Token导航 LogoToken导航

Agent Skill · 研究检索

skillful-subagent-creator(熟练的子 Agent 创建者)

skillful-subagent-creator 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。

skillful-subagent-creator 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,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 38,879。

总安装 1,498周安装 60GitHub Stars 98下载 485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
unknown

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:skillful-subagent-creator(熟练的子 Agent 创建者)
来源仓库:https://github.com/erichowens/some_claude_skills
仓库路径:skills/skillful-subagent-creator
安装命令:
npx skills add https://github.com/erichowens/some_claude_skills --skill skillful-subagent-creator
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/erichowens/some_claude_skills --skill skillful-subagent-creator

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。

用于查找、检索和筛选相关信息。适合在子 Agent 创建场景中快速定位候选结果。可通过来源仓库和 README 核验具体用法。

安装命令:npx skills add https://github.com/erichowens/some_claude_skills --skill skillful-subagent-creator。建议确认权限范围及是否会触发联网或文件读写。

来源仓库内路径:skills/skillful-subagent-creator

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/erichowens/some_claude_skills --skill skillful…;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

    它需要调用外部服务,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

skillful-subagent-creator 怎么安装?

在终端执行 npx skills add https://github.com/erichowens/some_claude_skills --skill skillful-subagent-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skillful-subagent-creator 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。

skillful-subagent-creator 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

skillful-subagent-creator 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

skillful-subagent-creator 用的人多吗?

公开数据是累计安装 1,498 次,近一周安装 60 次,下载 485 次,GitHub 98 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。

skillful-subagent-creator 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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

  • 需要明确授权才能用到公司项目里的场景。
  • 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
skillful-subagent-creator

熟练的子 Agent 创建者 · 本页

1,49898调用外部服务未明确通过

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

Skillful Subagent Creator

Creates Claude subagents that are equipped with curated skills as their standard operating procedures. Each subagent is "a specialist with a toolkit" — a narrow role, a small skill set, and a clear workflow for applying those skills.


When to Use

✅ Use for:

  • Designing a specialist subagent for a specific domain
  • Selecting which skills to attach to a subagent (2-5 core skills)
  • Writing the 4-section subagent prompt (Identity, Skills, Task Loop, Constraints)
  • Wiring subagents into DAG orchestration workflows
  • Defining input/output contracts between DAG nodes

❌ NOT for:

  • Creating the skills themselves (use skill-architect)
  • Single-agent prompting without skills
  • General Claude Code features or MCP setup

Subagent Creation Process

flowchart TD
  A[Define the role] --> B[Select 2-5 skills]
  B --> C[Write 4-section prompt]
  C --> D[Define input/output contracts]
  D --> E[Choose DAG position]
  E --> F{Standalone or chained?}
  F -->|Standalone| G[Test with sample task]
  F -->|Chained| H[Define handoff protocol]
  H --> G
  G --> I{Works correctly?}
  I -->|No| C
  I -->|Yes| J[Deploy]

Step 1: Define the Role

A subagent's role must be narrow enough to be expert, broad enough to be useful.

flowchart TD
  A{How narrow?} -->|Too broad| B["Do all marketing" ❌]
  A -->|Right| C["Draft landing page copy" ✅]
  A -->|Too narrow| D["Fix typos in H1 tags" ❌]

Test: Can you describe what this subagent does in one sentence with a specific verb and noun? If not, narrow it.

Examples of well-scoped roles:

RoleDomainSentence
RefactorerTypeScript"Designs and executes safe refactors in TypeScript monorepos"
PR ReviewerCode review"Reviews pull request diffs for correctness, style, and security"
Research SynthesizerResearch"Synthesizes multi-source research into cited briefing documents"
Migration PlannerDatabases"Plans database schema migrations with rollback strategies"

Step 2: Select Skills (2-5)

Skills are the subagent's standard operating procedures. Too few and it improvises; too many and it drowns in context.

