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 tools	Don't give
Create files only	Read, Write, Edit, Glob, Grep	Bash
Run scripts/CLIs	Read, Write, Edit, Glob, Grep, Bash	—
Read/audit only	Read, Glob, Grep	Write, 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.

Model	Use For
Opus	Creative work (page building, design, content) - quality matters
Sonnet	Most agents - content, code, research (default)
Haiku	Only 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 Type	Max Parallel	Notes
Any agents	2-3	Context accumulates; batch then pause
Heavy creative (Opus)	1-2	Uses 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.

Aspect	Remote API Call	Task Sub-Agent
Tool access	None	Full (Read, Grep, Write, Bash)
File reading	Must pass all content in prompt	Can read files iteratively
Cross-referencing	Single context window	Can reason across documents
Decision quality	Generic suggestions	Specific decisions with rationale
Output quality	~100 lines typical	600+ 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
```bash
grep -r "PLACEHOLDER:" build/*.html

### Right (Declarative)

```markdown
### 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

Include	Skip
Task goal and context	Explicit bash/tool commands
Input file paths	"Use X tool to..."
Output file paths and format	Step-by-step tool invocations
Success/failure criteria	Shell 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

Discovery	Where to Capture
Bug fix pattern	Agent's "Corrections" or "Common Issues" section
Quality requirement	Agent's "Quality Checklist" section
File path convention	Agent's "Output" section
Tool usage pattern	Agent's "Process" section
Blocking prerequisite	Agent'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-Pattern	Why It Fails
"As we discussed earlier..."	No prior context exists
Relying on files read during dev	Agent may not read same files
Assuming knowledge from errors	Agent won't see your debugging
"Just like the home page"	Agent hasn't built home page

Flexibility vs Rigidity

Match specification level to task type. Over-specifying flexible agents makes them brittle.

Task Type	Specification Level	Example
Mechanical/repetitive	High (rigid steps)	Version checker, file copier
Judgment-based	Low (guidelines)	Docs auditor, code reviewer
Creative	Minimal (goals only)	Content writer, brainstormer

Signs You've Over-Specified

• Agent fills in template sections with "N/A"
• Agent tries to complete all phases even when irrelevant
• Scoring systems produce meaningless numbers
• Agent fails when scope doesn't match assumptions
• Long agents (>150 lines) for simple tasks

Flexible Agent Guidelines

DO:

• Describe what to look for, not exact steps
• Provide output examples, not rigid templates
• Include scope control ("if >30 items, ask user")
• Give escape hatches ("if unsure, flag for review")
• Keep under 100 lines for judgment tasks

DON'T:

• Require filling every section of a template
• Create elaborate weighted scoring systems
• List every possible check exhaustively
• Assume scope without asking

Example: Docs Auditor

Over-specified (bad):

## Phase 1: Discovery
Execute Glob for all .md files...

## Phase 6: Generate Report
| Category | Weight | Score | Weighted |
|----------|--------|-------|----------|
| Links    | 20%    | X/100 | X        |

Right-sized (good):

## What to Check
- TODOs, broken links, stale versions

## Output Format
List issues by severity. Include file:line and fix.

## Scope Control
If >30 files, ask user which to focus on.

---

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-01 → HTML-05 → HTML-11):

Must Update	What
New agent	"Workflow Position" diagram + "Next" field
Predecessor agent	Its "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

how to use agent-development
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use agent-development on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add agent-development
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/jezweb/claude-skills --skill agent-developmentcopy
The skills CLI fetches agent-development from GitHub repository jezweb/claude-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/agent-development
Reload or restart Cursor to activate agent-development. Access the skill through slash commands (e.g., /agent-development) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?