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skillful-subagent-creator熟练的子 Agent 创建者

Agent Skill

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。

总安装

1,498

周安装

60

GitHub Stars

98

下载量

485
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: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

简介

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。

SKILL.md

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."

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

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

external-service

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。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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