Ma1orek Replay Sub Agent Patterns

Sub-Agents in Claude Code

Status: Production Ready ✅ Last Updated: 2026-01-14 Source: https://code.claude.com/docs/en/sub-agents

Sub-agents are specialized AI assistants that Claude Code can delegate tasks to. Each sub-agent has its own context window, configurable tools, and custom system prompt.

Why Use Sub-Agents: Context Hygiene

The primary value of sub-agents isn't specialization—it's keeping your main context clean.

Without agent (context bloat):

Main context accumulates:
├─ git status output (50 lines)
├─ npm run build output (200 lines)
├─ tsc --noEmit output (100 lines)
├─ wrangler deploy output (100 lines)
├─ curl health check responses
├─ All reasoning about what to do next
└─ Context: 📈 500+ lines consumed

With agent (context hygiene):

Main context:
├─ "Deploy to cloudflare"
├─ [agent summary - 30 lines]
└─ Context: 📊 ~50 lines consumed

Agent context (isolated):
├─ All verbose tool outputs
├─ All intermediate reasoning
└─ Discarded after returning summary

The math: A deploy workflow runs ~10 tool calls. That's 500+ lines in main context vs 30-line summary with an agent. Over a session, this compounds dramatically.

When this matters most:

• Repeatable workflows (deploy, migrate, audit, review)
• Verbose tool outputs (build logs, test results, API responses)
• Multi-step operations where only the final result matters
• Long sessions where context pressure builds up

Key insight: Use agents for workflows you repeat, not just for specialization. The context savings compound over time.

Built-in Sub-Agents

Claude Code includes three built-in sub-agents available out of the box:

Explore Agent

Fast, lightweight agent optimized for read-only codebase exploration.

Thoroughness levels (specify when invoking):

• quick - Fast searches, targeted lookups
• medium - Balanced speed and thoroughness
• very thorough - Comprehensive analysis across multiple locations

When Claude uses it: Searching/understanding codebase without making changes. Findings don't bloat the main conversation.

User: Where are errors from the client handled?
Claude: [Invokes Explore with "medium" thoroughness]
       → Returns: src/services/process.ts:712

Plan Agent

Specialized for plan mode research and information gathering.

When Claude uses it: In plan mode when researching codebase to create a plan. Prevents infinite nesting (sub-agents cannot spawn sub-agents).

General-Purpose Agent

Capable agent for complex, multi-step tasks requiring both exploration AND action.

When Claude uses it:

• Task requires both exploration and modification
• Complex reasoning needed to interpret search results
• Multiple strategies may be needed
• Task has multiple dependent steps

Creating Custom Sub-Agents

File Locations

When names conflict, project-level takes precedence.

⚠️ CRITICAL: Session Restart Required

Agents are loaded at session startup only. If you create new agent files during a session:

1. They won't appear in /agents
2. Claude won't be able to invoke them
3. Solution: Restart Claude Code session to discover new agents

This is the most common reason custom agents "don't work" - they were created after the session started.

File Format

Markdown files with YAML frontmatter:

---
name: code-reviewer
description: Expert code reviewer. Use proactively after code changes.
tools: Read, Grep, Glob, Bash
model: inherit
permissionMode: default
skills: project-workflow
hooks:
  PostToolUse:
    - matcher: "Edit|Write"
      hooks:
        - type: command
          command: "./scripts/run-linter.sh"
---

Your sub-agent's system prompt goes here.

Include specific instructions, best practices, and constraints.

Configuration Fields

Available Tools Reference

Complete list of tools that can be assigned to sub-agents:

Tool Access Patterns by Agent Type:

⚠️ Tool Access Principle: If an agent doesn't need Bash, don't give it Bash. Each bash command requires approval, causing workflow interruptions. See "Avoiding Bash Approval Spam" below.

