasks complete and verified:
STOP executing immediately when:
Ask for clarification rather than guessing.
Return to Review (Step 1) when:
Don't force through blockers - stop and ask.
Required workflow skills:
