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agent-developmentAgent 开发

Agent Skill

agent-development 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

100,837

周安装

4,244

GitHub Stars

8

下载量

35,310
OpenClaw

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

MIT-0

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:agent-development(Agent 开发)
来源仓库:https://github.com/veeramanikandanr48/agent-development
安装命令:
openclaw skills install agent-development
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install agent-development

简介

设计和构建具有有效描述、工具访问模式的自定义 Claude Code 代理, 和自我记录提示。涵盖任务工具委托、模型选择、内存限制、

  • 和陈述性指令设计。
  • 使用场合:创建自定义代理、设计自动委派的代理描述、
  • 解决代理内存问题或构建代理管道。

SKILL.md

name
agent-development
description
|
license
MIT

Agent Development for Claude Code

Build effective custom agents for Claude Code with proper delegation, tool access, and prompt design.

Agent Description Pattern

The description field determines whether Claude will automatically delegate tasks.

Strong Trigger Pattern

---
name: agent-name
description: |
  [Role] specialist. MUST BE USED when [specific triggers].
  Use PROACTIVELY for [task category].
  Keywords: [trigger words]
tools: Read, Write, Edit, Glob, Grep, Bash
model: sonnet
---

Weak vs Strong Descriptions

Weak (won't auto-delegate)Strong (auto-delegates)
"Analyzes screenshots for issues""Visual QA specialist. MUST BE USED when analyzing screenshots. Use PROACTIVELY for visual QA."
"Runs Playwright scripts""Playwright specialist. MUST BE USED when running Playwright scripts. Use PROACTIVELY for browser automation."

Key phrases:

  • "MUST BE USED when..."
  • "Use PROACTIVELY for..."
  • Include trigger keywords

Delegation Mechanisms

  1. Explicit: Task tool subagent_type: "agent-name" - always works
  2. Automatic: Claude matches task to agent description - requires strong phrasing

Session restart required after creating/modifying agents.

Tool Access Principle

If an agent doesn't need Bash, don't give it Bash.

Agent needs to...Give toolsDon't give
Create files onlyRead, Write, Edit, Glob, GrepBash
Run scripts/CLIsRead, Write, Edit, Glob, Grep, Bash
Read/audit onlyRead, Glob, GrepWrite, Edit, Bash

Why? Models default to cat > file << 'EOF' heredocs instead of Write tool. Each bash command requires approval, causing dozens of prompts per agent run.

Allowlist Pattern

Instead of restricting Bash, allowlist safe commands in .claude/settings.json:

{
  "permissions": {
    "allow": [
      "Write", "Edit", "WebFetch(domain:*)",
      "Bash(cd *)", "Bash(cp *)", "Bash(mkdir *)", "Bash(ls *)",
      "Bash(cat *)", "Bash(head *)", "Bash(tail *)", "Bash(grep *)",
      "Bash(diff *)", "Bash(mv *)", "Bash(touch *)", "Bash(file *)"
    ]
  }
}

Model Selection (Quality First)

Don't downgrade quality to work around issues - fix root causes instead.

ModelUse For
OpusCreative work (page building, design, content) - quality matters
SonnetMost agents - content, code, research (default)
HaikuOnly script runners where quality doesn't matter

Memory Limits

Root Cause Fix (REQUIRED)

Add to ~/.bashrc or ~/.zshrc:

export NODE_OPTIONS="--max-old-space-size=16384"

Increases Node.js heap from 4GB to 16GB.

Parallel Limits (Even With Fix)

Agent TypeMax ParallelNotes
Any agents2-3Context accumulates; batch then pause
Heavy creative (Opus)1-2Uses more memory

Recovery

  1. source ~/.bashrc or restart terminal
  2. NODE_OPTIONS="--max-old-space-size=16384" claude
  3. Check what files exist, continue from there

Sub-Agent vs Remote API

Always prefer Task sub-agents over remote API calls.

AspectRemote API CallTask Sub-Agent
Tool accessNoneFull (Read, Grep, Write, Bash)
File readingMust pass all content in promptCan read files iteratively
Cross-referencingSingle context windowCan reason across documents
Decision qualityGeneric suggestionsSpecific decisions with rationale
Output quality~100 lines typical600+ lines with specifics
// ❌ WRONG - Remote API call
const response = await fetch('https://api.anthropic.com/v1/messages', {...})

// ✅ CORRECT - Use Task tool
// Invoke Task with subagent_type: "general-purpose"

Declarative Over Imperative

Describe what to accomplish, not how to use tools.

