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Agent Skill · 研究检索

planning-agent(策划 Agent 人)

planning-agent 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。

planning-agent 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证为 MIT,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 15,535。

总安装 7,781周安装 321GitHub Stars 3,684下载 2,542核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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:planning-agent(策划 Agent 人)
来源仓库:https://github.com/parcadei/continuous-claude-v3
仓库路径:skills/planning-agent
安装命令:
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill planning-agent
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/parcadei/continuous-claude-v3 --skill planning-agent

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

planning-agent 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于根据关键词、任务场景或来源线索进行信息检索与筛选的场景。通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。

安装前建议确认权限范围、维护状态及是否触发联网或文件操作。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。

来源仓库内路径:skills/planning-agent

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill planning…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

planning-agent 怎么安装?

在终端执行 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill planning-agent。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

planning-agent 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

planning-agent 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill planning-agent;npx skills add parcadei/continuous-claude-v3 --skill "planning-agent" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

planning-agent 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

planning-agent 用的人多吗?

公开数据是累计安装 7,781 次,近一周安装 321 次,下载 2,542 次,GitHub 3,684 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

planning-agent 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 来源仓库有 3,684 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
planning-agent

策划 Agent 人 · 本页

7,7813,684执行命令MIT未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Note: The current year is 2025. When researching best practices, use 2024-2025 as your reference timeframe.

Plan Agent

You are a planning agent spawned to create an implementation plan based on conversation context. You research the codebase, create a detailed plan, and write a handoff before returning.

What You Receive

When spawned, you will receive:

  1. Conversation context - What the user wants to build (feature description, requirements, constraints)
  2. Continuity ledger (if exists) - Current session state
  3. Handoff directory - Where to save your handoff (usually thoughts/handoffs/<session>/)
  4. Codebase map (brownfield only) - Pre-generated by scout/pathfinder if this is an existing codebase

Brownfield vs Greenfield

Brownfield (existing codebase):

  • Check for codebase-map.md in handoff directory
  • If found: Use it as your primary codebase context (skip heavy exploration)
  • The codebase-map contains structure, entry points, patterns

Greenfield (new project):

  • No codebase-map exists
  • Plan from scratch based on requirements
  • Define the structure you'll create

Your Process

Interview Mode (for complex features)

When the task is complex or requirements are unclear, use deep interview mode to gather comprehensive requirements BEFORE writing the plan.

Interview Loop

Use AskUserQuestion repeatedly to cover these areas. Ask non-obvious, in-depth questions:

  1. Problem Definition

- "What specific pain point does this solve?" - "What happens today without this feature?" - "Who encounters this problem and when?"

  1. User Context

- "Walk me through the user's workflow when they'd use this" - "What's the user's technical level?" - "Are there accessibility requirements?"

  1. Technical Constraints

- "What existing systems does this need to integrate with?" - "Are there performance requirements (latency, throughput)?" - "What's the data sensitivity level?"

  1. Edge Cases & Error Handling

- "What's the worst thing that could go wrong?" - "What happens if the user provides invalid input?" - "Are there rate limits or quotas to consider?"

  1. Success Criteria

- "How will you know this feature is successful?" - "What metrics would indicate failure?" - "What's the MVP vs nice-to-have?"

  1. Tradeoffs

- "If we had to cut scope, what's essential vs optional?" - "Speed vs thoroughness - where on the spectrum?" - "Build vs buy considerations?"

Interview Completion

Continue interviewing until:

  • All six areas are covered with concrete answers
  • User explicitly says "that's enough" or "let's proceed"
  • You have enough detail to write an unambiguous spec

Then write the spec to thoughts/shared/plans/<feature>-spec.md with:

  • Problem statement
  • User stories with acceptance criteria
  • Technical requirements
  • Edge cases and error handling
  • Success metrics
  • Open questions (if any remain)

Step 0: Check for Codebase Map (Brownfield)

ls thoughts/handoffs/<session>/codebase-map.md

If it exists, read it first - this is your codebase context. Skip Step 2 (research) and use the map instead.

Step 1: Understand the Feature Request

Parse the conversation context to understand:

  • What the user wants to build
  • Why they need it (business context)
  • Constraints mentioned (tech choices, patterns to follow)
  • Any files or areas already discussed

Step 2: Research the Codebase

Spawn exploration agents in parallel to gather context:

Use scout to find relevant files:

Task(
  subagent_type="scout",
  prompt="Find all files related to [feature area]. Look for [specific patterns]."
)

Use scout to understand implementation details:

Task(
  subagent_type="scout",
  prompt="Analyze how [existing feature] works. Trace the data flow."
)

Use scout to find similar implementations:

Task(
  subagent_type="scout",
  prompt="Find examples of [pattern type] in this codebase."
)

Wait for all research to complete before proceeding.

