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This skill helps you design and deploy Claude Code agents with strong descriptions, controlled delegation, and safe tool access for scalable automation.
npx playbooks add skill openclaw/skills --skill agent-developmentReview the files below or copy the command above to add this skill to your agents.
---
name: agent-development
description: |
Design and build custom Claude Code agents with effective descriptions, tool access patterns,
and self-documenting prompts. Covers Task tool delegation, model selection, memory limits,
and declarative instruction design.
Use when: creating custom agents, designing agent descriptions for auto-delegation,
troubleshooting agent memory issues, or building agent pipelines.
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
```yaml
---
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`:
```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`:
```bash
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 |
```typescript
// ❌ 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)
```markdown
### 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 |
## Agent Prompt Structure
Effective agent prompts include:
```markdown
## 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**:
```bash
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
```yaml
---
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
```bash
export NODE_OPTIONS="--max-old-space-size=16384"
source ~/.bashrc && claude
```
This skill teaches how to design and build custom Claude Code agents with robust descriptions, safe tool access patterns, and self-documenting prompts. It focuses on task delegation, model selection, memory limits, and declarative instruction design to make agents reliable and autonomous. Use it to create agents that proactively take action, read and edit files, and run safe scripts when appropriate.
The skill shows how to write strong agent descriptions so Claude will auto-delegate or be invoked explicitly via Task sub-agents. It prescribes allowlist patterns and minimal tool sets to avoid approval spam, recommends model choices by task quality, and documents memory configuration and recovery steps. It also provides a prompt structure and pipeline rules so agents can reproduce behavior without developer context.
When should I include Bash in an agent's tools?
Only include Bash if the agent must run scripts or CLIs. Otherwise give Read/Write/Edit/Glob/Grep and use an allowlist for specific safe Bash commands if needed.
How do I ensure an agent will auto-delegate on matching tasks?
Use a strong description pattern with explicit trigger phrases, keywords, and instructions like 'MUST BE USED when...' so Claude can match tasks automatically.