Avoiding Bash Approval Spam (CRITICAL)

When sub-agents have Bash in their tools list, they often default to using cat > file << 'EOF' heredocs for file creation instead of the Write tool. Each unique bash command requires user approval, causing:

• Dozens of approval prompts per agent run
• Slow, frustrating workflow
• Hard to review (heredocs are walls of minified content)

Root Causes:

1. Models default to bash for file ops - Training data bias toward shell commands
2. Bash in tools list = Bash gets used - Even if Write tool is available
3. Instructions get buried - A "don't use bash" rule at line 300 of a 450-line prompt gets ignored

Solutions (in order of preference):

1. Remove Bash from tools list (if not needed):

   # Before - causes approval spam
   tools: Read, Write, Edit, Glob, Grep, Bash
   
   # After - clean file operations
   tools: Read, Write, Edit, Glob, Grep
   

   If the agent only creates files, it doesn't need Bash. The orchestrator can run necessary scripts after.

2. Put critical instructions FIRST (immediately after frontmatter):

   ---
   name: site-builder
   tools: Read, Write, Edit, Glob, Grep
   model: sonnet
   ---
   
   ## ⛔ CRITICAL: USE WRITE TOOL FOR ALL FILES
   
   **You do NOT have Bash access.** Create ALL files using the **Write tool**.
   
   ---
   
   [rest of prompt...]
   

   Instructions at the top get followed. Instructions buried 300 lines deep get ignored.

3. Remove contradictory instructions:

   # BAD - contradictory
   Line 75: "Copy images with `cp -r intake/images/* build/images/`"
   Line 300: "NEVER use cp, mkdir, cat, or echo"
   
   # GOOD - consistent
   Only mention the pattern you want used. Remove all bash examples if you want Write tool.
   

When to keep Bash:

• Agent needs to run external CLIs (wrangler, npm, git)
• Agent needs to execute scripts
• Agent needs to check command outputs

Testing: Before vs after removing Bash:

• Before (with Bash): 11+ heredoc approval prompts, wrong patterns applied
• After (no Bash): Mostly Write tool usage, correct patterns, minimal prompts

Using /agents Command (Recommended)

/agents

Interactive menu to:

• View all sub-agents (built-in, user, project)
• Create new sub-agents with guided setup
• Edit existing sub-agents and tool access
• Delete custom sub-agents
• See which sub-agents are active

CLI Configuration

claude --agents '{
  "code-reviewer": {
    "description": "Expert code reviewer. Use proactively after code changes.",
    "prompt": "You are a senior code reviewer. Focus on code quality, security, and best practices.",
    "tools": ["Read", "Grep", "Glob", "Bash"],
    "model": "sonnet"
  }
}'

Using Sub-Agents

Automatic Delegation

Claude proactively delegates based on:

• Task description in your request
• description field in sub-agent config
• Current context and available tools

Tip: Include "use PROACTIVELY" or "MUST BE USED" in description for more automatic invocation.

Explicit Invocation

> Use the test-runner subagent to fix failing tests
> Have the code-reviewer subagent look at my recent changes
> Ask the debugger subagent to investigate this error

Resumable Sub-Agents

Sub-agents can be resumed to continue previous conversations:

# Initial invocation
> Use the code-analyzer agent to review the auth module
[Agent completes, returns agentId: "abc123"]

# Resume with full context
> Resume agent abc123 and now analyze the authorization logic

Use cases:

• Long-running research across multiple sessions
• Iterative refinement without losing context
• Multi-step workflows with maintained context

Disabling Sub-Agents

Add to settings.json permissions:

{
  "permissions": {
    "deny": ["Task(Explore)", "Task(Plan)"]
  }
}

Or via CLI:

claude --disallowedTools "Task(Explore)"

Agent Orchestration

Sub-agents can invoke other sub-agents, enabling sophisticated orchestration patterns. This is accomplished by including the Task tool in an agent's tool list.

