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agents-subagentsAgent 子 Agent

Agent Skill

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。

总安装

1,420

周安装

61

GitHub Stars

59

下载量

498
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: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

简介

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。

SKILL.md

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.

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

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。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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