Agent Creator

Creates spec-compliant agent .md files following the Anthropic 2026 16-field schema. Supports both creation of new agents and validation of existing ones.

Overview

Agent Creator fills the gap between ad-hoc agent files and production-grade agents that pass marketplace validation. It enforces the Anthropic agent schema (14 valid fields), prevents common mistakes (using allowed-tools instead of disallowedTools, adding invalid fields like capabilities or expertise_level), and produces agents with substantive body content that actually guides Claude's behavior.

Key difference from skill-creator: agents support both tools (allowlist) AND disallowedTools (denylist), while skills only use allowed-tools (allowlist). Agents also support effort, maxTurns, skills, memory, isolation, permissionMode, background, color, and initialPrompt — fields that don't exist for skills. The agent body becomes the system prompt that drives the subagent — it does NOT receive the full Claude Code system prompt.

Prerequisites

• Claude Code CLI with agent support
• Target directory writable (agents/ within a plugin or ~/.claude/agents/ for standalone)
• Familiarity with what the agent should specialize in

Instructions

Mode Detection

Determine user intent from their prompt:

• Create mode: "create an agent", "build a subagent", "new agent" -> Step 1
• Validate mode: "validate agent", "check agent", "grade agent" -> Validation Workflow

Step 1: Understand Requirements

Ask the user with AskUserQuestion:

Agent Identity:

• Name (kebab-case, 1-64 chars, e.g., risk-assessor, clause-analyzer)
• Specialty description (20-200 chars — shown in agent selection UI)

Execution Context:

• Plugin agent (plugins/*/agents/) or standalone (~/.claude/agents/)?
• Will it be spawned by an orchestrator skill via Task tool?
• Does it need to preload specific skills? (skills: [skill-name])

Behavioral Controls:

• Model override? (sonnet for speed, opus for quality, inherit for default)
• Reasoning effort? (low for simple, medium default, high for complex analysis)
• Max iterations? (maxTurns — how many tool-use loops before stopping)
• Tools to deny? (disallowedTools — denylist approach, opposite of skills)

Plugin Restrictions (if plugin agent):

• hooks — NOT supported in plugin agents (use plugin-level hooks)
• mcpServers — NOT supported in plugin agents
• permissionMode — standalone only, NOT plugin agents

Step 2: Plan the Agent

Before writing, determine:

Agent Role Clarity: The agent body must make three things unambiguous:

1. What it IS responsible for — its specific domain/methodology
2. What it is NOT responsible for — boundaries with other agents
3. How it communicates results — output format and structure

Body Structure Pattern: All production agents should follow this body structure:

Output Structure Decision:

• If the agent feeds into an orchestrator: use JSON output (machine-parseable)
• If the agent is user-facing: use markdown output (human-readable)
• If the agent produces both: JSON primary with markdown summary

Step 3: Write the Agent File

Generate the agent .md using the template from ${CLAUDE_SKILL_DIR}/../skill-creator/templates/agent-template.md.

Frontmatter Rules (Anthropic 16-field schema):

See Anthropic Agent Spec for the full official reference.

Required fields:

name: {agent-name}         # Lowercase letters and hyphens, unique identifier
description: "{specialty}"  # When Claude should delegate to this subagent

Optional fields (include only what's needed):

tools: "Read, Glob, Grep"  # Allowlist — inherits all tools if omitted
disallowedTools: "Write"   # Denylist — removed from inherited/specified list
model: sonnet              # sonnet|haiku|opus|inherit|full model ID
effort: medium             # low|medium|high|max (max = Opus 4.6 only)
maxTurns: 15               # Max agentic turns before stopping
skills: [skill-name]       # Skills to inject at startup (full content loaded)
memory: project            # user|project|local — persistent cross-session
background: false          # Always run as background task
isolation: worktree        # Run in temporary git worktree
color: blue                # Display: red|blue|green|yellow|purple|orange|pink|cyan
initialPrompt: "..."       # Auto-submitted first turn (--agent mode only)
permissionMode: default    # Standalone only, NOT plugin agents
hooks: {}                  # Standalone only, NOT plugin agents
mcpServers: {}             # Standalone only, NOT plugin agents

Tool access:

• tools = allowlist (like skills' allowed-tools)
• disallowedTools = denylist (remove specific tools)
• If both set: disallowed applied first, then tools resolved
• If neither set: inherits all tools from parent conversation

Invalid fields (ERROR — never use these):

• capabilities — looks valid but flagged by validator
• expertise_level — invented, not in Anthropic spec
• activation_priority — invented, not in Anthropic spec
• activation_triggers, type, category — not in spec
• allowed-tools — that's the skill-only syntax; agents use tools or disallowedTools

Body Content Guidelines:

1. Role section must set boundaries. Don't just say what the agent does — say what it does NOT do. Example: "You analyze contract clauses for risk. You do NOT provide legal advice or make recommendations — that is the recommendations agent's responsibility."

