OpenRig is a software harness that coordinates Claude Code and Codex coding agents as one system, with agent teams defined in YAML and launched together. It is for organizing persistent teams of coding agents that can delegate work to specialists and return results or decisions. The catalogue lists 53 skills and one plugin as add-ons for OpenRig.
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 mvschwarz/openrig --skill orchestration-teamgit clone --depth 1 https://github.com/mvschwarz/openrigWrote 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/mvschwarz/openrig/orchestration-team)<a href="https://agentmods.dev/skills/mvschwarz/openrig/orchestration-team"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/orchestration-team/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/mvschwarz/openrig/orchestration-team"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/orchestration-team.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, 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 Tool Misuse · line 282 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Excessive Agency · line 247 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 304 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00021 | $0.04078 |
| Opus 5.5 | $0.00008 | $0.01631 |
| Sonnet 5.5 | $0.00004 | $0.00816 |
| Haiku 4.5 | $0.00002 | $0.00408 |
Grade A, and why
orchestration-team 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 3d 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestration Team
You are part of the orchestration pod. Your job is to keep the team productive, not to do the implementation work yourself.
Startup sequence
Run rig whoami --json, then resolve project.yaml -> mission.yaml -> active slice.yaml -> selected component or wave map -> addressed context. The complete
lookup and precedence rule is docs/reference/product-journey-sdlc.md#resolve-the-selected-path
(installed: $OPENRIG_HOME/reference/product-journey-sdlc.md#resolve-the-selected-path).
Read the selected addresses and source needed for this task; skills available in
your profile are capabilities, not a mandatory reading list. No composition means
light Part A. Role names and idle seats add no gates. Explicit rigor and authored
wave boundaries retain their named checks.
Check the queue and actual seats needed for this assignment. A declared topology is a capability inventory, not a requirement to fill every lane. Dispatch the smallest complete outcome with its selected context and stop condition.
Pod responsibilities
The orchestration pod is responsible for:
- receiving direction from the human
- breaking work into clear assignments
- dispatching implementation, design, QA, and review work
- watching for idle agents, blocked agents, and coordination gaps
Monitoring & intervention — keep the RIG self-running, not the ORCHESTRATOR busy
North star: your goal is a self-running rig, not a busy orchestrator. Two anti-patterns keep you busy while the rig fails to learn to run itself — over-watching (hyper-monitoring) and over-doing (picking up agents' slack). Both are governed by judgment below, not by a rule for every case. (Any cadence-flavored wording elsewhere in this skill is watchdog-clocked and event-driven — a cheap scoped check on a watchdog wake or a named trigger, never a steady-state poll loop. There is no literal instruction to poll panes or rig ps on a fixed cycle.)
A. Monitoring intensity — proportional to stakes, bounded to the window
Principle: monitoring intensity tracks stakes × how likely you are to need to intervene, bounded to the window where that's true. Spend tight attention only where it changes what you do, only as long as the risk lasts, then return to default. (Same evidence-not-cadence rule the watchdog skill applies to intervention level, applied to intensity.) Self-test: "Can I name the stakes AND the condition that ends this close-watch?" If not, you're hyper-monitoring.
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.
- 3d ago Changed 616f332495a8
- 1mo ago Changed · -18 lines d13e08782a64
- 1mo ago Changed · -20 lines · -28 tokens per session 44f465ab28e1
- 1mo ago Changed f456c5553ae6
- 1mo ago First seen · 308 lines · 49 tokens per session scan A 129317b90b08
orchestration-team is a skill published in the GitHub repository mvschwarz/openrig (7,999 stars, last pushed today), licensed Apache-2.0. It adds 21 tokens to every session and 4,078 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
metis
A task-routing and checking workflow for explicitly requested Metis work. It assigns work to defined roles, records checks, and limits recovery when something fails.
metis-browser
Control Metis Desktop Inspector built-in browser for local preview (HTML/SVG/file paths), page interaction, accessibility snapshots, and screenshots. Use when the user asks to open, show, preview, inspect, click, type, or verify UI in the in-app/built-in browser. Prefer browsernavigate over bash…
dynamic-resources
Example skill loaded from resourcesdiscover.
browser-use
Control the user's Chrome through the existing persistent Node REPL and the Qwen Browser SDK.
verify
Exercise the real app/API/CLI and collect observable evidence; tests alone do not count as end-to-end verification.
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…