new-agent

new-agent is a skill for Claude Code from ag2ai/ag2-claude-plugins. It costs 35 tokens per session (558 once invoked), scanned A, original, Apache-2.0.

A scaffold for creating a standalone AG2 ConversableAgent, an agent that can converse with users and call defined tools.

In plain words
What is it for?
Creating an AG2 agent with a purpose, tools, a system prompt, model settings, and optional external API access.
Why use it?
It gives a new agent a consistent file structure, tool-function pattern, error handling, and language-model configuration.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the ag2-agent-scaffold plugin — 2 skills shipped together

Good fit Creating an AG2 agent with a purpose, tools, a system prompt, model settings, and optional external API access.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ag2ai/ag2-claude-plugins/new-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.

Any agent
npx skills add ag2ai/ag2-claude-plugins --skill new-agent
Clone the repo
git clone --depth 1 https://github.com/ag2ai/ag2-claude-plugins

Made for: Claude Code.

Or install ag2-agent-scaffold, the plugin that ships this one along with the rest of its 2 skills.

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.

agentmods badge for new-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/ag2ai/ag2-claude-plugins/new-agent.svg)](https://agentmods.dev/skills/ag2ai/ag2-claude-plugins/new-agent)
Your own site
<a href="https://agentmods.dev/skills/ag2ai/ag2-claude-plugins/new-agent"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-claude-plugins/new-agent.svg" alt="Measured on agentmods" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 558 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.00558
Opus 5 $0.00017 $0.00279
Sonnet 5 $0.00007 $0.00112
Haiku 4.5 $0.00003 $0.00056

Measured 7d ago against content hash 8aa492c3e917, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

new-agent 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 7d 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/ag2-agent-scaffold/skills/new-agent/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are scaffolding a new AG2 (AutoGen) agent. Follow the AG2 framework patterns exactly.

Instructions

  1. Ask the user for:

    • Agent name and purpose
    • What tools/capabilities it needs
    • Which LLM model to use (default: gpt-4o-mini)
    • Whether it needs external API access
  2. Create the agent following this exact structure:

File Structure

agents/<agent-name>/
  <agent_name>.py    # Agent definition + tools
  README.md          # Capabilities documentation

Agent Code Pattern

import json
from autogen import ConversableAgent, LLMConfig
from autogen.tools import tool

# --- Tool Functions ---
# Each tool returns a JSON string with {"success": bool, "data": ..., "error": ...}

@tool()
def tool_name(param1: str, param2: int = 10) -> str:
    """Clear description of what this tool does.

    Args:
        param1: Description of param1
        param2: Description of param2 (default: 10)
    """
    try:
        # Implementation
        result = {"key": "value"}
        return json.dumps({"success": True, "data": result})
    except Exception as e:
        return json.dumps({"success": False, "error": str(e)})


# --- Agent Definition ---
agent = ConversableAgent(
    name="agent_name",
    description="One-line description for orchestrator routing",
    system_message="""You are a [role description].

Your capabilities:
- Capability 1
- Capability 2

Guidelines:
- Always use the appropriate tool for the task
- Return structured responses
- Handle errors gracefully and explain what went wrong
""",
    llm_config=LLMConfig({"api_type": "anthropic", "model": "claude-sonnet-4-6"}),
    functions=[tool_name],
)

Key Rules

  • Tool functions MUST have docstrings (used for LLM function calling schema)
  • System messages should be specific about the agent's role and boundaries
  • Agent description is used by orchestrators to route tasks -- keep it concise
  • Use @tool() decorator from autogen.tools
  • Group related tools in the same file
  • Never use bare except: -- always catch specific exceptions or Exception

Read the full file on GitHub · 80 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. 7d ago First seen · 80 lines · 35 tokens per session scan A 8aa492c3e917

Subscribe to this mod's changes

new-agent is a skill published in the GitHub repository ag2ai/ag2-claude-plugins (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 558 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens