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Plan Mode

AI Agent Updated 2026.08.29

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Please follow https://skillhub.cn/install/skillhub.md to install @user_5607203c/plan-mode.

About this skill

Problem

When an agent starts by writing code, editing files, or running commands, it can drift from the actual goal, miss side effects, and create work that is hard to review. plan-mode forces the agent to produce a plan first and block implementation until the user explicitly approves the approach.

How It Works

The skill is triggered by /plan. It then outputs a structured plan:
- Task Understanding: goal, expected deliverable, success criteria, constraints, and open questions.
- Context Review: modules, dependencies, interfaces, existing patterns, and affected logic.
- Missing Information: key assumptions, with at most three focused questions when needed.
- Implementation Plan: 3-8 steps, each with action, reason, impact, and expected output.
- Risk and Validation: regression risks, compatibility, performance, security, maintenance cost, tests, and edge cases.

After the plan is printed, the agent stops and waits for approval such as go, approved, or “execute”. Once approved, it enters execution mode: follow the approved plan, pause on material deviations, and keep progress updates concise.

Fit and Limits

This is useful for cross-module changes, interface updates, refactors, and production work where impact review matters. For trivial one-line edits or when the user explicitly wants immediate execution, the planning step can feel unnecessary.

Use Cases

  • Before a cross-module API change, use /plan to have the agent list affected files, interfaces, and tests first.
  • Before refactoring payment callbacks, ask plan-mode to produce a 3-8 step plan and regression risks for approval.
  • When reviewing a production config change, require the agent to explain config dependencies, data flow, and smaller alternatives.
  • Before fixing a security issue, ask the agent to propose validation, edge cases, and decision points first.

Best For

  • Backend engineers doing cross-module refactors who need impact review before the agent edits code.
  • SREs delegating production changes to an agent and needing explicit approval gates.
  • Tech leads using AI for troubleshooting who want to see the plan and risks before execution.
  • Developers making high-risk interface changes who want test strategy and edge cases confirmed first.