custom-agent

name	custom-agent
description	[AI & Tools] Use when you need create, verify, or enhance Claude Code custom agents (Claude Code custom agent files). Use when this capability is needed.

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

Mode	Trigger	Action
Create	$ARGUMENTS describes a new agent	Create agent file
Audit	mentions verify, audit, review, check, quality	Audit existing agents
Enhance	mentions refactor, enhance, improve, optimize	Improve 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:
                
                 


  


  
  

Tool Restriction Patterns

Agent Type	Recommended tools
Explorer/Scout	Read, Grep, Glob, Bash
Reviewer (read-only)	Read, Grep, Glob
Writer/Implementer	Read, Write, Edit, Grep, Glob, Bash
Researcher	Read, Grep, Glob, WebFetch, WebSearch
Orchestrator	Read, 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

Model	Best For
haiku	Fast read-only: scanning, search, file listing
sonnet	Balanced: code review, debugging, analysis
opus	High-stakes: architecture, complex implementation. Better quality for code review, debugging, analysis
inherit	Default — 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-Pattern	Fix
No tool restrictions	Add tools allowlist
Vague description	Write specific trigger conditions
Giant system prompt	Keep concise, use skills for detail
Recursive subagents	Restrict 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

#	Check	Rule	Severity
1	Frontmatter exists	Must have --- delimiters	Error
2	Name present & valid	Lowercase + hyphens only	Error
3	Description present	Non-empty, >20 chars	Error
4	No duplicate names	Unique across all agent files	Error
5	Description quality	Specific trigger scenarios	Warning
6	Tools minimal	Only what agent needs	Warning
7	Prompt structure	Has ## Role + ## Workflow	Warning
8	Model set	When task differs from default	Info

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

---

[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.

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. Keep domain concepts out of generic/shared/infrastructure layers. A reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/customer/product IDs, business entities, feature rules. The leak compiles and runs, so it passes review silently while coupling the "reusable" layer to one consumer. Push domain fields/logic down into the consumer via subclass or composition.

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.

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.

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

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

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Source: duc01226/EasyPlatform — distributed by TomeVault.