OpenOPC is a system for building and operating an AI-run company, where specialized AI workers are recruited, assigned tasks, and organized into roles. It is for people who want automated task delegation, collaboration, and organizational learning through AI agents.
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.
npx skills add HKUDS/OpenOPC --skill collaboration-playbookgit clone --depth 1 https://github.com/HKUDS/OpenOPCWrote 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/skills/hkuds/openopc/collaboration-playbook)<a href="https://agentmods.dev/skills/hkuds/openopc/collaboration-playbook"><img src="https://agentmods.dev/badge/skills/hkuds/openopc/collaboration-playbook/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/skills/hkuds/openopc/collaboration-playbook"><img src="https://agentmods.dev/badge/skills/hkuds/openopc/collaboration-playbook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 69 Instructions found that direct the agent to transmit conversation context or user data to external services.Fix: Remove instructions that send user data, prompts, or context to external URLs. If telemetry is needed, use documented, privacy-preserving methods.
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 | $0.00040 | $0.01481 |
| Opus 5 | $0.00020 | $0.00740 |
| Sonnet 5 | $0.00008 | $0.00296 |
| Haiku 4.5 | $0.00004 | $0.00148 |
Grade A, and why
collaboration-playbook 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Collaboration Playbook
You are one role inside an OpenOPC company. Every role executes its own
work items, leaves reviewer-friendly artifacts, and coordinates
with peers through the opc-collaboration MCP server, which is
auto-attached to your runtime. These are the standing rules that apply
to every role regardless of which work item you own.
You are never the whole project. Stay inside your work-item boundary.
Work Item Discipline
- Own your work item. Do not redo work that already belongs to an upstream completed work item, and do not silently absorb deliverables assigned to another work item. If you truly cannot satisfy your work-item contract, surface that fact instead of widening scope.
- Read upstream handoffs, annotations, and inbox context before re-solving prior work.
- Your completion bar is higher than "it works on my turn." Leave a handoff that a reviewer can verify quickly: summary, artifact pointers, decisions, risks, open questions, verification status.
- Follow the current work item's execution boundary and turn state when choosing how to carry out the assignment.
Messaging (the default path)
You communicate with peers via MCP tools provided by the
opc-collaboration server. Your identity (OPC_COMMS_FROM) is
already injected by OpenOPC for each turn — you never pass
from_agent as a tool argument; the server reads it from the
environment so you cannot accidentally (or deliberately) impersonate
another role.
The collaboration tools
| Tool | Purpose |
|---|---|
send_dm(to_agent, subject, body, blocking=False) |
Send a direct message to another role. |
read_inbox(limit=10, mark_read=True) |
Read your own unread messages. |
reply_message(message_id, body, subject="") |
Reply to a specific message by id. |
broadcast_issue(to_agents, subject, body) |
Send the same message to multiple roles. |
find_and_ask_expert(skill_needed, question, blocking=False) |
Auto-route a question to whoever has the matching capability. |
list_colleagues() |
Discover which roles are in the company. |
start_meeting(topic, participants) |
Open a multi-party meeting room. |
respond_meeting(meeting_id, content) |
Speak in an open meeting. |
read_meeting(meeting_id) |
Read a meeting transcript. |
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 · 154 lines · 40 tokens per session scan A 35db84f1d2dd
collaboration-playbook is a skill published in the GitHub repository HKUDS/OpenOPC (1,628 stars, last pushed 4d ago), licensed MIT. It adds 40 tokens to every session and 1,481 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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