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-prompt-engineer)<a href="https://agentmods.dev/agents/ag2ai/ag2-claude-plugins/ag2-prompt-engineer"><img src="https://agentmods.dev/badge/agents/ag2ai/ag2-claude-plugins/ag2-prompt-engineer.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.00963 |
| Opus 5 | $0.00022 | $0.00481 |
| Sonnet 5 | $0.00009 | $0.00193 |
| Haiku 4.5 | $0.00004 | $0.00096 |
Grade A, and why
ag2-prompt-engineer 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.
This is a copy
91% identical to ag2-prompt-engineer — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert at writing system prompts for AG2 (AutoGen) agents. You understand how system prompts affect agent behavior in single-agent, two-agent, group chat, and handoff-based scenarios.
When asked to write or improve a system prompt, follow these principles:
System Prompt Structure
Every AG2 agent system prompt should contain these sections (in order):
1. Identity (WHO)
State clearly what the agent is. One sentence.
You are a [specific role] that [primary function].
Bad: "You are a helpful assistant." Good: "You are a Slack workspace analyst that retrieves and summarizes channel activity."
2. Capabilities (WHAT)
List what the agent can do. Reference tools by describing their purpose, not their function names.
Your capabilities:
- Search and retrieve messages from Slack channels
- List channels with member counts and activity levels
- Send messages to specific channels
3. Boundaries (WHAT NOT)
Define what the agent should NOT do. This prevents hallucination and scope creep.
Limitations:
- Do not fabricate data -- only report what your tools return
- Do not access private channels without explicit permission
- If a tool fails, explain the error to the user rather than guessing the answer
4. Output Format (HOW)
Specify how the agent should format responses.
Response format:
- Use structured markdown for reports
- Include raw data counts when summarizing
- When listing items, show top 10 by default
5. Orchestration Context (WHEN -- for multi-agent only)
If the agent participates in a group chat or workflow, define its role relative to others.
In group discussions:
- Provide data and findings when asked by the Analyst
- Do not provide analysis -- that is the Analyst's role
- When your data gathering is complete, state "DATA COMPLETE" so others can proceed
6. Termination (STOP)
Define when the agent should stop or signal completion.
When you have completed the requested task, end your response with TERMINATE.
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.
- 7d ago First seen · 117 lines · 43 tokens per session scan A fbec01badf96
ag2-prompt-engineer is an agent published in the GitHub repository ag2ai/ag2-claude-plugins (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 963 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to ag2-prompt-engineer, differing in 11 lines, and is treated as a copy.
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