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plan
Agent Skill for planning implementation work into comprehensive, testable steps. Use before implementation to create a clear roadmap for execution.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Agent Skill for planning implementation work into comprehensive, testable steps. Use before implementation to create a clear roadmap for execution.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
Agent Skill for writing and reviewing durable design specs. Use when the user asks to write, formalize, save, review, or approve a spec, design document, RFC, or implementation-ready proposal. If the direction is still vague or unsettled, use `brainstorming` first before producing the spec. If the direction is already clear and no spec is needed, use `plan`.
Agent Skill for spawning new AI agents with interactive TUI in tmux. Use when work can be offloaded to new or existing AI agents (exploration, code review, second opinions, parallel work, etc.). Can also be used if spawning new agents with different model families (e.g. Spawn GPT models when you are Claude Code, Spawn Claude models when you are Codex, Or spawn any other models (e.g. DeepSeek, Gemini) with Pi). Prefer harness's own subagent tools when no need for a different model.
Agent Skill for OpenCLI. Use when the user wants to control websites through the `opencli` CLI, run or inspect OpenCLI site/app/external adapters, use the OpenCLI browser bridge/CDP automation
Agent Skill for collaborative brainstorming and design exploration. Use when the user wants to think through an idea, compare options, clarify trade-offs, or get a quick recommendation, without forcing a written artifact. If the user wants a saved, reviewed design spec, use `design-spec`.
Agent Skill for working with Python. Use when writing, editing, or reviewing Python code and scripts.
Agent Skill for browser automation over the Chrome DevTools Protocol connection. Use when a task requires interacting with or inspecting live web pages, forms, UI behavior, or any scraping tasks like data extraction, form filling, etc, or any browser automation tasks.
| name | plan |
| description | Agent Skill for planning implementation work into comprehensive, testable steps. Use before implementation to create a clear roadmap for execution. |
| metadata | {"attribution":"attribution.md"} |
Plan implementation work for an implementer who starts with very little context about the codebase or problem domain. Shape the execution strategy into clear, reliable, and verifiable steps, then save the plan as a durable handoff artifact under plan/.
Every plan MUST include a ## References section generated by invoking the reference-recorder skill.
Default save path: plan/plan-<slug>-YYYYMMDD.md
User preferences for the plan location override this default.
Use this skill when:
Do not use this skill when:
If the task still needs design work, use brainstorming first, then design-spec if a durable, reviewed spec is wanted before planning.
Start from the best available source of truth:
Use one planning process that scales with task complexity. Simpler work needs fewer planning steps; larger work needs more detail.
reference-recorder skill to generate a ## References sectionplan/When todo tools or skills are available, use them to track planning progress.
If the work covers multiple independent subsystems or would produce an unwieldy single plan, propose splitting it into multiple plans. Each plan should target one coherent outcome and produce working, testable software on its own.
Before defining tasks, map which files or areas are likely to change and why. This is where decomposition decisions get locked in.
Each task should be independently meaningful, executable, and verifiable.
Every plan MUST start with this header:
# [Feature Name] Implementation Plan
> **For agentic workers:** Use the harness's preferred task-tracking and delegation tools when available. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** [One sentence describing what this changes]
**Source of Truth:** [Approved spec, issue, user request, or other basis for
this plan]
**Scope:** [What is included and any important exclusions]
**Approach:** [2-3 sentences about the execution strategy]
**Verification:** [How the final result will be validated]
---
Create a ### Task Steps section.
Use a structure like this and adapt the detail to the task
### Task N: [Task Name]
#### N.1 Intent
[What this task accomplishes]
#### N.2 Files
- Create: `exact/path/to/new_file.py`
- Modify: `exact/path/to/existing.py`
- Test: `tests/exact/path/to/test_file.py`
#### N.3 Dependencies
[Earlier tasks, prerequisites, or "None"]
- [ ] **Step 1:** [Concrete action]
- [ ] **Step 2:** [Concrete action]
- [ ] **Step 3:** [Concrete action]
#### N.4 Verification
- Run: `pytest tests/path/test_file.py -v`
- Expect: [What should pass, change, or be observable]
#### N.5 Notes
- [Optional details, snippets, or edge cases only when useful]
Every plan MUST include a ## References section generated by invoking the reference-recorder skill.
Use references to capture:
Mark especially important references clearly when needed.
Dispatch a plan reviewer subagent using references/plan-reviewer-prompt.md to review the plan before handoff to ensure the scope, task order, verification, and references are clear and reliable.
For smaller or straightforward tasks, one review iteration may be enough. For larger, riskier, or more ambiguous tasks, multiple iterations may be needed to ensure the plan is clear and reliable.
If the review loop exceeds 3 iterations, surface the problem to a human for guidance.
After saving the plan:
"Plan complete and saved to
<path>. Review it and let me know how you'd like to execute it."
Present the saved plan to the user and ask for execution instructions.