How It Works

When a sub-agent has access to the Task tool, it can:

• Spawn other sub-agents (built-in or custom)
• Coordinate parallel work across specialists
• Chain agents for multi-phase workflows

---
name: orchestrator
description: Orchestrator agent that coordinates other specialized agents. Use for complex multi-phase tasks.
tools: Read, Grep, Glob, Task  # ← Task tool enables agent spawning
model: sonnet
---

Orchestrator Pattern

---
name: release-orchestrator
description: Coordinates release preparation by delegating to specialized agents. Use before releases.
tools: Read, Grep, Glob, Bash, Task
---

You are a release orchestrator. When invoked:

1. Use Task tool to spawn code-reviewer agent
   → Review all uncommitted changes

2. Use Task tool to spawn test-runner agent
   → Run full test suite

3. Use Task tool to spawn doc-validator agent
   → Check documentation is current

4. Collect all reports and synthesize:
   - Blockers (must fix before release)
   - Warnings (should address)
   - Ready to release: YES/NO

Spawn agents in parallel when tasks are independent.
Wait for all agents before synthesizing final report.

Practical Examples

Multi-Specialist Workflow:

User: "Prepare this codebase for production"

orchestrator agent:
  ├─ Task(code-reviewer) → Reviews code quality
  ├─ Task(security-auditor) → Checks for vulnerabilities
  ├─ Task(performance-analyzer) → Identifies bottlenecks
  └─ Synthesizes findings into actionable report

Parallel Research:

User: "Compare these 5 frameworks for our use case"

research-orchestrator:
  ├─ Task(general-purpose) → Research framework A
  ├─ Task(general-purpose) → Research framework B
  ├─ Task(general-purpose) → Research framework C
  ├─ Task(general-purpose) → Research framework D
  ├─ Task(general-purpose) → Research framework E
  └─ Synthesizes comparison matrix with recommendation

Nesting Depth

Best practice: Keep orchestration to 2 levels deep. Beyond that, context windows shrink and coordination becomes fragile.

When to Use Orchestration

Orchestrator vs Direct Delegation

Direct (simpler, often sufficient):

User: "Review my code changes"
Claude: [Invokes code-reviewer agent directly]

Orchestrated (when coordination needed):

User: "Prepare release"
Claude: [Invokes release-orchestrator]
        orchestrator: [Spawns code-reviewer, test-runner, doc-validator]
        orchestrator: [Synthesizes all reports]
        [Returns comprehensive release readiness report]

Configuration Notes

1. Tool access propagates: An orchestrator with Task can spawn any agent the session has access to
2. Model inheritance: Spawned agents use their configured model (or inherit if set to inherit)
3. Context isolation: Each spawned agent has its own context window
4. Results bubble up: Orchestrator receives agent results and can synthesize them

Advanced Patterns

Background Agents (Async Delegation)

Send agents to the background while continuing work in your main session:

Ctrl+B during agent execution moves it to background.

> Use the research-agent to analyze these 10 frameworks
[Agent starts working...]
[Press Ctrl+B]
→ Agent continues in background
→ Main session free for other work
→ Check results later with: "What did the research agent find?"

Use cases:

• Long-running research tasks
• Parallel documentation fetching
• Non-blocking code reviews

Model Selection Strategy

Quality-First Approach: Default to Sonnet for most agents. The cost savings from Haiku rarely outweigh the quality loss.

Why Sonnet Default?

Testing showed significant quality differences:

• Haiku: Wrong stylesheet links, missing CSS, wrong values, incorrect patterns
• Sonnet: Correct patterns, proper validation, fewer errors

Pattern: Default Sonnet, use Opus for creative, Haiku only when quality truly doesn't matter:

---
name: site-builder
model: sonnet  # Content quality matters - NOT haiku
tools: Read, Write, Edit, Glob, Grep
---

---
name: creative-director
model: opus  # Creative work needs maximum quality
tools: Read, Write, Edit, Glob, Grep
---

---
name: deploy-runner
model: haiku  # Just running wrangler commands - quality irrelevant
tools: Read, Bash
---

Agent Context Considerations

Agent context usage depends heavily on the task:

Reality: Agents with 90+ tool calls and 130k context work fine when doing meaningful work. The limiting factor is task complexity, not arbitrary token limits.

What actually matters:

• Is the agent making progress on each tool call?
• Is context being used for real work vs redundant instructions?
• Are results coherent at the end?

