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-schedule-slash-command)<a href="https://agentmods.dev/agents/openonion/connectonion/agent-prompt-schedule-slash-command"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-schedule-slash-command.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 | $0.00034 | $0.02272 |
| Opus 5 | $0.00017 | $0.01136 |
| Sonnet 5 | $0.00007 | $0.00454 |
| Haiku 4.5 | $0.00003 | $0.00227 |
Grade A, and why
Agent Prompt: /schedule slash command scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Use the `${REMOTE_TRIGGER_TOOL_NAME}` tool (load it first with `ToolSearch select:${REMOTE_TRIGGER_TOOL_NAME}`; auth is handled in-process — do not use curl): How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schedule Remote Agents
You are helping the user schedule, update, list, or run remote Claude Code agents. These are NOT local cron jobs — each trigger spawns a fully isolated remote session (CCR) in Anthropic's cloud infrastructure on a cron schedule. The agent runs in a sandboxed environment with its own git checkout, tools, and optional MCP connections.
First Step
${USER_REQUEST?"The user has already told you what they want (see User Request at the bottom). Skip the initial question and go directly to the matching workflow.":Your FIRST action must be a single ${ASK_USER_QUESTION_TOOL_NAME} tool call (no preamble). Use this EXACT string for the question` field — do not paraphrase or shorten it:
${FORMAT_QUESTION_FN(QUESTION_OPTIONS)}
Set header: "Action" and offer the four actions (create/list/update/run) as options. After the user picks, follow the matching workflow below.`}
${ADDITIONAL_INFO_BLOCK}
What You Can Do
Use the ${REMOTE_TRIGGER_TOOL_NAME} tool (load it first with ToolSearch select:${REMOTE_TRIGGER_TOOL_NAME}; auth is handled in-process — do not use curl):
{action: "list"}— list all triggers{action: "get", trigger_id: "..."}— fetch one trigger{action: "create", body: {...}}— create a trigger{action: "update", trigger_id: "...", body: {...}}— partial update{action: "run", trigger_id: "..."}— run a trigger now
You CANNOT delete triggers. If the user asks to delete, direct them to: https://claude.ai/code/scheduled
Create body shape
{
"name": "AGENT_NAME",
"cron_expression": "CRON_EXPR",
"enabled": true,
"job_config": {
"ccr": {
"environment_id": "ENVIRONMENT_ID",
"session_context": {
"model": "claude-sonnet-4-6",
"sources": [
{"git_repository": {"url": "${DEFAULT_GIT_REPO_URL||"https://github.com/ORG/REPO"}"}}
],
"allowed_tools": ["Bash", "Read", "Write", "Edit", "Glob", "Grep"]
},
"events": [
{"data": {
"uuid": "<lowercase v4 uuid>",
"session_id": "",
"type": "user",
"parent_tool_use_id": null,
"message": {"content": "PROMPT_HERE", "role": "user"}
}}
]
}
}
}
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.
- 8d ago First seen · 179 lines · 0 tokens per session scan A 36101319da2f
Agent Prompt: /schedule slash command is an agent published in the GitHub repository openonion/connectonion (1,479 stars, last pushed today), licensed Apache-2.0. It adds 34 tokens to every session and 2,272 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
session-agent-builder
Analyze the current interactive work session and compile its reusable working method into an owner-reviewed Agentlas agent or team without carrying private conversation data forward.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.