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deep-plan深度计划

Agent Skill

deep-plan 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

451

周安装

19

GitHub Stars

公开资料未说明

下载量

158
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:deep-plan(深度计划)
来源仓库:https://github.com/b4r7x/agent-skills
仓库路径:skills/deep-plan
安装命令:
npx skills add https://github.com/b4r7x/agent-skills --skill deep-plan
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/b4r7x/agent-skills --skill deep-plan

简介

deep-plan 将粗略需求自动转化为完整实施方案,涵盖任务分解、歧义澄清和执行步骤。

  • 适用于在 Codex、Claude、Cursor、Gemini CLI 中从零开始规划复杂功能实现或系统重构。
  • 按五阶段流程执行:分解、澄清、调研、计划、执行,确保不遗漏关键环节。
  • 安装方式:npx skills add https://github.com/b4r7x/agent-skills --skill deep-plan。
  • 使用前需确认权限范围、维护状态,并注意是否会触发联网、命令执行或文件读写操作。

SKILL.md

Deep Plan

Takes a rough idea and autonomously produces an implementation plan — researching, clarifying, and executing without manual prompt refinement.

Workflow

Execute all five phases in strict order. Do not skip phases. Do not show intermediate artifacts to the user unless asked.

Phase 1 — Decompose the Dirty Prompt

Parse the user's rough input. Produce an internal working list:

  1. Task type: implement / refactor / debug / add feature / migrate / integrate / remove
  2. Scope estimate: single file / multi-file / cross-package / architectural
  3. Ambiguities: list every unclear aspect (behavior, boundaries, edge cases, naming, approach)
  4. Deep-dive targets: list specific files, modules, or patterns that need reading before planning

Do not output this list to the user. It drives the next phases internally.

Phase 2 — Deep Research

2a. Project context (always run)

Follow references/project-research-playbook.md — run the minimum required research:

cat README.md 2>/dev/null | head -80
cat CLAUDE.md 2>/dev/null | head -60
cat AGENTS.md 2>/dev/null | head -60
cat .github/copilot-instructions.md 2>/dev/null | head -40
cat .cursorrules 2>/dev/null | head -40
cat package.json 2>/dev/null | head -40
find . -maxdepth 2 -type d | grep -v node_modules | grep -v .git | head -25
git log --oneline -8 2>/dev/null

2b. Task-specific deep dives

For each deep-dive target from Phase 1:

  • Read the relevant source files (use view, grep, glob)
  • Understand existing patterns, interfaces, and conventions in those areas
  • Map dependencies — what does the target file import/export? What depends on it?
  • Note any existing tests for affected code

Build an internal understanding of current state vs desired state.

Phase 3 — Ask Clarifying Questions

Review the ambiguities list from Phase 1, filtered by what Phase 2 answered.

For each remaining ambiguity:

  • Ask the user one question at a time using the ask_user tool
  • Prefer multiple-choice questions (provide a choices array) — faster for the user
  • Put the recommended option first with (Recommended) suffix
  • Ask a maximum of 5 questions — if more remain, make reasonable inferences and state them in the plan

Skip this phase entirely if Phase 2 resolved all ambiguities.

Question quality guidelines:

  • Ask about behavior and scope, not implementation details you can decide yourself
  • Bad: "Should I use a for loop or map?" — decide this yourself
  • Good: "Should the search be case-sensitive or case-insensitive?"
  • Good: "Should this feature support batch operations or single-item only?"

Phase 4 — Generate Internal Prompt

Using everything gathered in Phases 1–3, generate a structured prompt following references/ai-coding-prompt-guide.md.

This prompt is internal — do NOT show it to the user.

The prompt must target: "Create a detailed implementation plan" as its task.

Required fields:

  • Role: matching the project's actual stack
  • Context: framework, key libraries, relevant file paths (exact paths from Phase 2)
  • Task: "Create an implementation plan for [specific feature/change]"
  • Constraints: project conventions, what not to change, patterns to follow
  • Output format: structured plan with approach, file changes, and actionable todos
  • Acceptance criteria: what must be true when the plan is fully implemented
  • References: exact file paths discovered in Phase 2

For complex multi-file tasks, use the XML format variant from the guide.

Phase 5 — Execute and Produce Plan

Now execute the prompt you generated in Phase 4. Produce a structured implementation plan.

The plan must include:

  1. Problem statement — one paragraph summarizing what needs to happen and why
  2. Approach — high-level strategy (2-4 sentences)
  3. File changes — for each file to create/modify:

- File path - What changes and why - Key implementation details (not full code, but enough to execute without guessing)

  1. Todos — ordered list of actionable tasks with clear descriptions
  2. Considerations — edge cases, risks, things to verify after implementation

If operating in plan mode (the primary use case):

  • Save the plan to plan.md in the session workspace
  • Populate the SQL todos table with actionable items (descriptive kebab-case IDs, full descriptions)
  • Set up todo_deps for any ordering constraints
  • Call exit_plan_mode to present the plan for user approval

If NOT in plan mode:

  • Output the plan directly in the response
  • Still offer to populate todos if the user wants to track implementation

Example

User input: "add dark mode"

Phase 1 (internal):

  • Task: add feature
  • Scope: multi-file (theme, components, storage)
  • Ambiguities: toggle location? persist preference? system preference detection? which components?
  • Deep-dive targets: existing theme/styling files, layout component, any CSS variables

Phase 2: Discovers Tailwind + CSS variables in globals.css, React context in src/providers/, localStorage usage in src/lib/storage.ts, 23 components using hardcoded colors.

Phase 3 (asks user):

  • "Where should the dark mode toggle live?" → choices: ["Header nav bar (Recommended)", "Settings page", "Both"]
  • "Should it detect system preference on first visit?" → choices: ["Yes (Recommended)", "No, default to light"]

Phase 4 (internal prompt generated, not shown)

Phase 5 (plan output):

## Problem
The app has no dark mode support. Users need a toggle in the header
that persists preference and respects system settings on first visit.

## Approach
Add a ThemeProvider using React context + CSS variables. Persist to
localStorage. Detect system preference via prefers-color-scheme media query.

## File Changes
- `src/providers/theme-provider.tsx` — new file, React context with light/dark/system
- `src/globals.css` — add CSS variables for dark palette under .dark class
- `src/components/header.tsx` — add toggle button
- `src/lib/storage.ts` — add theme preference getter/setter
...

## Todos
1. create-theme-provider — Create ThemeProvider with context, localStorage sync, system detection
2. add-css-variables — Define dark palette CSS variables in globals.css
3. add-toggle-ui — Add dark mode toggle to header
4. update-storage — Add theme preference to storage helpers
5. audit-components — Check 23 components for hardcoded colors, switch to CSS variables

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.29%
按下载量换算54

Claude

29.45%
按下载量换算47

Cursor

18.98%
按下载量换算30

Gemini CLI

8.53%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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