Meta-skill that generates Claude Code agent definition files (.md).

MITAuto-check passedAgent Workflows

Install Subagent Creator

skills CLI
$ npx skills add idoforgod/Dissertation-Simulator-AgenticWorkflow --skill subagent-creator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install idoforgod/Dissertation-Simulator-AgenticWorkflow subagent-creator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/idoforgod/Dissertation-Simulator-AgenticWorkflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/subagent-creator .claude/skills/subagent-creator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
subagent-creator
GitHub stars
108
Token cost
~1.9k tokens
SKILL.md length
737 words
Files
5 (incl. references)
Skills in repo
4
Repo updated
First seen
Licence
MIT

At a glance

Meta-skill that generates Claude Code agent definition files (.md).

  • Works in 8 steps: Agent Design Analysis → Model Selection → Tool Selection → …
  • Asked to create an agent
  • SKILL.md covers When to Use, Inherited DNA, Agent File Schema and Generation Protocol, plus 3 more sections
  • Calls python3

What it does

Subagent Creator is an agent skill from idoforgod/Dissertation-Simulator-AgenticWorkflow. 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".

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/agent-template-guide.md`, `references/gra-schema-reference.md` and `references/output-path-conventions.md`).

It sits in Agent Workflows, covering Subagents. The repository describes itself as: AI 에이전트 58개가 협업하는 211-step 박사 논문 연구 시뮬레이션 시스템. AgenticWorkflow 프레임워크에서 태어난 자식 시스템. The licence is MIT.

When your agent uses it

  • Asked to create an agent
  • Make a sub-agent
  • Generate agent for X

Example prompts

  • “create an agent”
  • “make a sub-agent”
  • “generate agent for X”
  • “/subagent-creator”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Agent Design Analysis
  2. Model Selection
  3. Tool Selection
  4. Generate Agent Definition
  5. Context Isolation Assessment
  6. GRA Integration (Research Agents Only)
  7. Validate Agent Definition
  8. Register Agent

What it can do on your machine

Read from SKILL.md and the folder at commit f2205f5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Subagent Creator loads about 1.9k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 737 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from idoforgod/Dissertation-Simulator-AgenticWorkflow at commit f2205f5, republished under its MIT licence (© idoforgod). 737 words, ~1,900 tokens.

Download SKILL.mdSave it as .claude/skills/subagent-creator/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
subagent-creator
description
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".

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 ComponentExpression 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-FirstAll agent instructions are in English
P1 ComplianceResearch agents include GroundedClaim schema + Hallucination Firewall
Quality GatesResearch 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:

ModelUse WhenExamples
opusComplex analysis, synthesis, critical reasoningThesis writer, critical reviewer, synthesis agent
sonnetStructured tasks, search, data processingLiterature searcher, formatting specialist
haikuSimple, repetitive tasksFile 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:

ToolWhen to Include
ReadAgent needs to read files (almost always)
WriteAgent produces output files
GlobAgent needs to find files by pattern
GrepAgent needs to search file contents
BashAgent needs to run commands (use sparingly)
WebSearchAgent needs to search the web
WebFetchAgent needs to fetch web content
AgentAgent 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
Show full SKILL.md (317 more words)Show less
Step 5: Context Isolation Assessment

Determine if commands invoking this agent should use context: fork:

FactorInline (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 scriptsFork requires Bash in tool listCheck 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 <command.md> --project-dir <project-root> 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

© idoforgod, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in .claude/skills/subagent-creator of idoforgod/Dissertation-Simulator-AgenticWorkflow.

  • SKILL.md
  • references/agent-template-guide.md
  • references/gra-schema-reference.md
  • references/output-path-conventions.md
  • references/tier-routing-guide.md

Open the folder on GitHubat commit f2205f5

Compare with similar skills

Subagent Creator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Subagent Creator compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Subagent Creator this skillidoforgod/Dissertation-Simulator-AgenticWorkflow108—~1.9kAutomated safety check: PassMIT
Claude Code Agent Developmentanthropics/claude-plugins-official38k8 repos~2.8kAutomated safety check: PassApache-2.0
Subagent Driven DevelopmentAsvarox/allkaraoke26138 repos~1.2kAutomated safety check: PassNone
Dispatching Parallel Agentsultralisp/ultralisp25841 repos~1.5kAutomated safety check: PassNone
Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence
Task Observerrebelytics/one-skill-to-rule-them-all3.2k1 repos~12kAutomated safety check: PassCC-BY-4.0

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Categories

Questions about Subagent Creator

What does Subagent Creator do?

Meta-skill that generates Claude Code agent definition files (.md). Subagent Creator is an agent skill from idoforgod/Dissertation-Simulator-AgenticWorkflow.md).

When should I use Subagent Creator?

Subagent Creator fits situations like: asked to create an agent; make a sub-agent; generate agent for X.

How do I install Subagent Creator in Claude Code?

Run `npx skills add idoforgod/Dissertation-Simulator-AgenticWorkflow --skill subagent-creator -a claude-code`. Or copy the skill folder (.claude/skills/subagent-creator in idoforgod/Dissertation-Simulator-AgenticWorkflow) into .claude/skills/subagent-creator in your project. Claude Code loads it when a task matches its description.

How do I install Subagent Creator in Codex?

Run `npx skills add idoforgod/Dissertation-Simulator-AgenticWorkflow --skill subagent-creator -a codex`. Or copy the skill folder (.claude/skills/subagent-creator in idoforgod/Dissertation-Simulator-AgenticWorkflow) into .agents/skills/subagent-creator in your project. Codex loads it when a task matches its description.

Can I use Subagent Creator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add idoforgod/Dissertation-Simulator-AgenticWorkflow --skill subagent-creator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/subagent-creator, .gemini/skills/subagent-creator, .github/skills/subagent-creator and .opencode/skills/subagent-creator in your project.

What does Subagent Creator need to run?

Going by SKILL.md and its folder, Subagent Creator needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Subagent Creator access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Subagent Creator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Subagent Creator use?

Subagent Creator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Subagent Creator use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Subagent Creator?

Skills that share tags, products or a category with Subagent Creator: Claude Code Agent Development (anthropics/claude-plugins-official, 38k stars), Subagent Driven Development (Asvarox/allkaraoke, 261 stars), Dispatching Parallel Agents (ultralisp/ultralisp, 258 stars) and Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Subagent Creator?

idoforgod (a GitHub user) maintains it in idoforgod/Dissertation-Simulator-AgenticWorkflow, which has 108 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on June 4, 2026.

Source: idoforgod/Dissertation-Simulator-AgenticWorkflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.