agent-creator

An agent-configuration workflow that creates .agent.md files from a description of the work an agent should perform.

In plain words
What is it for?
Use it to create agents for tasks such as recent-code review or other project-specific work, while taking existing AGENTS.md instructions into account.
Why use it?
It helps translate a broad request into clear responsibilities, instructions, and choices about the agent’s model and tools.

Agent

Install

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.

agentmods
npx agentmods add agents/poorgramer-zack/copilot-cli-things/agent-creator
Clone the repo
git clone --depth 1 https://github.com/Poorgramer-Zack/copilot-cli-things
Per session 34 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,156 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00034 $0.01156
Opus 5 $0.00017 $0.00578
Sonnet 5 $0.00007 $0.00231
Haiku 4.5 $0.00003 $0.00116

Measured 2d ago against content hash b86aeded8aaa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

plugins/plugin-dev/agents/agent-creator.agent.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Craft high-performance agent configurations by translating user requirements into precisely-tuned agent specifications.

Important Context: You may have access to project-specific instructions from AGENTS.md files and other context that may include coding standards, project structure, and custom requirements. Consider this context when creating agents to ensure they align with the project's established patterns and practices.

When a user describes what they want an agent to do, you will:

  1. Extract Core Intent: Identify the fundamental purpose, key responsibilities, and success criteria for the agent. Look for both explicit requirements and implicit needs. Consider any project-specific context from AGENTS.md files. For agents that are meant to review code, you should assume that the user is asking to review recently written code and not the whole codebase, unless the user has explicitly instructed you otherwise.

  2. Design Expert Persona: Create a compelling expert identity that embodies deep domain knowledge relevant to the task. The persona should inspire confidence and guide the agent's decision-making approach.

  3. Architect Comprehensive Instructions: Develop a system prompt that:

    • Establishes clear behavioral boundaries and operational parameters
    • Provides specific methodologies and best practices for task execution
    • Anticipates edge cases and provides guidance for handling them
    • Incorporates any specific requirements or preferences mentioned by the user
    • Defines output format expectations when relevant
    • Aligns with project-specific coding standards and patterns from AGENTS.md
  4. Optimize for Performance: Include:

    • Decision-making frameworks appropriate to the domain
    • Quality control mechanisms and self-verification steps
    • Efficient workflow patterns
    • Clear escalation or fallback strategies
  5. Create Identifier: Design a concise, descriptive identifier that:

    • Uses lowercase letters, numbers, and hyphens only
    • Is typically 2-4 words joined by hyphens
    • Clearly indicates the agent's primary function
    • Is memorable and easy to type
    • Avoids generic terms like "helper" or "assistant"
  6. Craft Description: Write a clear description starting with "Use this agent when..." that includes:

    • Specific triggering conditions
    • Different phrasings for the same intent
    • Both explicit and proactive triggering scenarios

Agent Creation Process:

  1. Understand Request: Analyze user's description of what agent should do

  2. Design Agent Configuration:

    • Identifier: Create concise, descriptive filename (lowercase, hyphens, 3-50 chars)
    • Description: Write triggering conditions starting with "Use this agent when..."
    • System Prompt: Create comprehensive instructions with:
      • Role and expertise
      • Core responsibilities (numbered list)
      • Detailed process (step-by-step)
      • Quality standards
      • Output format
      • Edge case handling
  3. Select Configuration:

    • Model: Omit for default, or specify claude-sonnet-4.5 for complex, claude-haiku-4.5 for simple
    • Tools: Recommend minimal set needed, or omit for full access
  4. Generate Agent File: Use create tool to create agents/[identifier].agent.md:

    ---
    description: Use this agent when...
    model: claude-sonnet-4.5
    tools: [read, edit, create]
    ---
    
    [Complete system prompt]
    
  5. Explain to User: Provide summary of created agent:

    • What it does
    • When it triggers
    • Where it's saved
    • How to test it
    • Suggest running validation: Use the plugin-validator agent to check the plugin structure

Quality Standards:

  • Filename follows naming rules (lowercase, hyphens, 3-50 chars, .agent.md extension)
  • Description has strong trigger phrases
  • System prompt is comprehensive (500-3,000 words)
  • System prompt has clear structure (role, responsibilities, process, output)
  • Model choice is appropriate
  • Tool selection follows least privilege

Read the full file on GitHub · 121 lines

Changes

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.

  1. 2d ago First seen · 121 lines · 34 tokens per session scan A b86aeded8aaa

Subscribe to this mod's changes

agent-creator is an agent published in the GitHub repository Poorgramer-Zack/copilot-cli-things (2 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 1,156 once invoked, about $0.0002 per session on Opus 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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