When context becomes a problem:

• Agent starts repeating itself or losing track
• Results become incoherent or contradictory
• Agent "forgets" earlier findings in long sessions

Persona-Based Routing

Prevent agents from drifting into adjacent domains with explicit constraints:

---
name: frontend-specialist
description: Frontend code expert. NEVER writes backend logic.
tools: Read, Write, Edit, Glob, Grep
---

You are a frontend specialist.

BOUNDARIES:
- NEVER write backend logic, API routes, or database queries
- ALWAYS use React patterns consistent with the codebase
- If task requires backend work, STOP and report "Requires backend specialist"

FOCUS:
- React components, hooks, state management
- CSS/Tailwind styling
- Client-side routing
- Browser APIs

This prevents hallucination when agents encounter unfamiliar domains.

Hooks Patterns

Hooks enable automated validation and feedback:

Block-at-commit (enforce quality gates):

hooks:
  PreToolUse:
    - matcher: "Bash"
      hooks:
        - type: command
          command: |
            if [[ "$BASH_COMMAND" == *"git commit"* ]]; then
              npm test || exit 1
            fi

Hint hooks (non-blocking feedback):

hooks:
  PostToolUse:
    - matcher: "Edit|Write"
      hooks:
        - type: command
          command: "./scripts/lint-check.sh"
          # Exits 0 to continue, non-zero to warn

Best practice: Validate at commit stage, not mid-plan. Let agents work freely, catch issues before permanent changes.

Nested CLAUDE.md Context

Claude automatically loads CLAUDE.md files from subdirectories when accessing those paths:

project/
├── CLAUDE.md              # Root context (always loaded)
├── src/
│   └── CLAUDE.md          # Loaded when editing src/**
├── tests/
│   └── CLAUDE.md          # Loaded when editing tests/**
└── docs/
    └── CLAUDE.md          # Loaded when editing docs/**

Use for: Directory-specific coding standards, local patterns, module documentation.

This is lazy-loaded context - sub-agents get relevant context without bloating main prompt.

Master-Clone vs Custom Subagent

Two philosophies for delegation:

Custom Subagents (explicit specialists):

Main Claude → task-runner agent → result

• Pros: Isolated context, specialized prompts, reusable
• Cons: Gatekeeper effect (main agent loses visibility)

Master-Clone (dynamic delegation):

Main Claude → Task(general-purpose) → result

• Pros: Main agent stays informed, flexible routing
• Cons: Less specialized, may need more guidance

Recommendation: Use custom agents for well-defined, repeated tasks. Use Task(general-purpose) for ad-hoc delegation where main context matters.

Delegation Patterns

The Sweet Spot

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

✅ Good fit:
   - Audit 70 skills (repetitive) checking versions against docs (judgment)
   - Update 50 files (repetitive) deciding what needs changing (judgment)
   - Research 10 frameworks (repetitive) evaluating trade-offs (judgment)

❌ Poor fit:
   - Simple find-replace (no judgment needed, use sed/grep)
   - Single complex task (not repetitive, do it yourself)
   - Tasks with cross-item dependencies (agents work independently)

Core Prompt Template

This 5-step structure works consistently:

For each [item]:
1. Read [source file/data]
2. Verify with [external check - npm view, API, docs]
3. Check [authoritative source]
4. Evaluate/score
5. FIX issues found ← Critical: gives agent authority to act

Key elements:

• "FIX issues found" - Without this, agents only report. With it, they take action.
• Exact file paths - Prevents ambiguity and wrong-file edits
• Output format template - Ensures consistent, parseable reports
• Item list - Explicit list of what to process

Batch Sizing

Why not more?

• Agent context fills up
• One failure doesn't ruin entire batch
• Easier to review smaller changesets

Parallel agents: Launch 2-4 agents simultaneously, each with their own batch.

