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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

499

周安装

21

GitHub Stars

公开资料未说明

下载量

175
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

3

许可证

MIT

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/idrevnii/perks
仓库路径:skills/deep-plan
安装命令:
npx skills add https://github.com/idrevnii/perks --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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/idrevnii/perks --skill deep-plan

简介

用于在实施前系统化澄清需求与决策点,避免盲目编码返工。

  • 适用于存在多个有效方案选择或上下文不足的场景。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • 输出结构化讨论记录与备选方案对比,辅助达成共识。
  • 不适用于单一明确解的问题,需用户主动提出规划请求。
  • deep-plan 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Deep Plan

Systematic approach for gathering requirements and clarifying decisions before implementation.

When to Activate

  • User is in PLANNING mode
  • User explicitly asks to "plan", "discuss", "let's think through", "review approach", "before we start", "what do you think about"
  • Agent identifies choices between 2+ valid approaches
  • Agent lacks context needed for confident decision-making

When NOT to Activate

  • Fixing obvious bugs/typos (clear single correct solution)
  • User explicitly says "do as you see fit" or similar
  • Trivial changes with single logical approach
  • User already provided specific requirements

Clarification Process

1. Context Questions First

Before technical decisions, understand the project context:

  • Timeline/deadline constraints?
  • Team size and experience level?
  • Existing tech stack or constraints?
  • Scale expectations?

These answers inform all subsequent recommendations.

2. Identify Decision Points

Scan the task and categorize decisions by priority:

  • 🔴 Blocking — cannot proceed without answer
  • 🟡 Important — affects outcome, but has reasonable default
  • 🟢 Nice-to-know — can decide later

Types of decisions:

  • Technical choices: libraries, tools, patterns, algorithms
  • Architecture decisions: structure, component breakdown, data flow
  • Scope boundaries: what's included vs excluded
  • Priority conflicts: what to implement first
  • Missing context: unclear requirements, ambiguous terminology

3. Progressive Questioning

Ask questions in phases, starting with most critical:

Phase 1: Context questions + 🔴 Blocking questions (max 5-7 per message) Phase 2: After blocking resolved, ask 🟡 Important questions Phase 3: Ask 🟢 Nice-to-know only when relevant phase begins

Do NOT dump all questions at once. Batch by priority and wait for responses.

4. Question Format with Confidence

Group questions by category, indicate recommendation confidence:

## [Category] Decisions

1. **[Brief description]** 🔴
   - Option A: [description] — **strongly recommend** because [reason]
   - Option B: [description] — better for [use case]

2. **[Another question]** 🟡
   - Option A: [description] — **lean towards** this
   - Option B: [description] — **no strong preference**, depends on [factor]

Confidence levels:

  • strongly recommend — almost always the right choice
  • lean towards — good option, but context-dependent
  • no strong preference — both options are equally valid

5. Handle Unanswered Questions

If user does not answer some questions:

  • Do NOT assume defaults silently
  • Explicitly ask: "For questions X and Y, should I proceed with my recommendations, or do you have other preferences?"
  • Wait for explicit confirmation before proceeding

6. Mid-Execution Re-check

If during implementation agent discovers:

  • New ambiguity not covered by initial planning
  • A decision that has multiple valid approaches
  • Context that contradicts earlier assumptions

STOP and return to clarification mode. Do not make assumptions.

7. Decision Summary (Optional)

After all questions resolved, offer to create a decision log:

  • "Would you like me to save these decisions to the implementation plan for reference?"
  • Only create if user agrees
  • Do NOT create files by default

Key Principles

  1. Context before details — understand the big picture first
  2. Ask first, implement after — when in doubt, ask
  3. Progressive disclosure — blocking → important → nice-to-know
  4. Explicit consent — never assume silence means agreement
  5. Stay focused — max 5-7 questions per message
  6. Re-check when uncertain — new ambiguity = new questions

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

26.41%
按下载量换算46

Antigravity

25.22%
按下载量换算44

OpenCode

18.89%
按下载量换算33

Codex

11.77%
按下载量换算21

Gemini CLI

7.61%
按下载量换算13

windsurf

3.73%
按下载量换算7

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。

来源信息

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