Wrong (Imperative)

### Check for placeholders

grep -r "PLACEHOLDER:" build/*.html

Right (Declarative)

### Check for placeholders
Search all HTML files in build/ for:
- PLACEHOLDER: comments
- TODO or TBD markers
- Template brackets like [Client Name]

Any match = incomplete content.

What to Include

IncludeSkip
Task goal and contextExplicit bash/tool commands
Input file paths"Use X tool to..."
Output file paths and formatStep-by-step tool invocations
Success/failure criteriaShell pipeline syntax
Blocking checks (prerequisites)Micromanaged workflows
Quality checklists

Self-Documentation Principle

"Agents that won't have your context must be able to reproduce the behaviour independently."

Every improvement must be encoded into the agent's prompt, not left as implicit knowledge.

What to Encode

DiscoveryWhere to Capture
Bug fix patternAgent's "Corrections" or "Common Issues" section
Quality requirementAgent's "Quality Checklist" section
File path conventionAgent's "Output" section
Tool usage patternAgent's "Process" section
Blocking prerequisiteAgent's "Blocking Check" section

Test: Would a Fresh Agent Succeed?

Before completing any agent improvement:

  1. Read the agent prompt as if you have no context
  2. Ask: Could a new session follow this and produce the same quality?
  3. If no: Add missing instructions, patterns, or references

Anti-Patterns

Anti-PatternWhy It Fails
"As we discussed earlier..."No prior context exists
Relying on files read during devAgent may not read same files
Assuming knowledge from errorsAgent won't see your debugging
"Just like the home page"Agent hasn't built home page

Agent Prompt Structure

Effective agent prompts include:

## Your Role
[What the agent does]

## Blocking Check
[Prerequisites that must exist]

## Input
[What files to read]

## Process
[Step-by-step with encoded learnings]

## Output
[Exact file paths and formats]

## Quality Checklist
[Verification steps including learned gotchas]

## Common Issues
[Patterns discovered during development]

Pipeline Agents

When inserting a new agent into a numbered pipeline (e.g., HTML-01HTML-05HTML-11):

Must UpdateWhat
New agent"Workflow Position" diagram + "Next" field
Predecessor agentIts "Next" field to point to new agent

Common bug: New agent is "orphaned" because predecessor still points to old next agent.

Verification:

grep -n "Next:.*→\|Then.*runs next" .claude/agents/*.md

The Sweet Spot

Best use case: Tasks that are repetitive but require judgment.

Example: Auditing 70 skills manually = tedious. But each audit needs intelligence (check docs, compare versions, decide what to fix). Perfect for parallel agents with clear instructions.

Not good for:

  • Simple tasks (just do them)
  • Highly creative tasks (need human direction)
  • Tasks requiring cross-file coordination (agents work independently)

Effective Prompt Template

For each [item]:
1. Read [source file]
2. Verify with [external check - npm view, API call, etc.]
3. Check [authoritative source]
4. Score/evaluate
5. FIX issues found ← Critical instruction

Key elements:

  • "FIX issues found" - Without this, agents only report. With it, they take action.
  • Exact file paths - Prevents ambiguity
  • Output format template - Ensures consistent, parseable reports
  • Batch size ~5 items - Enough work to be efficient, not so much that failures cascade

Workflow Pattern

1. ME: Launch 2-3 parallel agents with identical prompt, different item lists
2. AGENTS: Work in parallel (read → verify → check → edit → report)
3. AGENTS: Return structured reports (score, status, fixes applied, files modified)
4. ME: Review changes (git status, spot-check diffs)
5. ME: Commit in batches with meaningful changelog
6. ME: Push and update progress tracking

Why agents don't commit: Allows human review, batching, and clean commit history.

Signs a Task Fits This Pattern

Good fit:

  • Same steps repeated for many items
  • Each item requires judgment (not just transformation)
  • Items are independent (no cross-item dependencies)
  • Clear success criteria (score, pass/fail, etc.)
  • Authoritative source exists to verify against

Bad fit:

  • Items depend on each other's results
  • Requires creative/subjective decisions
  • Single complex task (use regular agent instead)
  • Needs human input mid-process

Quick Reference

Agent Frontmatter Template

---
name: my-agent
description: |
  [Role] specialist. MUST BE USED when [triggers].
  Use PROACTIVELY for [task category].
  Keywords: [trigger words]
tools: Read, Write, Edit, Glob, Grep, Bash
model: sonnet
---

Fix Bash Approval Spam

  1. Remove Bash from tools if not needed
  2. Put critical instructions FIRST (right after frontmatter)
  3. Use allowlists in .claude/settings.json

Memory Crash Recovery

export NODE_OPTIONS="--max-old-space-size=16384"
source ~/.bashrc && claude

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

77.41%
按下载量换算27,333

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

未展示

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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