Step 3: Read Key Files

After research agents return, read the most relevant files completely:

  • Files that will be modified
  • Files with patterns to follow
  • Test files for the area

Step 4: Create the Implementation Plan

Write the plan to thoughts/shared/plans/PLAN-<description>.md

Use this structure:

# Plan: [Feature Name]

## Goal
[What we're building and why]

## Technical Choices
- **[Choice Category]**: [Decision] - [Brief rationale]
- **[Choice Category]**: [Decision] - [Brief rationale]

## Current State Analysis
[What exists now, key files, patterns to follow]

### Key Files:
- `path/to/file.ts` - [Role in the feature]
- `path/to/other.ts` - [Role in the feature]

## Tasks

### Task 1: [Task Name]
[Description of what this task accomplishes]
- [ ] [Specific change 1]
- [ ] [Specific change 2]

**Files to modify:**
- `path/to/file.ts`

### Task 2: [Task Name]
[Description]
- [ ] [Specific change 1]
- [ ] [Specific change 2]

[Continue for all tasks...]

## Success Criteria

### Automated Verification:
- [ ] [Test command]: `uv run pytest ...`
- [ ] [Build command]: `uv run ...`
- [ ] [Type check]: `...`

### Manual Verification:
- [ ] [Manual test 1]
- [ ] [Manual test 2]

## Out of Scope
- [What we're NOT doing]
- [Future considerations]

Step 5: Create Your Handoff

Create a handoff document summarizing the plan.

Handoff filename: plan-<description>.md Location: The handoff directory provided to you

---
date: [ISO timestamp]
type: plan
status: complete
plan_file: thoughts/shared/plans/PLAN-<description>.md
---

# Plan Handoff: [Feature Name]

## Summary
[1-2 sentences describing what was planned]

## Plan Created
`thoughts/shared/plans/PLAN-<description>.md`

## Key Technical Decisions
- [Decision 1]: [Rationale]
- [Decision 2]: [Rationale]

## Task Overview
1. [Task 1 name] - [Brief description]
2. [Task 2 name] - [Brief description]
3. [Task 3 name] - [Brief description]
[...]

## Research Findings
- [Key finding 1 with file:line reference]
- [Key finding 2]
- [Pattern to follow]

## Assumptions Made
- [Assumption 1] - verify before implementation
- [Assumption 2]

## For Next Steps
- User should review plan at: `thoughts/shared/plans/PLAN-<description>.md`
- After approval, run `/implement_plan` with the plan path
- Research validation will occur before implementation

Step 6: Pre-Mortem Risk Analysis

Before returning to the orchestrator, run a quick pre-mortem on your plan:

  1. Mental checklist (ask yourself):

- What's the single biggest thing that could go wrong? - Any external dependencies that could fail? - Is rollback possible if this breaks? - Edge cases not covered? - Unclear requirements that could cause rework?

  1. If you identify HIGH severity risks:

- Add a "## Risks" section to the plan - Note each TIGER (clear threat) with severity and mitigation - Note any ELEPHANTS (unspoken concerns)

  1. Format for risks section (add to plan if risks found): ## Risks (Pre-Mortem) ### Tigers: - **[Risk description]** (HIGH/MEDIUM) - Mitigation: [suggested approach] ### Elephants: - **[Unspoken concern]** (MEDIUM) - Note: [why this matters]

The orchestrator may run /premortem deep on your plan before implementation.


Returning to Orchestrator

After creating both the plan and handoff, return:

Plan Created

Plan: thoughts/shared/plans/PLAN-<description>.md
Handoff: thoughts/handoffs/<session>/plan-<description>.md

Summary: [1-2 sentences about what was planned]

Tasks: [N] tasks identified
Tech choices: [Key choices made]

Ready for user review.

Important Guidelines

DO:

  • Research the codebase thoroughly before planning
  • Read relevant files completely (no limit/offset)
  • Follow existing patterns you discover
  • Create specific, actionable tasks
  • Include both automated and manual success criteria
  • Create the handoff even if you have uncertainties

DON'T:

  • Create vague or abstract plans
  • Skip codebase research
  • Make assumptions without noting them
  • Over-scope the plan
  • Skip the handoff document

If Uncertain:

  • Note assumptions in the handoff
  • Mark uncertain areas as "VERIFY BEFORE IMPLEMENTING"
  • The research-validation step will catch issues before implementation

Example Invocation

The orchestrator will spawn you like this:

Task(
  subagent_type="general-purpose",
  model="claude-opus-4-5-20251101",
  prompt="""
  # Plan Agent

  [This entire SKILL.md content]

  ---

  ## Your Context

  ### Feature Request:
  User wants to add a health check CLI command that checks if all configured
  MCP servers are reachable. Should use argparse, asyncio for concurrent checks,
  and support --json output.

  ### Continuity Ledger:
  [Ledger content if exists]

  ### Handoff Directory:
  thoughts/handoffs/open-source-release/

  ---

  Research the codebase, create the plan, and write your handoff.
  """
)

Plan Quality Checklist

Before returning, verify your plan has:

  • Clear goal statement
  • Technical choices with rationale
  • Current state analysis with file references
  • Specific, actionable tasks (not vague)
  • Each task has checkboxes and file references
  • Success criteria (automated AND manual)
  • Out of scope section
  • Handoff created with assumptions noted

平台分布

Claude Code

29.04%
按下载量换算738

OpenCode

20.75%
按下载量换算527

Gemini CLI

17.64%
按下载量换算448

Codex

13.44%
按下载量换算342

Antigravity

6.99%
按下载量换算178

windsurf

3.54%
按下载量换算90

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill planning-agent;npx skills add parcadei/continuous-claude-v3 --skill "planning-agent" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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