Workflow Pattern

┌─────────────────────────────────────────────────────────────┐
│  1. PLAN: Identify items, divide into batches               │
│     └─ "58 skills ÷ 10 per batch = 6 agents"                │
├─────────────────────────────────────────────────────────────┤
│  2. LAUNCH: Parallel Task tool calls with identical prompts │
│     └─ Same template, different item lists                  │
├─────────────────────────────────────────────────────────────┤
│  3. WAIT: Agents work in parallel                           │
│     └─ Read → Verify → Check → Edit → Report                │
├─────────────────────────────────────────────────────────────┤
│  4. REVIEW: Check agent reports and file changes            │
│     └─ git status, spot-check diffs                         │
├─────────────────────────────────────────────────────────────┤
│  5. COMMIT: Batch changes with meaningful changelog         │
│     └─ One commit per tier/category, not per agent          │
└─────────────────────────────────────────────────────────────┘

Prompt Templates

Audit/Validation Pattern

Deep audit these [N] [items]. For each:

1. Read the [source file] from [path]
2. Verify [versions/data] with [command or API]
3. Check official [docs/source] for accuracy
4. Score 1-10 and note any issues
5. FIX issues found directly in the file

Items to audit:
- [item-1]
- [item-2]
- [item-3]

For each item, create a summary with:
- Score and status (PASS/NEEDS_UPDATE)
- Issues found
- Fixes applied
- Files modified

Working directory: [absolute path]

Bulk Update Pattern

Update these [N] [items] to [new standard/format]. For each:

1. Read the current file at [path pattern]
2. Identify what needs changing
3. Apply the update following this pattern:
   [show example of correct format]
4. Verify the change is valid
5. Report what was changed

Items to update:
- [item-1]
- [item-2]
- [item-3]

Output format:
| Item | Status | Changes Made |
|------|--------|--------------|

Working directory: [absolute path]

Research/Comparison Pattern

Research these [N] [options/frameworks/tools]. For each:

1. Check official documentation at [URL pattern or search]
2. Find current version and recent changes
3. Identify key features relevant to [use case]
4. Note any gotchas, limitations, or known issues
5. Rate suitability for [specific need] (1-10)

Options to research:
- [option-1]
- [option-2]
- [option-3]

Output format:
## [Option Name]
- **Version**: X.Y.Z
- **Key Features**: ...
- **Limitations**: ...
- **Suitability Score**: X/10
- **Recommendation**: ...

Example Custom Sub-Agents

Code Reviewer

---
name: code-reviewer
description: Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code.
tools: Read, Grep, Glob, Bash
model: inherit
---

You are a senior code reviewer ensuring high standards of code quality and security.

When invoked:
1. Run git diff to see recent changes
2. Focus on modified files
3. Begin review immediately

Review checklist:
- Code is clear and readable
- Functions and variables are well-named
- No duplicated code
- Proper error handling
- No exposed secrets or API keys
- Input validation implemented
- Good test coverage
- Performance considerations addressed

Provide feedback organized by priority:
- Critical issues (must fix)
- Warnings (should fix)
- Suggestions (consider improving)

Include specific examples of how to fix issues.

Debugger

---
name: debugger
description: Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.
tools: Read, Edit, Bash, Grep, Glob
---

You are an expert debugger specializing in root cause analysis.

When invoked:
1. Capture error message and stack trace
2. Identify reproduction steps
3. Isolate the failure location
4. Implement minimal fix
5. Verify solution works

Debugging process:
- Analyze error messages and logs
- Check recent code changes
- Form and test hypotheses
- Add strategic debug logging
- Inspect variable states

For each issue, provide:
- Root cause explanation
- Evidence supporting the diagnosis
- Specific code fix
- Testing approach
- Prevention recommendations

Focus on fixing the underlying issue, not the symptoms.

Data Scientist

---
name: data-scientist
description: Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
tools: Bash, Read, Write
model: sonnet
---

You are a data scientist specializing in SQL and BigQuery analysis.

When invoked:
1. Understand the data analysis requirement
2. Write efficient SQL queries
3. Use BigQuery command line tools (bq) when appropriate
4. Analyze and summarize results
5. Present findings clearly

Key practices:
- Write optimized SQL queries with proper filters
- Use appropriate aggregations and joins
- Include comments explaining complex logic
- Format results for readability
- Provide data-driven recommendations

Always ensure queries are efficient and cost-effective.

