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spawning-plan产卵计划

Agent Skill

spawning-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

815

周安装

35

GitHub Stars

4

下载量

286
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:spawning-plan(产卵计划)
来源仓库:https://github.com/octaviusp/spawning-plan-skill
仓库路径:skills/spawning-plan
安装命令:
npx skills add https://github.com/octaviusp/spawning-plan-skill --skill spawning-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/octaviusp/spawning-plan-skill --skill spawning-plan

简介

spawning-plan 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 安装前需确认权限范围、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。

SKILL.md

Spawning Plan

Design the optimal agent team for the task. Performant, precise, minimal. Docs: https://code.claude.com/docs/en/agent-teams.md

Task: $ARGUMENTS

Step 1: Context Gathering (Silent — no user interaction)

A) Read environment:

  • CLAUDE.md — workflow rules, conventions, constraints
  • Project manifests — package.json, pyproject.toml, Cargo.toml, go.mod, etc.
  • Directory structure — src/, app/, packages/, test dirs, monorepo indicators

B) Inventory existing agents:

  • Scan ~/.claude/agents/*.md — reuse matching agents instead of creating duplicates

C) Analyze task complexity:

  • Work type: research, implementation, review, debugging, refactoring
  • Scope: single-layer vs cross-layer
  • Parallelism: can work split into independent streams?
  • Complexity → team size: simple (2 agents), medium (3-4), complex cross-cutting (5-6, max 8)

Step 2: Ask Team Questions (AskUserQuestion Tool)

Ask 3-5 questions based on Step 1 findings. Not all apply every time — pick what matters.

  1. Team Composition — "For this [work type] on [stack], I'm thinking [N] agents: [role list]. What would you change?" Options: Perfect / Add role / Remove role / Different approach
  2. Coordination — "How should agents work together?" Options: Independent (no messaging) / Team (peer messaging) / Hub-spoke (lead coordinates)
  3. Dependencies — "Work order?" Options: All parallel / Sequential (A→B→C) / Mixed
  4. Models — "Model allocation: opus (research), sonnet (implementation), haiku (scanning). Adjust?" Options: As suggested / All opus / All sonnet / Custom
  5. Agent Reuse *(only if matching agents found in Step 1B)* — "Found existing [agent-name] that handles [capability]. Reuse it?" Options: Reuse / Create fresh / Both

Step 3: Output & Approval

Present clean TEAM PLAN:

## TEAM PLAN

Task: [description]
Pattern: [independent | team | hub-spoke]
Work Order: [parallel | sequential | mixed]
Agents: [count]

### Teammates

- Teammate 1: [Name] ([Role])
  Description: [1-2 line expertise and specialization]
  Model: [opus|sonnet|haiku]
  Type: [general-purpose | feature-dev:code-X | reuse ~/.claude/agents/X.md]
  Responsible for: [specific deliverable]
  Depends on: [— | Teammate N]

- Teammate 2: [Name] ([Role])
  Description: [1-2 line expertise and specialization]
  Model: [opus|sonnet|haiku]
  Type: [general-purpose | feature-dev:code-X]
  Responsible for: [specific deliverable]
  Depends on: [— | Teammate N]

- ...

### Research (injected into agent prompts)
- [key finding or best practice 1]
- [key finding or best practice 2]

Final Approval (AskUserQuestion Tool)

"Launch this team?"
- Deploy & Save — spawn agents and save as reusable skill
- Deploy Once — spawn agents, one-time
- Adjust — change something (iterate plan)
- Cancel — abort

Deploy & Save → save team as skill at ~/.claude/skills/<team-name>/SKILL.md for future use via /<team-name> [task]. Saved skill skips planning, bakes in agent definitions, uses $ARGUMENTS for task input.

Adjust → ask what to change → regenerate plan → ask again. Loop until approved.

Deploy Once → spawn immediately, no save.

Cancel → stop.

Spawning Execution

Based on chosen pattern:

  • Independent: parallel Task tool calls, one per agent
  • Team: TeamCreate → TaskCreate per agent → Task tool with team_name → TaskUpdate for dependencies
  • Hub-spoke: TeamCreate with lead agent (opus) that delegates via SendMessage

For detailed agent prompt structure, see references/agent-prompts.md.

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

35.96%
按下载量换算103

Claude

31.13%
按下载量换算89

Cursor

20.2%
按下载量换算58

Gemini CLI

8.87%
按下载量换算25

安全审计

Gen Agent Trust Hub

可疑

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/octaviusp/spawning-plan-skill --skill spawning-plan 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 warning/failed 结果,不能写成本站确认安全。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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