# Subagent Creator > Meta-skill that generates Claude Code agent definition files (.md). Creates specialized agents with proper tool access, model selection, and behavioral instructions. Use when asked to "create an agent", "make a sub-agent", or "generate agent for X". Use when this capability is needed. - Skill: `tomevault-io/subagent-creator-6` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/subagent-creator-6` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/subagent-creator-6/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/subagent-creator-6 --- # Sub-Agent Creator — Meta-Skill for Generating Agent Definitions A meta-skill that produces Claude Code agent `.md` files. It designs agents with appropriate expertise, tool access, behavioral rules, and quality requirements following AgenticWorkflow conventions. ## When to Use - User asks to create a new agent for a specific task - System needs to generate agents for a workflow (e.g., thesis agents) - Batch creation of multiple related agents ## Inherited DNA This meta-skill inherits the AgenticWorkflow genome. As a skill that generates agents, it must itself embody the DNA it enforces. | DNA Component | Expression in subagent-creator | |--------------|-------------------------------| | Absolute Criteria 1 (Quality) | Generated agents use optimal model selection (opus for research, sonnet for utility) | | Absolute Criteria 2 (SOT) | Generated agents respect single-writer SOT pattern; guard_sot_write.py compatibility | | Absolute Criteria 3 (CCP) | Research agents include GRA compliance; utility agents document CCP exemption rationale | | English-First | All agent instructions are in English | | P1 Compliance | Research agents include GroundedClaim schema + Hallucination Firewall | | Quality Gates | Research agents integrate with validate_grounded_claim.py PostToolUse hook | ## Agent File Schema Agent definitions live in `.claude/agents/` and follow this structure: ```markdown --- name: {agent-name} # kebab-case description: {brief description} model: opus # opus | sonnet | haiku tools: Read, Write, Glob, Grep # comma-separated tool list maxTurns: {N} # max reasoning turns (default: 20) memory: project # project | none --- {Agent behavioral instructions in English} ``` ## Generation Protocol ### Step 1: Agent Design Analysis Determine the agent's requirements: 1. **Primary task**: What is the agent's core responsibility? 2. **Expertise domain**: What specialized knowledge does it need? 3. **Input/Output**: What does it receive and produce? 4. **Quality criteria**: What makes a good output for this agent? 5. **Tool needs**: Which tools does it require? ### Step 2: Model Selection Select the appropriate model based on task complexity: | Model | Use When | Examples | |-------|----------|---------| | `opus` | Complex analysis, synthesis, critical reasoning | Thesis writer, critical reviewer, synthesis agent | | `sonnet` | Structured tasks, search, data processing | Literature searcher, formatting specialist | | `haiku` | Simple, repetitive tasks | File validation, format checking | **Default to `opus`** when quality is the absolute criterion (Absolute Criteria 1). ### Step 3: Tool Selection Assign tools based on the agent's needs: | Tool | When to Include | |------|-----------------| | `Read` | Agent needs to read files (almost always) | | `Write` | Agent produces output files | | `Glob` | Agent needs to find files by pattern | | `Grep` | Agent needs to search file contents | | `Bash` | Agent needs to run commands (use sparingly) | | `WebSearch` | Agent needs to search the web | | `WebFetch` | Agent needs to fetch web content | | `Agent` | Agent needs to delegate to sub-agents | ### Step 4: Generate Agent Definition Write the agent `.md` file with: 1. **Frontmatter**: name, description, model, tools, maxTurns, memory 2. **Role definition**: "You are a [role] specializing in [domain]." 3. **Task instructions**: Step-by-step protocol for the agent's work 4. **Output format**: Exact specification of expected output structure 5. **Quality rules**: Domain-specific quality requirements 6. **GRA compliance** (if research agent): GroundedClaim schema, Hallucination Firewall rules ### Step 5: Context Isolation Assessment Determine if commands invoking this agent should use `context: fork`: | Factor | Inline (no fork) | Fork recommended | |--------|-----------------|------------------| | Agent is part of orchestration flow | ✅ | ❌ | | Agent writes to SOT | ✅ (orchestrator only) | ❌ never | | Agent does independent analysis/production | ❌ | ✅ | | Agent needs Bash for P1 validation scripts | Fork requires Bash in tool list | Check tool compatibility | | Agent's work would pollute main context | ❌ | ✅ | **If fork is recommended**, note this in the agent's documentation: ```markdown ## Fork Compatibility This agent is safe for `context: fork` invocation. It: - Reads SOT but never writes to it - Produces independent output files at {output_path} - Does not require Bash / Does require Bash (specify) ``` **Most thesis workflow agents should NOT be forked** — they are invoked by thesis-orchestrator within Agent Teams, which already provides context isolation. ### Step 6: GRA Integration (Research Agents Only) For agents that produce research claims, add: ```markdown ## GRA Compliance All claims must follow the GroundedClaim schema: - **id**: "{CLAIM_PREFIX}-{NNN}" (e.g., "LS-001") - **claim_type**: FACTUAL | EMPIRICAL | THEORETICAL | METHODOLOGICAL | INTERPRETIVE | SPECULATIVE - **sources**: At least one PRIMARY or SECONDARY source with reference and DOI - **confidence**: 0-100 score - **effect_size**: When applicable (statistical findings) - **uncertainty**: Explicit limitation statement ### Hallucination Firewall - BLOCK: "all studies agree", "100%", "no exceptions" - REQUIRE_SOURCE: Any statistical claim (p-values, effect sizes) - SOFTEN: "certainly", "obviously", "clearly" → add hedging - VERIFY: "it is known that" → add citation ``` ### Step 7: Validate Agent Definition Verify the generated agent: - [ ] Frontmatter has all required fields - [ ] name matches filename (kebab-case) - [ ] Instructions are in English (AI performance) - [ ] Output format is clearly specified - [ ] Tool list matches actual needs - [ ] GRA compliance section present (if research agent) - [ ] No placeholder content - [ ] **Fork Safety Cross-Validation** (if fork-compatible in Step 5): Any command/skill using `agent: {this-agent}` with `context: fork` must pass: `python3 .claude/hooks/scripts/validate_fork_safety.py --file --project-dir ` Validates FS-3 (Bash dependency vs agent tools) and FS-5 (agent existence). ### Step 8: Register Agent Output: 1. Agent file location: `.claude/agents/{name}.md` 2. How to invoke: `@{name}` in prompts or via Agent tool 3. Claim prefix (if GRA agent): `{PREFIX}` ## Language Rules - **Agent instructions (body)**: English — AI performance optimization - **Description (frontmatter)**: English — for agent matching - **Output instructions for user-facing text**: Include Korean translation directive ## Batch Creation When creating multiple related agents: 1. Design all agents together for consistency 2. Ensure claim prefixes are unique across the set 3. Define inter-agent dependencies explicitly 4. Verify no overlapping responsibilities ## Quality Checklist - [ ] Agent follows AgenticWorkflow conventions - [ ] Model selection justified by task complexity - [ ] Tool list is minimal but sufficient - [ ] Instructions are specific and actionable - [ ] GRA compliance complete (for research agents) - [ ] Claim prefix unique (for GRA agents) - [ ] Fork compatibility assessed (Step 5) and documented if applicable - [ ] No hardcoded file paths --- > Source: [idoforgod/Dissertation-Simulator-AgenticWorkflow](https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflow) — distributed by [TomeVault](https://tomevault.io).