| name | agent-creator |
| description | Authoritative templates and scaffolding for creating agent system prompts (primary agents and subagents). This skill should be used when creating new agents, reviewing existing agent prompts for template compliance, verifying agent structure, or extracting knowledge into agent prompts. Contains YAML templates with section-by-section instructions and scaffolding scripts for generating skeleton files. |
Agent Creator
This skill provides authoritative templates and tools for creating agent system prompts.
When to Use This Skill
- Creating new primary agents or subagents
- Reviewing existing agent prompts for template compliance
- Verifying agent structure against templates
- Extracting knowledge into agent prompts (need to know valid section names)
- Understanding what sections an agent should have
Agent Types
Primary Agents
Full-featured agents that may orchestrate subagents. They have:
- Complete identity (Role Definition, Who You Are/NOT, Philosophy)
- Cognitive approach (When to Think Deeply, Analysis Mindset)
- Orchestration patterns (if they spawn subagents)
- Knowledge Base with domain expertise
- Multi-phase Workflow
- Learned Constraints
Template: references/primary-agent.yaml
Subagents
Focused specialists spawned by primary agents via Task tool. They have:
- Narrow identity (Opening Statement)
- Core Responsibilities (3-4 focused tasks)
- Domain Strategy
- Structured Output Format
- Execution Boundaries
Template: references/subagent.yaml
Scripts
Execute scaffolding via justfile or directly with uv:
Via Justfile (Recommended)
just -f {base_dir}/justfile <recipe> [args...]
| Recipe | Arguments | Description |
|---|---|---|
scaffold-primary |
name path |
Create primary agent skeleton |
scaffold-subagent |
name path |
Create subagent skeleton |
Direct Execution
uv run {base_dir}/scripts/scaffold_agent.py <type> <name> --path <path>
| Argument | Description |
|---|---|
type |
primary or subagent |
name |
Agent name (kebab-case) |
--path |
Directory to create agent file |
Examples
# Create a primary agent
just -f {base_dir}/justfile scaffold-primary my-agent .opencode/agent
# Create a subagent
just -f {base_dir}/justfile scaffold-subagent code-analyzer .opencode/agent
# Direct execution
uv run {base_dir}/scripts/scaffold_agent.py primary my-agent --path .opencode/agent
Template Reference
The YAML templates in references/ are the authoritative source for agent structure.
Each template contains:
- frontmatter: Required and optional metadata fields
- sections: Ordered list of sections with:
id: Unique section identifiertitle: Section headingtype: Content type (text, bullet-list, structured, etc.)instruction: Detailed guidance on what to writetemplate: Example format/structureoptional: Whether section can be omitted
Reading Templates
To understand what an agent section should contain:
- Read the appropriate template from
references/ - Find the section by
idortitle - Follow the
instructionfield for guidance - Use the
templatefield as a structural example
Domain Patterns
Variable Notation Standard
Apply consistent variable notation across all prompts:
Assignment formats:
- Static:
VARIABLE_NAME: "fixed-value" - Dynamic:
VARIABLE_NAME: $ARGUMENTS - Parsing:
VARIABLE_NAME: [description-of-what-to-extract]
Usage in instructions:
- Always:
{{VARIABLE_NAME}}(double curly braces) - Never:
$VARIABLE_NAME,[[VARIABLE_NAME]], or bareVARIABLE_NAME
Rationale: {{}} notation matches LLM training on template systems (Jinja2, Handlebars, Mustache). It's unambiguous and visually clear.
Workflow Integration
When Prompter creates an agent:
- Analyze plan - Identify requirements
- Determine type - Primary (orchestrator) or Subagent (specialist)
- Scaffold - Run scaffolding script to create skeleton
- Reference template - Read YAML for section instructions
- Fill sections - Work through todo list, section by section
- Consider skills - Does this agent need domain expertise externalized?
The scaffolding creates the structure; the templates guide the content.