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.
npx skills add ag2ai/ag2-skills --skill ag2-quickstartgit clone --depth 1 https://github.com/ag2ai/ag2-skillsWrote 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.
[](https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-quickstart)<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-quickstart"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-quickstart/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.
<a href="https://agentmods.dev/skills/ag2ai/ag2-skills/ag2-quickstart"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-skills/ag2-quickstart.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00106 | $0.01849 |
| Opus 5 | $0.00053 | $0.00924 |
| Sonnet 5 | $0.00021 | $0.00370 |
| Haiku 4.5 | $0.00011 | $0.00185 |
Grade A, and why
ag2-quickstart 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quickstart: build your first AG2 Agent
When to use
- The user is starting from a blank file and wants a working AG2 agent.
- The user is unsure which provider config to use.
- The user wants to chain follow-up turns without losing conversation context.
- A larger task needs the basic Agent setup as its skeleton — start here, then layer the relevant feature skill on top.
Prerequisites
Required step: install dependencies before finishing. After writing AG2 code, install the matching provider extra (plus any feature extra the task needs, e.g.
ag-ui,tracing). Run the install:pip install "ag2[openai]" # swap in the provider/extra you usedIf you cannot run commands, state the exact
pip installcommand. This is part of finishing the task, not an optional note.For a multi-file project (more than a throwaway script), also drop a
requirements.txtpinningag2with the extras you used (e.g.ag2[openai]>=0.14.0) so the environment is reproducible.
Install the right provider extra and have a key for it. Each *Config requires its provider SDK — without the matching extra you'll see ImportError: ... requires optional dependencies. Install with pip install "ag2[<provider>]".
| Provider | Install | Env var | Config class |
|---|---|---|---|
| OpenAI | pip install "ag2[openai]" |
OPENAI_API_KEY |
OpenAIConfig, OpenAIResponsesConfig |
| Anthropic | pip install "ag2[anthropic]" |
ANTHROPIC_API_KEY |
AnthropicConfig |
| Gemini (API key) | pip install "ag2[gemini]" |
GEMINI_API_KEY (or GOOGLE_API_KEY) |
GeminiConfig |
| Vertex AI (Gemini) | pip install "ag2[gemini]" |
service-account / ADC | VertexAIConfig |
| Ollama (local) | pip install "ag2[ollama]" |
— | OllamaConfig |
| DashScope (Qwen) | pip install "ag2[dashscope]" |
DASHSCOPE_API_KEY |
DashScopeConfig |
Load env vars from a project-root .env with python-dotenv so scripts pick up keys without exporting them in your shell:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 154 lines · 106 tokens per session scan A da5a0e0556da
ag2-quickstart is a skill published in the GitHub repository ag2ai/ag2-skills (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 106 tokens to every session and 1,849 once invoked, about $0.0005 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.
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