create-agent — community create-agent, agent-skills-self-learning-sample, community, ide skills

v1.0.0

About this Skill

Perfect for AI Developer Agents needing streamlined agent creation and registration processes. Scaffold a new agent in this multi-agent system following the project skeleton. Use when the user wants to create, add, or scaffold a new agent, or asks about adding agents to the workflow.

zzfancitizen zzfancitizen
[0]
[0]
Updated: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Locale and body language aligned
Review Score
7/11
Quality Score
42
Canonical Locale
en
Detected Body Locale
en

Perfect for AI Developer Agents needing streamlined agent creation and registration processes. Scaffold a new agent in this multi-agent system following the project skeleton. Use when the user wants to create, add, or scaffold a new agent, or asks about adding agents to the workflow.

Core Value

Empowers agents to create new directory structures, write SKILL.md files, and register agents using Python, providing a structured approach to agent development with steps including writing agent.py and updating workflow graphs.

Ideal Agent Persona

Perfect for AI Developer Agents needing streamlined agent creation and registration processes.

Capabilities Granted for create-agent

Creating new AI agents from scratch
Registering custom agents in src/agents/__init__.py
Updating workflow graphs to integrate new agents

! Prerequisites & Limits

  • Requires Python environment
  • Specific directory structure and file naming conventions must be followed

Why this page is reference-only

  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

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Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is create-agent?

Perfect for AI Developer Agents needing streamlined agent creation and registration processes. Scaffold a new agent in this multi-agent system following the project skeleton. Use when the user wants to create, add, or scaffold a new agent, or asks about adding agents to the workflow.

How do I install create-agent?

Run the command: npx killer-skills add zzfancitizen/agent-skills-self-learning-sample/create-agent. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for create-agent?

Key use cases include: Creating new AI agents from scratch, Registering custom agents in src/agents/__init__.py, Updating workflow graphs to integrate new agents.

Which IDEs are compatible with create-agent?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for create-agent?

Requires Python environment. Specific directory structure and file naming conventions must be followed.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add zzfancitizen/agent-skills-self-learning-sample/create-agent. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use create-agent immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

create-agent

Install create-agent, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Create Agent

Follow this checklist when creating a new agent. Replace <name> with the agent name (lowercase, e.g. reviewer) and <Name> with the PascalCase class name (e.g. ReviewerAgent).

Progress:
- [ ] Step 1: Create directory structure
- [ ] Step 2: Write SKILL.md
- [ ] Step 3: Write agent.py
- [ ] Step 4: Write package __init__.py
- [ ] Step 5: Register in src/agents/__init__.py
- [ ] Step 6: Add to workflow graph
- [ ] Step 7: Update router
- [ ] Step 8: Run verification

Step 1: Create directory structure

src/agents/<name>/
├── __init__.py
├── agent.py
└── skills/
    └── <skill_name>/
        ├── SKILL.md
        └── tools/          # optional, only if agent needs tools
            ├── __init__.py
            └── <tool>.py

Step 2: Write SKILL.md

Create src/agents/<name>/skills/<skill_name>/SKILL.md:

markdown
1--- 2name: <skill_name> 3description: Brief description of what this skill does 4tags: [<tag1>, <tag2>] 5--- 6 7# <Skill Title> 8 9Detailed instructions the LLM follows when this skill is loaded...

Step 3: Write agent.py

Create src/agents/<name>/agent.py following this skeleton:

python
1""" 2<Name> Agent - <one-line purpose> 3""" 4 5from pathlib import Path 6 7from langchain_core.messages import BaseMessage, AIMessage 8 9from ..base import BaseAgent 10from ...skills.registry import SkillRegistry 11from ...skills.loader import SkillLoader 12 13 14class <Name>Agent(BaseAgent): 15 """<Name> Agent — <brief description>""" 16 17 def __init__(self, skill_registry: SkillRegistry, **kwargs): 18 # Discover and register all skills from skills/ subdirectory 19 agent_dir = Path(__file__).parent 20 skills = SkillLoader.load_agent_skills(agent_dir, lazy=skill_registry._lazy) 21 for skill in skills: 22 skill_registry.register(skill) 23 24 super().__init__(skill_registry, **kwargs) 25 26 # If agent has tools, register them here: 27 # from .skills.<skill_name>.tools.<tool_module> import <tool> 28 # self.register_tools([<tool>]) 29 30 @property 31 def agent_name(self) -> str: 32 return "<Name>Agent" 33 34 @property 35 def default_skills(self) -> list[str]: 36 return ["<skill_name>"] 37 38 @property 39 def base_system_prompt(self) -> str: 40 return """<system prompt for this agent>""" 41 42 def process(self, messages: list[BaseMessage], **kwargs) -> dict: 43 response = self.invoke(messages) 44 content = response.content 45 return { 46 "result": content, 47 "raw_response": response, 48 }

Step 4: Write package __init__.py

Create src/agents/<name>/__init__.py:

python
1""" 2<Name> Agent Package 3""" 4 5from .agent import <Name>Agent 6 7__all__ = ["<Name>Agent"]

Step 5: Register in src/agents/__init__.py

Add the import and export:

python
1from .<name> import <Name>Agent

And add "<Name>Agent" to the __all__ list.

Step 6: Add to workflow graph

Edit src/graph/workflow.py:

  1. Import the new agent class in the imports section.

  2. Instantiate it in create_workflow() alongside the other agents:

    python
    1<name> = <Name>Agent(skill_registry, **agent_kwargs)
  3. Define a node function following the existing pattern:

    python
    1def <name>_node(state: AgentState) -> dict: 2 input_data = {"messages": state["messages"]} 3 result = <name>.process(state["messages"]) 4 output = { 5 "current_agent": "<name>", 6 # map result keys to AgentState fields as needed 7 } 8 _log_agent("<Name>Agent", input_data, output) 9 return output
  4. Register the node:

    python
    1workflow.add_node("<name>", <name>_node)
  5. Add edges — connect the node to the graph with add_edge or add_conditional_edges depending on the routing logic.

  6. If the new agent's output needs a new state field, add it to AgentState in src/graph/state.py and to create_initial_state().

Step 7: Update router

If the router should be able to route to this new agent:

  1. Add the new route to RouterAgent.VALID_ROUTES in src/agents/router/agent.py.

  2. Add the route description to RouterAgent.base_system_prompt.

  3. Update src/agents/router/skills/routing/SKILL.md with a new section describing when to route to this agent.

  4. Update route_decision() in src/graph/workflow.py to handle the new route value.

Step 8: Run verification

Run the verification skill to confirm nothing is broken:

bash
1uv run python -c "import src.main; import src.graph; import src.agents; import src.skills" 2uv run python -m src.main --test

Expected: "Workflow created successfully", correct skill count, "All tests passed".

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