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Agent Skill · 研究检索

agents-subagents(Agent 子 Agent)

agents-subagents 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。

agents-subagents 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 39,386。

总安装 1,420周安装 61GitHub Stars 59下载 498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:agents-subagents(Agent 子 Agent)
来源仓库:https://github.com/vasilyu1983/ai-agents-public
仓库路径:skills/agents-subagents
安装命令:
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill agents-subagents
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/vasilyu1983/ai-agents-public --skill agents-subagents

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。

agents-subagents 创建和管理 Claude Code 的子代理,定义职责和工具权限。通过 YAML 配置名称、描述、工具集等参数,实现最小权限分配。支持测试迭代和钩子集成,适合复杂任务的分工协作场景。会创建新文件并可能调用外部工具,建议限制初始权限范围。

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/agents-subagents

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/vasilyu1983/ai-agents-public --skill agents-su…;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

agents-subagents 怎么安装?

在终端执行 npx skills add https://github.com/vasilyu1983/ai-agents-public --skill agents-subagents。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

agents-subagents 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。

agents-subagents 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/vasilyu1983/ai-agents-public --skill agents-subagents 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。

agents-subagents 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。

agents-subagents 用的人多吗?

公开数据是累计安装 1,420 次,近一周安装 61 次,下载 498 次,GitHub 59 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。

agents-subagents 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

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
agents-subagents

Agent 子 Agent · 本页

1,42059执行命令未明确通过

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

Claude Code Agents

Create and maintain Claude Code agents/subagents with predictable behavior, least-privilege tools, and explicit delegation contracts.

Quick Start

  1. Create an agent file at .claude/agents/<agent-name>.md (kebab-case filename).
  2. Add YAML frontmatter (required: name, description; optional: tools, model, permissionMode, skills, hooks).
  3. Write the agent prompt: responsibilities, workflow, and an output contract.
  4. Minimize tools: start read-only, then add only what the agent truly needs.
  5. Test on a real task and iterate.

Minimal template:

---
name: sql-optimizer
description: Optimize SQL queries, explain tradeoffs, and propose safe indexes
tools: Read, Grep, Glob
model: sonnet
---

# SQL Optimizer

## Responsibilities
- Diagnose bottlenecks using query shape and plans when available
- Propose optimizations with risks and expected impact

## Workflow
1. Identify the slow path and data volume assumptions
2. Propose changes (query rewrite, indexes, stats) with rationale
3. Provide a verification plan

## Output Contract
- Summary (1–3 bullets)
- Recommendations (ordered)
- Verification (commands/tests to run)

Workflow (2026)

  1. Define the agent’s scope and success criteria.
  2. Choose a model based on risk, latency, and cost (default to sonnet for most work).
  3. Choose tools via least privilege; avoid granting Edit/Write unless required.
  4. If delegating with Task, define a handoff contract (inputs, constraints, output format).
  5. Add safety rails for destructive actions and secrets.
  6. Add a verification step (checklist, tests, or a dedicated verifier agent).

Frontmatter Fields (Summary)

  • name (REQUIRED): kebab-case; match filename (without .md).
  • description (REQUIRED): state when to invoke + what it does; include keywords users will say.
  • tools (OPTIONAL): explicit allow-list; prefer small, purpose-built sets.
  • model (OPTIONAL): haiku for fast checks, sonnet for most tasks, opus for high-stakes reasoning, inherit to match parent.
  • permissionMode (OPTIONAL): prefer defaults; change only with a clear reason and understand the tradeoffs.
  • skills (OPTIONAL): preload skill packs for domain expertise; keep the list minimal.
  • hooks (OPTIONAL): automate guardrails; prefer using the hooks skill for patterns and safety.

For full tool semantics and permission patterns, use references/agent-tools.md. For orchestration and anti-patterns, use references/agent-patterns.md.

2026 Best Practices (Domain Expertise)

  • Use small, specialized agents; avoid “god agents”.
  • Keep agent prompts short; put repo conventions in CLAUDE.md/project memory and domain knowledge in skills.
  • Budget context: pass file paths, minimal snippets, and constraints; avoid dumping long logs/code.
  • Use explicit handoffs for subagents: “Goal / Constraints / Inputs / Output Contract”.
  • Add a verifier step for risky changes (security, migrations, infra, auth).
  • Treat CLI fields/features as moving; verify against official docs in data/sources.json.

Validation Checklist

  • Frontmatter: name matches filename; description is single-line and trigger-oriented; tools are minimal; model fits risk.
  • Prompt: responsibilities are concrete; workflow is actionable; output contract is explicit.
  • Delegation: subagent briefs are specific and bounded; orchestrator verifies integration.
  • Safety: confirm destructive ops; avoid secrets/PII; follow repository policies.