Selection Criteria

flowchart TD
  A{For each candidate skill} --> B{Needed for >50% of tasks?}
  B -->|Yes| C[Preload into context]
  B -->|No| D{Needed for 10-50%?}
  D -->|Yes| E[Add to dynamic catalog]
  D -->|No| F[Don't include]

Three Loading Tiers

TierSkillsHow LoadedToken Cost
Preloaded2-5 core skillsFull SKILL.md injected into system prompt~2-5k tokens per skill
Catalog5-20 available skillsName + 1-line description in prompt; loaded on demand~50 tokens per skill
NoneEverything elseNot mentioned; agent uses generic reasoning0 tokens

Example Skill Selection

Role: PR Reviewer for TypeScript/React

TierSkillWhy
Preloadedcode-review-skillCore to every task
Preloadedreact-server-componentsCatches RSC anti-patterns
Catalogtypescript-strict-modeSometimes relevant
Catalogtesting-patternsOnly for test file reviews
Nonedatabase-migrationNot in scope

Step 3: Write the 4-Section Prompt

Every subagent prompt has exactly four sections. This structure ensures the subagent knows its role, its tools, its workflow, and its constraints.

Section 1: Identity

You are the **[Role Name]** subagent for this system.
You handle [narrow domain of tasks].
When a task is outside this scope, explicitly say so and
ask the orchestrator for a different agent.

Keep it to 2-4 sentences. Name the specific domain. State the boundary explicitly.

Section 2: Skill Usage Rules

You have access to the following skills, which define your methods:
- `skill-a`: [1-line purpose]
- `skill-b`: [1-line purpose]
- `skill-c`: [1-line purpose]

These are your standard operating procedures, not optional hints.
When tackling a task:
1. Decide which skill(s) apply
2. Follow their step-by-step workflow
3. Use their output formats and checklists
4. Reference skill steps by number as you work

Section 3: Task-Handling Loop

For each task you receive:
1. Restate the task in your own words
2. Select one or more skills that fit. If none fit well, say so.
3. If needed, ask 2-5 clarifying questions
4. Produce a short internal plan
5. Execute the skill workflow step by step
6. Run any validation/QA steps from the skill
7. Return:
   (a) Final artifacts
   (b) Which skills you used (and which steps)
   (c) Assumptions and remaining risks

Section 4: Constraints

Quality bar: [e.g., "Never knowingly leave tests failing"]
Safety: [e.g., "No destructive operations without confirmation"]
Tie-breaking: [e.g., "If speed vs robustness conflict, pick robustness"]
Output format: [e.g., "Always return valid JSON matching the output contract"]

Step 4: Define Input/Output Contracts

Every subagent must have explicit contracts so orchestrators and downstream agents can work with it.

Input Contract

{
  "task": "string — description of what to do",
  "files": ["string — paths to relevant files"],
  "context": "string — prior agent output or user requirements",
  "constraints": {
    "time_budget": "string — e.g., '5 minutes'",
    "quality_bar": "string — e.g., 'production-ready'"
  }
}

Output Contract

{
  "status": "pass | warn | fail",
  "artifacts": ["string — files created or modified"],
  "summary": "string — 1-3 sentence description of what was done",
  "skills_used": ["string — skill names and step numbers"],
  "risks": ["string — remaining risks or assumptions"],
  "metadata": {
    "duration_ms": "number",
    "tokens_used": "number"
  }
}

Step 5: Wire into DAG Workflows

Orchestration Patterns

flowchart TD
  subgraph "Single Specialist"
    O1[Orchestrator] --> S1[Specialist]
    S1 --> O1
  end

  subgraph "Chain"
    O2[Orchestrator] --> C1[Designer]
    C1 --> C2[Implementer]
    C2 --> C3[Tester]
    C3 --> O2
  end

  subgraph "Fan-out / Fan-in"
    O3[Orchestrator] --> P1[Auth Agent]
    O3 --> P2[Billing Agent]
    O3 --> P3[UI Agent]
    P1 --> M[Merger]
    P2 --> M
    P3 --> M
    M --> O3
  end