2. Process steps must be concrete. Each step should tell Claude exactly what to do, not vaguely gesture at an activity. Bad: "Analyze the document." Good: "Read the full contract. For each clause, extract: (a) the exact text, (b) the clause category from the taxonomy below, (c) a plain English summary in one sentence."

3. Output format must be machine-parseable if feeding an orchestrator. Use JSON with a concrete schema example. Include field descriptions so Claude knows what each field means.

4. Guidelines should include both DO and DON'T rules. Example:

   • DO: "Be specific — quote exact clause text, don't paraphrase"
   • DON'T: "Don't make legal recommendations — only identify and score risks"

5. Keep under 300 lines (agent body limit — prevents context bloat in subagent window). If the agent needs extensive reference material, create a companion skill with references/ directory and preload it via the skills field.

Step 4: Validate the Agent

Run validation against the Anthropic 16-field schema:

Manual checklist:

Automated validation:

python3 ${CLAUDE_SKILL_DIR}/../skill-creator/scripts/validate-skill.py --agents-only {plugin-dir}/

Step 5: Test the Agent

Test the agent by spawning it via the Task tool or the Agent tool:

1. Write a test prompt that exercises the agent's core capability
2. Spawn the agent with that prompt
3. Check: Does the output match the declared Output Format?
4. Check: Does the agent stay within its declared Role boundaries?
5. Check: Does it follow the Process steps?
6. Iterate on the body content if the agent strays

Step 6: Report

Provide a summary:

• Agent name and file path
• Frontmatter field count (of 14 possible)
• Body line count
• Sections present
• Validation result (pass/fail with specific issues)
• Test result summary

Validation Workflow

When the user wants to validate an existing agent:

1. Locate the agent .md file
2. Parse YAML frontmatter
3. Check against the 16-field Anthropic schema:
   • name present and valid (1-64 chars, kebab-case)?
   • description present and valid (20-200 chars)?
   • Any invalid fields? (capabilities, expertise_level, activation_priority, etc.)
   • Any skill-only fields? (allowed-tools)
   • Plugin restrictions respected?
4. Check body content:
   • Has ## Role section?
   • Has ## Process section with numbered steps?
   • Has ## Output Format with concrete example?
   • Has ## Guidelines?
   • Under 300 lines? (agent body limit)
5. Report findings with severity (ERROR/WARNING/INFO)
6. Suggest specific fixes for each issue

Output

• Create mode: A complete agent .md file with valid frontmatter and substantive body, plus a creation report with validation status.
• Validate mode: A compliance report listing errors, warnings, and info items with specific fix recommendations for each.

Examples

Subagent for Orchestrator Skill

Input: "Create a risk assessment agent that scores contract clauses"

Output: agents/risk-assessor.md with frontmatter:

name: risk-assessor
description: "Score contract clauses for legal and financial risk on a 1-10 scale"
model: sonnet
effort: high
maxTurns: 10

Body sections: Role (risk scoring specialist, does NOT make recommendations), Inputs (contract_text, contract_type, output_path), Process (4 steps: read, categorize, score, aggregate), Output Format (JSON with clause scores and risk matrix), Guidelines (be specific, cite clause text, use 4-factor scoring methodology).

Standalone User-Facing Agent

Input: "Create a code review agent"

Output: ~/.claude/agents/code-reviewer.md with frontmatter:

name: code-reviewer
description: "Review code for bugs, performance issues, and security vulnerabilities"
effort: high

Body sections: Role (code quality specialist), Process (read code, check patterns, identify issues, suggest fixes), Output Format (markdown with severity-rated findings), Guidelines (cite line numbers, explain why not just what), Communication Style (direct, educational, actionable).

Error Handling

Resources

• Anthropic Agent Spec — Official 16-field schema from code.claude.com/docs/en/sub-agents
• Agent template — Skeleton with placeholders
• Frontmatter spec — Field reference (internal)
• Source of truth — Canonical spec
• Validation rules — Agent validation section