agent-file-structure

Agent File Structure Skill

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Install skill "agent-file-structure" with this command: npx skills add oocx/tfplan2md/oocx-tfplan2md-agent-file-structure

Agent File Structure Skill

Purpose

Defines the required structure and best practices for creating agent definition files in .github/agents/*.agent.md .

When to Use

  • When creating a new agent definition file

  • When reviewing or refactoring existing agent definitions

  • When ensuring consistency across the agent ecosystem

Required Structure

All agents must follow this structure:


description: Brief, specific description (≤100 chars) name: Workflow Engineer (coding agent) model: <model name>

Agent Name Agent

You are the Agent Name agent for this project...

Your Goal

Single, clear goal statement.

Boundaries

✅ Always Do: ... ⚠️ Ask First: ... 🚫 Never Do: ...

Context to Read

  • Relevant docs with links

Workflow

Step-by-step numbered approach

Output

What this agent produces

Key Principles

  • Specific over general - "Write unit tests for React components" beats "Help with testing"

  • Commands over descriptions - Include exact commands: npm test , dotnet build

  • Examples over explanations - Show real code examples, not abstract descriptions

  • Boundaries first - Clear rules prevent mistakes

Frontmatter Requirements

The YAML frontmatter at the top of each agent file must include:

  • description : Brief description (100 characters or less) that explains the agent's purpose

  • name : The agent's display name (include "(coding agent)" or local agent type)

  • model : VS Code agents only — The language model assigned to this agent (must exist in docs/ai-model-reference.md)

⚠️ Coding agents (*-coding-agent.agent.md ) must NOT include model: in frontmatter. The model: property is not supported on GitHub.com coding agents and causes a hard CAPIError: 400 The requested model is not supported error, preventing the agent from running. VS Code agents (without the -coding-agent suffix) should always include model: for LLM selection. See docs/ai-model-reference.md for details.

Section Guidelines

Your Goal

  • One clear sentence describing what the agent accomplishes

  • Focus on outcomes, not process

  • Example: "Implement features and tests according to specifications"

Boundaries

  • ✅ Always Do: Mandatory actions the agent must take (use specific commands)

  • ⚠️ Ask First: Situations requiring maintainer approval before proceeding

  • 🚫 Never Do: Actions that are explicitly forbidden or outside agent scope

Context to Read

  • List of documentation files the agent should review before starting work

  • Use relative paths with links (e.g., docs/spec.md )

Workflow

  • Numbered steps in logical sequence

  • Include exact commands where applicable

  • Specify decision points and branching logic

Output

  • List of artifacts the agent produces

  • Include file locations and formats

  • Clarify deliverables vs intermediate work

Best Practices

  • Be specific: Include exact file paths, command syntax, and expected outputs

  • Use examples: Show don't tell - include code snippets and command examples

  • Keep it actionable: Every instruction should be something the agent can execute

  • Avoid ambiguity: Use precise language and avoid vague terms like "usually" or "might"

Source Transparency

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