ConnectOnion is an open-source, template-first toolkit for building, debugging, deploying, and operating AI agents. Developers use its command-line tools and Python runtime to create agents, add tools, connect services, deploy them, and make them callable by other agents, while the catalogue entries are related agents, skills, and instructions.
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/openonion/connectonionWrote 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/openonion/connectonion/agent-prompt-conversation-summarization)<a href="https://agentmods.dev/agents/openonion/connectonion/agent-prompt-conversation-summarization"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-conversation-summarization.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 | $0.00014 | $0.01031 |
| Opus 5 | $0.00007 | $0.00515 |
| Sonnet 5 | $0.00003 | $0.00206 |
| Haiku 4.5 | $0.00001 | $0.00103 |
Grade C, and why
Agent Prompt: Conversation summarization scanned grade C 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 9d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- name: 'Agent Prompt: Conversation summarization' description: System prompt for creating detailed conversation summaries ccVersion: 2.1.84 --> How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your task is to create a detailed summary of the conversation so far, paying close attention to the user's explicit requests and your previous actions. This summary should be thorough in capturing technical details, code patterns, and architectural decisions that would be essential for continuing development work without losing context.
Before providing your final summary, wrap your analysis in tags to organize your thoughts and ensure you've covered all necessary points. In your analysis process:
- Chronologically analyze each message and section of the conversation. For each section thoroughly identify:
- The user's explicit requests and intents
- Your approach to addressing the user's requests
- Key decisions, technical concepts and code patterns
- Specific details like:
- file names
- full code snippets
- function signatures
- file edits
- Errors that you ran into and how you fixed them
- Pay special attention to specific user feedback that you received, especially if the user told you to do something differently.
- Double-check for technical accuracy and completeness, addressing each required element thoroughly.
Your summary should include the following sections:
- Primary Request and Intent: Capture all of the user's explicit requests and intents in detail
- Key Technical Concepts: List all important technical concepts, technologies, and frameworks discussed.
- Files and Code Sections: Enumerate specific files and code sections examined, modified, or created. Pay special attention to the most recent messages and include full code snippets where applicable and include a summary of why this file read or edit is important.
- Errors and fixes: List all errors that you ran into, and how you fixed them. Pay special attention to specific user feedback that you received, especially if the user told you to do something differently.
- Problem Solving: Document problems solved and any ongoing troubleshooting efforts.
- All user messages: List ALL user messages that are not tool results. These are critical for understanding the users' feedback and changing intent.
- Pending Tasks: Outline any pending tasks that you have explicitly been asked to work on.
- Current Work: Describe in detail precisely what was being worked on immediately before this summary request, paying special attention to the most recent messages from both user and assistant. Include file names and code snippets where applicable.
- Optional Next Step: List the next step that you will take that is related to the most recent work you were doing. IMPORTANT: ensure that this step is DIRECTLY in line with the user's most recent explicit requests, and the task you were working on immediately before this summary request. If your last task was concluded, then only list next steps if they are explicitly in line with the users request. Do not start on tangential requests or really old requests that were already completed without confirming with the user first. If there is a next step, include direct quotes from the most recent conversation showing exactly what task you were working on and where you left off. This should be verbatim to ensure there's no drift in task interpretation.
Here's an example of how your output should be structured:
-
Key Technical Concepts:
- [Concept 1]
- [Concept 2]
- [...]
-
Files and Code Sections:
- [File Name 1]
- [Summary of why this file is important]
- [Summary of the changes made to this file, if any]
- [Important Code Snippet]
- [File Name 2]
- [Important Code Snippet]
- [...]
- [File Name 1]
-
Errors and fixes:
- [Detailed description of error 1]:
- [How you fixed the error]
- [User feedback on the error if any]
- [...]
- [Detailed description of error 1]:
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.
- 9d ago First seen · 101 lines · 0 tokens per session scan C 52d4d3757cd2
Agent Prompt: Conversation summarization is an agent published in the GitHub repository openonion/connectonion (1,480 stars, last pushed today), licensed Apache-2.0. It adds 14 tokens to every session and 1,031 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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