Commit Strategy

Agents don't commit - they only edit files. This is by design:

Why?

• Review before commit catches agent errors
• Batch multiple agents into meaningful commits
• Clean commit history (not 50 tiny commits)
• Human decides commit message/grouping

Commit pattern:

git add [files] && git commit -m "$(cat <<'EOF'
[type]([scope]): [summary]

[Batch 1 changes]
[Batch 2 changes]
[Batch 3 changes]

🤖 Generated with [Claude Code](https://claude.com/claude-code)
EOF
)"

Error Handling

When One Agent Fails

1. Check the error message
2. Decide: retry that batch OR skip and continue
3. Don't let one failure block the whole operation

When Agent Makes Wrong Change

1. git diff [file] to see what changed
2. git checkout -- [file] to revert
3. Re-run with more specific instructions

When Agents Conflict

Rare (agents work on different items), but if it happens:

1. Check which agent's change is correct
2. Manually resolve or re-run one agent

Best Practices

1. Start with Claude-generated agents: Use /agents to generate initial config, then customize
2. Design focused sub-agents: Single, clear responsibility per agent
3. Write detailed prompts: Specific instructions, examples, constraints
4. Don't give Bash unless needed: Prevents approval spam (see "Avoiding Bash Approval Spam")
5. Put critical instructions FIRST: Instructions at top of prompt get followed, buried ones get ignored
6. Remove contradictory instructions: If you want Write tool, remove all bash examples
7. Default to Sonnet model: Quality matters more than cost savings (see Model Selection)
8. Version control: Check .claude/agents/ into git for team sharing
9. Use inherit for model sparingly: Better to explicitly set model for predictable behavior

Performance Considerations

Quick Reference

Built-in agents:
  Explore  → Haiku, read-only, quick/medium/thorough
  Plan     → Sonnet, plan mode research
  General  → Sonnet, all tools, read/write

Custom agents:
  Project  → .claude/agents/*.md (highest priority)
  User     → ~/.claude/agents/*.md
  CLI      → --agents '{...}'

Config fields:
  name, description (required)
  tools, model, permissionMode, skills, hooks (optional)

Tool access principle:
  ⚠️ Don't give Bash unless agent needs CLI execution
  File creators: Read, Write, Edit, Glob, Grep (no Bash!)
  Script runners: Read, Write, Edit, Glob, Grep, Bash (only if needed)
  Research: Read, Grep, Glob, WebFetch, WebSearch

Model selection (quality-first):
  Default: sonnet (most agents - quality matters)
  Creative: opus (maximum quality)
  Scripts only: haiku (just running commands)
  ⚠️ Avoid Haiku for content generation - quality drops significantly

Instruction placement:
  ⛔ Critical instructions go FIRST (right after frontmatter)
  ⚠️ Instructions buried 300+ lines deep get ignored
  ✅ Remove contradictory instructions (pick one pattern)

Delegation:
  Batch size: 5-8 items per agent
  Parallel: 2-4 agents simultaneously
  Prompt: 5-step (read → verify → check → evaluate → FIX)

Orchestration:
  Enable: Add "Task" to agent's tools list
  Depth: Keep to 2 levels max
  Use: Multi-phase workflows, parallel specialists

Advanced:
  Background: Ctrl+B during agent execution
  Context: 130k+ and 90+ tool calls work fine for real work
  Hooks: PreToolUse, PostToolUse, Stop events

Resume agents:
  > Resume agent [agentId] and continue...

References

Official Documentation

• Sub-Agents Documentation
• Plugins Documentation
• Tools Documentation
• Hooks Documentation
• CLI Reference

Community Resources

• Awesome Claude Code Subagents - 100+ specialized agents
• Claude Code System Prompts - Internal prompt analysis
• Claude Code Built-in Tools Reference - Comprehensive tool guide
• Claude Code Frameworks Guide (Dec 2025) - Advanced patterns

Last Updated: 2026-01-14

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