Token导航 LogoToken导航

Agent Skill · 研究检索

custom-agent(定制 Agent)

custom-agent 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。

custom-agent 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 67,439。

总安装 245周安装 10GitHub Stars 6下载 78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:custom-agent(定制 Agent)
来源仓库:https://github.com/duc01226/easyplatform
仓库路径:skills/custom-agent
安装命令:
npx skills add https://github.com/duc01226/easyplatform --skill custom-agent
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/duc01226/easyplatform --skill custom-agent

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。

custom-agent 提供定制 AI 代理的任务分解与执行框架,强调分步处理和证据引用。适用于复杂问题拆解和多文件协同处理的场景。使用前应将任务拆分为小单元,防止上下文溢出。所有断言需有来源支撑,不确定时明确声明置信度。

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/custom-agent

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/duc01226/easyplatform --skill custom-agent;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

custom-agent 怎么安装?

在终端执行 npx skills add https://github.com/duc01226/easyplatform --skill custom-agent。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

custom-agent 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。

custom-agent 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。

custom-agent 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。

custom-agent 用的人多吗?

公开数据是累计安装 245 次,近一周安装 10 次,下载 78 次,GitHub 6 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。

custom-agent 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
custom-agent

定制 Agent · 本页

2456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

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.
Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.
AI Mistake Prevention — Failure modes to avoid on every task: - Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. - Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. - Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. - Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. - When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. - Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. - Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. - Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. - Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. - Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Quick Summary

Goal: Create new custom agents, audit existing agent quality, or enhance agent definitions.

Workflow: Detect mode (Create/Audit/Enhance) from $ARGUMENTS → Execute → Validate

Key Rules:

  • Agent files: .claude/agents/{name}.md with YAML frontmatter + markdown body as system prompt
  • Agent does NOT inherit Claude Code system prompt — write complete instructions
  • Minimize tools to only what the agent needs
  • System prompt structure: ## Role → ## Workflow → ## Key Rules → ## Output

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Modes

ModeTriggerAction
Create$ARGUMENTS describes a new agentCreate agent file
Auditmentions verify, audit, review, check, qualityAudit existing agents
Enhancementions refactor, enhance, improve, optimizeImprove existing agent

Mode 1: Create Agent

  1. Clarify — AskUserQuestion: purpose, read-only vs read-write, model preference, memory needs
  2. Check Existing — Glob .claude/agents/*.md for similar agents. Avoid duplication.
  3. Scaffold — Create .claude/agents/{name}.md using frontmatter template below
  4. Write System Prompt — Structure: ## Role → ## Workflow → ## Key Rules → ## Output
  5. Validate — Run audit checklist below

Mode 2: Audit Agents

  1. Discover — Glob .claude/agents/*.md
  2. Parse — Read first 30 lines of each, extract frontmatter
  3. Validate — Check each audit rule below
  4. Report — Issues grouped by severity (Error > Warning > Info), include quality scores
  5. Fix — If user confirms, fix Error-level issues automatically

Mode 3: Enhance Agent

  1. Read — Load specified agent file
  2. Analyze — Check against best practices and audit checklist
  3. Recommend — List improvements with rationale
  4. Apply — If user confirms, apply enhancements

Agent Frontmatter Schema

---
# REQUIRED
name: my-agent # Lowercase + hyphens only
description: >- # Claude uses this to decide when to delegate
    Use this agent when [specific trigger scenarios].

