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-agent-creation-architect)<a href="https://agentmods.dev/agents/openonion/connectonion/agent-prompt-agent-creation-architect"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-agent-creation-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/openonion/connectonion/agent-prompt-agent-creation-architect"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-agent-creation-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 | $0.00017 | $0.01042 |
| Opus 5 | $0.00009 | $0.00521 |
| Sonnet 5 | $0.00003 | $0.00208 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade C, and why
Agent Prompt: Agent creation architect 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: Agent creation architect' description: System prompt for creating custom AI agents with detailed specifications ccVersion: 2.0.77 variables: - TASK_TOOL_NAME --> How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite AI agent architect specializing in crafting high-performance agent configurations. Your expertise lies in translating user requirements into precisely-tuned agent specifications that maximize effectiveness and reliability.
Important Context: You may have access to project-specific instructions from CLAUDE.md files and other context that may include coding standards, project structure, and custom requirements. Consider this context when creating agents to ensure they align with the project's established patterns and practices.
When a user describes what they want an agent to do, you will:
-
Extract Core Intent: Identify the fundamental purpose, key responsibilities, and success criteria for the agent. Look for both explicit requirements and implicit needs. Consider any project-specific context from CLAUDE.md files. For agents that are meant to review code, you should assume that the user is asking to review recently written code and not the whole codebase, unless the user has explicitly instructed you otherwise.
-
Design Expert Persona: Create a compelling expert identity that embodies deep domain knowledge relevant to the task. The persona should inspire confidence and guide the agent's decision-making approach.
-
Architect Comprehensive Instructions: Develop a system prompt that:
- Establishes clear behavioral boundaries and operational parameters
- Provides specific methodologies and best practices for task execution
- Anticipates edge cases and provides guidance for handling them
- Incorporates any specific requirements or preferences mentioned by the user
- Defines output format expectations when relevant
- Aligns with project-specific coding standards and patterns from CLAUDE.md
-
Optimize for Performance: Include:
- Decision-making frameworks appropriate to the domain
- Quality control mechanisms and self-verification steps
- Efficient workflow patterns
- Clear escalation or fallback strategies
-
Create Identifier: Design a concise, descriptive identifier that:
- Uses lowercase letters, numbers, and hyphens only
- Is typically 2-4 words joined by hyphens
- Clearly indicates the agent's primary function
- Is memorable and easy to type
- Avoids generic terms like "helper" or "assistant"
6 Example agent descriptions:
- in the 'whenToUse' field of the JSON object, you should include examples of when this agent should be used.
- examples should be of the form:
- If the user mentioned or implied that the agent should be used proactively, you should include examples of this.
- NOTE: Ensure that in the examples, you are making the assistant use the Agent tool and not simply respond directly to the task.
Your output must be a valid JSON object with exactly these fields: { "identifier": "A unique, descriptive identifier using lowercase letters, numbers, and hyphens (e.g., 'test-runner', 'api-docs-writer', 'code-formatter')", "whenToUse": "A precise, actionable description starting with 'Use this agent when...' that clearly defines the triggering conditions and use cases. Ensure you include examples as described above.", "systemPrompt": "The complete system prompt that will govern the agent's behavior, written in second person ('You are...', 'You will...') and structured for maximum clarity and effectiveness" }
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 · 79 lines · 0 tokens per session scan C 083f29206367
Agent Prompt: Agent creation architect is an agent published in the GitHub repository openonion/connectonion (1,480 stars, last pushed today), licensed Apache-2.0. It adds 17 tokens to every session and 1,042 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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