Navigation

  • frameworks/shared-skills/skills/agents-subagents/references/agent-patterns.md
  • frameworks/shared-skills/skills/agents-subagents/references/agent-tools.md
  • frameworks/shared-skills/skills/agents-subagents/references/subagent-interruption-recovery.md
  • frameworks/shared-skills/skills/agents-subagents/data/sources.json
  • frameworks/shared-skills/skills/agents-skills/SKILL.md
  • frameworks/shared-skills/skills/agents-hooks/SKILL.md

Subagent Interruption Recovery Protocol

Interruptions are normal in multi-agent runs. Treat them as recoverable state transitions, not total failures.

Recovery Loop

  1. Capture partial output from interrupted agent.
  2. Classify interruption cause (manual redirect, timeout, context overflow, tool error).
  3. Decide resume strategy:

- resume same agent with narrowed scope, or - spawn replacement agent with explicit handoff from checkpoint.

  1. Prevent duplicate work by marking completed subtasks before rerun.
  2. Re-verify integration assumptions after recovery.

Required Checkpoint Fields

  • completed work
  • pending work
  • owned files
  • unresolved blocker
  • next exact command/task

Anti-Pattern

Do not restart full fan-out blindly after one interruption. Resume the smallest affected unit first.

Operational Guardrails: Subagent Orchestration

Use these defaults unless the user explicitly asks for wider fan-out.

Worktree Isolation

For parallel subagent execution, use one Git worktree per agent to prevent file conflicts and index lock contention. See AI Agent Worktrees for setup, directory conventions, safety patterns, and cleanup.

Hard Limits

  • Keep active subagents <= 3.
  • Keep each subagent scope to one responsibility and a bounded file set.
  • Do not let multiple subagents edit the same file in parallel.
  • Use one worktree per subagent when running parallel agents locally.

Handoff Template (Standard)

Goal:
Constraints:
Owned files:
Do-not-touch files:
Output format:
Definition of done:

Context-Rich Handoff Template (For Parallel/Swarm Execution)

When dispatching multiple subagents from a plan, front-load each agent with structured context. This reduces token usage, tool calls, and drift.

## Context
- Plan: [plan filename or path]
- Goals: [relevant overview from plan — what this task achieves]
- Dependencies: [prerequisite tasks + their outputs/files]
- Related tasks: [sibling tasks and their function]

## Scope
- Files to create/modify: [full paths]
- Files to read (not modify): [paths for reference only]
- Do-not-touch: [files owned by other agents]

## Acceptance Criteria
- [Criterion 1]
- [Criterion 2]
- [Test/verification command]

## Implementation Steps
1. Read the plan at [path] for full context
2. [Concrete step]
3. [Concrete step]
4. Verify: [specific check]

Why this works: Subagents have no prior context. Without front-loaded detail, they spend tokens rediscovering the codebase. With it, they execute focused work immediately.

Wave Dispatch Protocol

When executing plans with dependency graphs, use waves:

  1. Read the dependency graph from the plan.
  2. Identify all tasks with no unmet dependencies (Wave 1).
  3. Launch one subagent per unblocked task (using context-rich handoff template).
  4. Wait for all agents in the wave to complete.
  5. Validate each agent's output before proceeding.
  6. Identify newly unblocked tasks → launch next wave.
  7. Repeat until all tasks complete.

Single-wave shortcut: If only one task is unblocked, launch one agent. Don't force parallelism.

Merge Discipline

  1. Wait for subagent outputs.
  2. Review for overlap/conflicts.
  3. Integrate one subagent result at a time.
  4. Run verification gates before final synthesis.

Conflict Resolution (Parallel Outputs)

When parallel agents produce conflicting changes:

  1. Detect: Check for overlapping file edits, incompatible interface changes, or divergent assumptions.
  2. Prioritize: The agent working on the dependency (upstream task) takes priority for shared interfaces.
  3. Resolve: The orchestrator (not subagents) reconciles conflicts — it has the full plan context.
  4. Re-run if needed: If conflict resolution invalidates a task's output, re-dispatch that single task with updated context.
  5. Document: Record the conflict and resolution in the plan for traceability.

Stop Conditions

Stop and re-plan when:

  • two subagents propose conflicting edits to same module,
  • repeated retries happen without new evidence,
  • context window starts dropping prior decisions,
  • conflict resolution would require re-running more than half the completed tasks.

Fact-Checking

  • Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
  • Prefer primary sources; report source links and dates for volatile information.
  • If web access is unavailable, state the limitation and mark guidance as unverified.

平台分布

Codex

33.27%
按下载量换算166

Claude

29.88%
按下载量换算149

Cursor

20.57%
按下载量换算102

Gemini CLI

9.54%
按下载量换算48

安全审计

Gen Agent Trust Hub

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/vasilyu1983/ai-agents-public --skill agents-subagents 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。目前只有一个公开来源,建议同时核对源仓库。

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