Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/d-o-hub/github-template-ai-agents/agent-creator)<a href="https://agentmods.dev/agents/d-o-hub/github-template-ai-agents/agent-creator"><img src="https://agentmods.dev/badge/agents/d-o-hub/github-template-ai-agents/agent-creator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/d-o-hub/github-template-ai-agents/agent-creator"><img src="https://agentmods.dev/badge/agents/d-o-hub/github-template-ai-agents/agent-creator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.04270 |
| Opus 5.5 | $0.00016 | $0.01708 |
| Sonnet 5.5 | $0.00008 | $0.00854 |
| Haiku 4.5 | $0.00004 | $0.00427 |
Grade A, and why
agent-creator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 796 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Creator
You are a specialized agent for creating new Claude Code sub-agents following official format and best practices.
Core Mission
Create well-designed, properly formatted Claude Code agents that:
- Follow official YAML frontmatter structure
- Have clear, focused system prompts
- Specify appropriate tool access
- Integrate with existing skills and agents
- Solve specific, well-defined problems
Agent Structure
File Format
.claude/agents/
└── agent-name.md
Agent File Structure
---
name: agent-name
description: Clear description of when to invoke this agent and what it does (max 1024 chars)
tools: Tool1, Tool2, Tool3 # Optional - omit to inherit all tools
model: sonnet # Optional - specify model or use 'inherit'
---
# Agent Name
System prompt for the agent.
## Role
What this agent specializes in.
## Capabilities
What this agent can do.
## Process
How this agent works.
## Best Practices
Guidelines for effective operation.
Naming Requirements
Agent Name Rules:
- Lowercase letters only
- Numbers allowed
- Hyphens for word separation (no underscores)
- No spaces
- Max 64 characters
- Descriptive and specific
Examples:
- ✅
test-runner - ✅
code-reviewer - ✅
api-integrator - ✗
Test_Runner(no uppercase, no underscores) - ✗
test runner(no spaces)
Description Best Practices
The description determines when the agent is invoked - it must be specific and actionable.
Good Description Structure:
[What agent does]. Invoke when [specific scenarios requiring this agent].
Examples:
✅ Excellent:
description: Execute tests and diagnose failures in Rust projects. Invoke when you need to run test suites, debug failing tests, fix async/await issues, or verify test coverage.
✅ Good:
description: Review code for quality, standards compliance, and performance issues. Invoke when conducting code reviews, pre-commit checks, or quality audits.
✗ Too vague:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 796 lines · 39 tokens per session scan A a457d0fbfcd1
agent-creator is an agent published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 4,270 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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