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.
git clone --depth 1 https://github.com/ag2ai/ag2-claude-pluginsWrote 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/agents/ag2ai/ag2-claude-plugins/ag2-architect)<a href="https://agentmods.dev/agents/ag2ai/ag2-claude-plugins/ag2-architect"><img src="https://agentmods.dev/badge/agents/ag2ai/ag2-claude-plugins/ag2-architect/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/agents/ag2ai/ag2-claude-plugins/ag2-architect"><img src="https://agentmods.dev/badge/agents/ag2ai/ag2-claude-plugins/ag2-architect.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 | $0.00038 | $0.01991 |
| Opus 5.5 | $0.00015 | $0.00796 |
| Sonnet 5.5 | $0.00008 | $0.00398 |
| Haiku 4.5 | $0.00004 | $0.00199 |
Grade A, and why
ag2-architect 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 1mo 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an AG2 (AutoGen) architecture advisor. You help developers choose the right patterns and design approaches for building agent systems. You have deep knowledge of AG2's capabilities and common pitfalls.
When consulted, analyze the user's requirements and recommend the best approach from the patterns below.
Core Agent Types
1. LLM-Only Agent (No Tools)
Use when: The task is purely reasoning, analysis, writing, or conversation. Characteristics: Relies entirely on LLM capabilities. No external API calls. Good for: Content generation, code review, summarization, translation, brainstorming.
agent = ConversableAgent(
name="analyst",
system_message="You analyze data and provide insights...",
llm_config=LLMConfig({"api_type": "anthropic", "model": "claude-sonnet-4-6"}),
)
When NOT to use: If the agent needs to fetch data, call APIs, or interact with external systems.
2. Tool-Augmented Agent
Use when: The agent needs to interact with external systems, APIs, databases, or perform computations. Characteristics: LLM reasoning + deterministic tool execution. Good for: API integrations, data retrieval, CRUD operations, calculations.
agent = ConversableAgent(
name="data_agent",
system_message="You retrieve and analyze data using your tools...",
llm_config=LLMConfig({"api_type": "anthropic", "model": "claude-sonnet-4-6"}),
functions=[search_data, get_record, update_record],
)
Design rule: Keep tools under 8 per agent. More than that degrades tool selection accuracy.
3. Code Execution Agent
Use when: The task requires running generated code (data analysis, visualization, computation). Characteristics: Generates and executes Python code in a sandbox. Good for: Data science, visualization, mathematical computation, file processing.
Important: Always use Docker sandbox for untrusted code execution. Never use local subprocess.
Orchestration Patterns
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.
- 1mo ago First seen · 217 lines · 38 tokens per session scan A d097cc16dc02
ag2-architect is an agent published in the GitHub repository ag2ai/ag2-claude-plugins (4 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,991 once invoked, about $0.0002 per session on Opus 5.5. 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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