# OPTIONAL — Tools
tools: Read, Grep, Glob, Bash # Allowlist (omit both → inherits all)
disallowedTools: Write, Edit # Denylist (removes from inherited set)
# Task(agent1, agent2) restricts spawnable subagents

# OPTIONAL — Model
model: inherit # inherit | sonnet | opus | haiku

# OPTIONAL — Permissions
permissionMode: default # default | acceptEdits | dontAsk | bypassPermissions | plan

# OPTIONAL — Skills (content injected at startup)
skills:
    - skill-name

# OPTIONAL — MCP Servers
mcpServers:
    - server-name

# OPTIONAL — Hooks (scoped to this agent)
hooks:
    PreToolUse:
        - matcher: 'Bash'
          hooks:
              - type: command
                command: './scripts/validate.sh'

# OPTIONAL — Memory (MEMORY.md auto-injected, Read/Write/Edit auto-added)
memory: project # user (~/.claude/agent-memory/) | project (.claude/agent-memory/) | local (gitignored)

# OPTIONAL — Execution
background: false # true = always background task
isolation: worktree # Run in temporary git worktree
---

Tool Restriction Patterns

Agent TypeRecommended tools
Explorer/ScoutRead, Grep, Glob, Bash
Reviewer (read-only)Read, Grep, Glob
Writer/ImplementerRead, Write, Edit, Grep, Glob, Bash
ResearcherRead, Grep, Glob, WebFetch, WebSearch
OrchestratorRead, Grep, Glob, Task(sub1, sub2)

Available tools: Read, Write, Edit, MultiEdit, Glob, Grep, Bash, WebFetch, WebSearch, Task, NotebookRead, NotebookEdit, TaskCreate, TaskUpdate, AskUserQuestion, + MCP tools.

Model Selection

ModelBest For
haikuFast read-only: scanning, search, file listing
sonnetBalanced: code review, debugging, analysis
opusHigh-stakes: architecture, complex implementation. Better quality for code review, debugging, analysis
inheritDefault — match parent's model

Description Best Practices

# BAD — too vague, Claude won't auto-delegate
description: Reviews code

# GOOD — specific trigger conditions
description: >-
  Use this agent for comprehensive code review after implementing features,
  before merging PRs, or when assessing code quality and technical debt.
  • Include "Use this agent when..." phrasing with concrete scenarios
  • Add "use proactively" to encourage auto-invocation

Common Anti-Patterns

Anti-PatternFix
No tool restrictionsAdd tools allowlist
Vague descriptionWrite specific trigger conditions
Giant system promptKeep concise, use skills for detail
Recursive subagentsRestrict Task in tools
Windows long prompts (>8191 chars)Use file-based agents, not --agents CLI

Context Passing

  • Agent receives ONLY its system prompt + task prompt — NOT parent conversation
  • Parent receives ONLY agent's final result — NOT intermediate tool calls
  • This isolation is the primary context management benefit

Audit Checklist

#CheckRuleSeverity
1Frontmatter existsMust have --- delimitersError
2Name present & validLowercase + hyphens onlyError
3Description presentNon-empty, >20 charsError
4No duplicate namesUnique across all agent filesError
5Description qualitySpecific trigger scenariosWarning
6Tools minimalOnly what agent needsWarning
7Prompt structureHas ## Role + ## WorkflowWarning
8Model setWhen task differs from defaultInfo

Quality Score: Valid frontmatter (20) + Description >50 chars (20) + Tools restricted (15) + Role section (15) + Workflow section (10) + Model set (10) = 90. Rating: 80+ Excellent, 60-79 Good, 40-59 Needs Work, <40 Poor.

File Priority (highest first)

  1. --agents CLI flag (session only)
  2. .claude/agents/*.md (project)
  3. ~/.claude/agents/*.md (user)
  4. Plugin agents/ directory

Same name across levels: higher-priority wins. Use claude agents CLI to list all.

Requirements

$ARGUMENTS


IMPORTANT Task Planning Notes (MUST ATTENTION FOLLOW)

  • Always break work into small todo tasks
  • Always add a final review todo task

Closing Reminders

  • MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
  • MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
  • MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
  • MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality
  • MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
  • MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.

平台分布

Codex

37.2%
按下载量换算29

Claude

30.33%
按下载量换算24

Cursor

17.15%
按下载量换算13

Gemini CLI

8.89%
按下载量换算7

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