Pattern Selection

PatternWhen to UseSkill Requirement
Single SpecialistTask maps to one domainAgent has 2-5 preloaded skills
ChainSequential transformation pipelineEach agent has different skills; output of A feeds input of B
Fan-out / Fan-inIndependent parallel workAgents work concurrently; merger resolves conflicts
LoopIterative refinementSame agent re-runs with feedback until quality bar met
Human-in-the-LoopApproval requiredAgent produces draft; human reviews; agent revises

DAG Node Definition

Each node in a DAG is a subagent with:

node:
  id: review-pr
  agent: pr-reviewer
  skills: [code-review-skill, react-server-components]
  input_from: [parse-diff]
  output_to: [merge-decision]
  retry: 2
  timeout: 300s
  on_failure: escalate-to-human

Step 6: Test

Test Protocol

  1. Happy path: Give the subagent a task that matches its skills perfectly
  2. Edge case: Give a task at the boundary of its scope
  3. Out of scope: Give a task outside its domain — it should refuse and say so
  4. Skill coverage: Verify it uses the attached skills, not ad-hoc reasoning
  5. Output contract: Verify output matches the JSON schema exactly

Red Flags

  • Agent invents processes instead of following skills → Skill usage rules not strong enough
  • Agent tries to handle out-of-scope tasks → Identity section not clear enough
  • Output doesn't match contract → Add explicit output format to constraints
  • Agent loads all references eagerly → Add lazy-loading instruction to prompt

Complete Example: PR Reviewer Subagent

Config

name: pr-reviewer
role: "Reviews TypeScript/React pull requests for correctness, style, and security"
skills:
  preloaded:
    - code-review-skill
    - react-server-components
  catalog:
    - typescript-strict-mode
    - testing-patterns
tools: [Read, Grep, Glob]
input_from: [diff-parser]
output_to: [merge-decision]

Prompt

You are the **PR Reviewer** subagent. You review TypeScript and React
pull request diffs for correctness, readability, performance, and security.
You do NOT implement features, fix bugs, or write new code. If asked to do
so, say "This is outside my scope — route to the Implementer agent."

Your skills define your standard process:
- `code-review-skill`: Structured review methodology (correctness → style → perf → security)
- `react-server-components`: Catches RSC anti-patterns ('use client' overuse, async component errors)

For each PR you receive:
1. Restate what the PR changes and why
2. Select applicable skills (always code-review-skill; add react-server-components if React files changed)
3. Follow the skill's review steps in order
4. Produce findings in the output contract format
5. Summarize: approve, request changes, or block

Quality bar: Never approve a PR with failing tests or security vulnerabilities.
Tie-breaking: If unsure whether something is a bug or style preference, flag it as "suggestion" not "required."
Output: Always return JSON matching the output contract schema.

Anti-Patterns

Skill-Less Subagent

Wrong: Creating a subagent with just a role description and no skills. Why: It will improvise a new process every time instead of following a consistent methodology. Fix: Attach 2-5 skills as standard operating procedures.

Skill Overload

Wrong: Attaching 15 skills to one subagent. Why: Blows context window; agent spends tokens selecting skills instead of executing. Fix: Max 5 preloaded, rest in catalog. Let the orchestrator pre-filter.

Missing Output Contract

Wrong: Subagent returns free-form text that downstream agents can't parse. Why: DAG breaks because the next node can't consume the output. Fix: Define explicit JSON output contract. Validate in tests.

Scope Creep

Wrong: Subagent tries to handle everything instead of refusing out-of-scope tasks. Why: Identity section doesn't state boundaries clearly enough. Fix: Add "You do NOT handle X, Y, Z. If asked, say so and suggest the right agent."

平台分布

Codex

37.12%
按下载量换算180

Claude

29.48%
按下载量换算143

Cursor

19.38%
按下载量换算94

Gemini CLI

10.03%
按下载量换算49

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills