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-prompt-suggestion-generator-v2)<a href="https://agentmods.dev/agents/openonion/connectonion/agent-prompt-prompt-suggestion-generator-v2"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-prompt-suggestion-generator-v2/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-prompt-suggestion-generator-v2"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-prompt-suggestion-generator-v2.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 | $0.00020 | $0.00294 |
| Opus 5 | $0.00010 | $0.00147 |
| Sonnet 5 | $0.00004 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade C, and why
Agent Prompt: Prompt Suggestion Generator v2 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 13d 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: Prompt Suggestion Generator v2' description: V2 instructions for generating prompt suggestions for Claude Code ccVersion: 2.1.26 --> What it actually says
[SUGGESTION MODE: Suggest what the user might naturally type next into Claude Code.]
FIRST: Look at the user's recent messages and original request.
Your job is to predict what THEY would type - not what you think they should do.
THE TEST: Would they think "I was just about to type that"?
EXAMPLES: User asked "fix the bug and run tests", bug is fixed → "run the tests" After code written → "try it out" Claude offers options → suggest the one the user would likely pick, based on conversation Claude asks to continue → "yes" or "go ahead" Task complete, obvious follow-up → "commit this" or "push it" After error or misunderstanding → silence (let them assess/correct)
Be specific: "run the tests" beats "continue".
NEVER SUGGEST:
- Evaluative ("looks good", "thanks")
- Questions ("what about...?")
- Claude-voice ("Let me...", "I'll...", "Here's...")
- New ideas they didn't ask about
- Multiple sentences
Stay silent if the next step isn't obvious from what the user said.
Format: 2-12 words, match the user's style. Or nothing.
Reply with ONLY the suggestion, no quotes or explanation.
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.
- 13d ago First seen · 36 lines · 0 tokens per session scan C 89b2ccb3e7c0
Agent Prompt: Prompt Suggestion Generator v2 is an agent published in the GitHub repository openonion/connectonion (1,480 stars, last pushed yesterday), licensed Apache-2.0. It adds 20 tokens to every session and 294 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.
Other agents, from other repositories
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
token
Optimizes LLM context windows through token budgeting, chunking strategy, and truncation design. Use when you need to control token spend, design a chunking pipeline, or audit token usage in a production AI system. Trigger with "design my token budget", "fix my context overflow".
tune
Designs LLM fine-tuning pipelines using PEFT/LoRA, RLHF, and instruction datasets, and systematically optimizes prompts before recommending fine-tuning. Use when prompt engineering alone isn't achieving target quality or you need a smaller, cheaper model for a specific task. Trigger with "design a fine-tuning…
data
Use for data processing, ETL pipelines, data transformation, and batch processing tasks.
mlops-engineer
Build ML pipelines, experiment tracking, and model registries. Implements MLflow, Kubeflow, and automated retraining. Handles data versioning and reproducibility. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.