What is creating-agents?

A scaffold generator for a new Agent2 agent service. It creates the service's data schema, API, tools, configuration, container setup, and Docker Compose entry according to the project's conventions.

Use it when adding an agent with a name, purpose, output fields, tools, and optional document-search needs, including domain-expert agents that should first use the brain-clone process.

It gives a new backend AI worker the expected project structure and supporting files. This reduces the setup work before implementation begins.

How Artesiana/agent2 configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to Artesiana/agent2. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/Artesiana/agent2/main/.agents/skills/creating-agents/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/Artesiana/agent2

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/artesiana/agent2/creating-agents/github.svg)](https://agentmods.dev/skills/artesiana/agent2/creating-agents)

Your own site

<a href="https://agentmods.dev/skills/artesiana/agent2/creating-agents"><img src="https://agentmods.dev/badge/skills/artesiana/agent2/creating-agents/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/artesiana/agent2/creating-agents"><img src="https://agentmods.dev/badge/skills/artesiana/agent2/creating-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 902 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00050	$0.00902
Opus 5.5	$0.00020	$0.00361
Sonnet 5.5	$0.00010	$0.00180
Haiku 4.5	$0.00005	$0.00090

Measured 2mo ago against content hash 0678bca274d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

creating-agents scanned grade A with 1 finding 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 2mo 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl http://localhost:<port>/health # Health check

.agents/skills/creating-agents/SKILL.md · 93 lines

How it starts

The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Creating Agents

Overview

Scaffold a complete Agent2 agent service. One agent = one schema, one API, one Docker service.

If the requested agent is a domain expert, regulated workflow, document reviewer, compliance worker, or professional brain clone, use brain-clone first and study agents/procurement-compliance-officer.

When to Activate

User says "create agent", "new agent", "scaffold agent", "add agent"
User describes a use case that needs a new agent service
User wants to add a new backend AI worker

Steps

1. Gather Requirements

Ask the user:

Name (kebab-case, e.g. support-ticket)
What it does (one sentence)
Output fields (what the structured result should contain)
Tools needed (what the agent should be able to call)
Knowledge needed? (does it need to search documents?)

2. Generate Files

Create all files in agents/<name>/:

File	What to put
`__init__.py`	Empty
`schemas.py`	Pydantic `BaseModel` with `Field()` descriptions. Use `Literal` for enums, validators for constraints.
`tools.py`	One function per tool. Include docstrings. Stub implementations.
`agent.py`	`create_agent()` with `instructions=` and `@agent.tool_plain` registrations
`config.yaml`	name, description, model (empty = use DEFAULT_MODEL), port, timeout, collections
`main.py`	`from shared.api import create_app; app = create_app("<name>")`
`Dockerfile`	Copy from `agents/example-agent/Dockerfile`, change agent paths
`tests/promptfoo/<name>/eval.yaml`	For domain agents, behavior-level evals

3. Key Rules

# agent.py — ALWAYS use instructions=, NEVER system_prompt=
agent = create_agent(
    name="my-agent",
    output_type=MySchema,
    instructions="You are...",
    toolsets=[knowledge_server] if needs_knowledge else [],
)

# Tools — ALWAYS @agent.tool_plain, delegate to tools.py
@agent.tool_plain
def my_tool(arg: str) -> dict:
    """Docstring is the tool description the LLM sees."""
    return tools.my_tool(arg)

Read the full file on GitHub · 93 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.

2mo ago First seen · 93 lines · 50 tokens per session scan A 0678bca274d6

Subscribe to this mod's changes

creating-agents is a skill published in the GitHub repository Artesiana/agent2 (37 stars, last pushed 6mo ago), licensed MIT. It adds 50 tokens to every session and 902 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Backend and APIs Building agents LLM Production ai-agents ai-framework fastapi pydantic-ai

creating-agents next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
creating-agentsArtesiana/agent2	Skill	37	6mo ago	MIT	A	50	Claude Code, Codex
background-taskvstorm-co/full-stack-ai-agent-template	Skill	2.0k	7d ago	MIT	A	62	Claude Code
channel-botvstorm-co/full-stack-ai-agent-template	Skill	2.0k	7d ago	MIT	A	53	Claude Code
mcp_protocolfdueblab/Micro-Agent	Skill	100	1mo ago	MIT	A	0	Claude Code, Codex
code_analysis_patternsfdueblab/Micro-Agent	Skill	100	1mo ago	MIT	A	0	Claude Code, Codex
gitlab_writeParendumOU/Nexora	Skill	26	28d ago	MIT	A	0	Claude Code, Codex
external-im-integrationopensquad-ai/opensquad	Skill	7	17d ago	MIT	A	50	Claude Code, Codex

agent2: Skill for Claude Code