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 agent-startersgit 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/agent-starters)<a href="https://agentmods.dev/skills/mvschwarz/openrig/agent-starters"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/agent-starters/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/agent-starters"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/agent-starters.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.01471 |
| Opus 5.5 | $0.00034 | $0.00588 |
| Sonnet 5.5 | $0.00017 | $0.00294 |
| Haiku 4.5 | $0.00008 | $0.00147 |
Grade A, and why
agent-starters 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 14d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Starters
A named, reusable per-seat starting point. Composes an agent role, startup context, optional native session source, and provenance into something a user or rig can choose when creating, refreshing, or packaging a seat.
This registry-backed startup context is distinct from the rig agent-image
surface and from a VM image. See the current behavior below before treating
optional session provenance as an executable native session source.
Use this when
- Creating a new seat from a known-good starting context.
- Refreshing a seat with
rig expandandstarter_ref. - Authoring a new starter (registry entry).
- Inspecting an existing starter's provenance, freshness, or recommended status.
- Promoting / deprecating starters in the registry.
- Composing a starter with a Composable Priming Pack (record manifest id/version, runtime, source session id or transcript path, ready-check evidence, freshness state).
Don't use this when
- You want VM-style deterministic state capture. Starters don't capture VM state.
- You want to copy provider auth material into a starter. Starters refer to session sources and context; they do NOT copy credentials.
What makes a starter valuable
A starter is valuable when it is functional — it carries the context the seat needs to do its task well. Functional is the only measure of a good starter. Size is not: a smaller starter is not a better one, and a bigger one is not worse. Whatever it took for the seat to become genuinely capable at its job is the right starter — 80K tokens or 800K.
Capture the seat as it naturally is at a functional, proven point. Don't pad it with context the seat doesn't use, and — just as important — don't strip context out to make it smaller. Size is an outcome of what the seat needed, never a target.
Never compact, summarize, or shrink a seat in order to make or "lean" a starter. There is nothing valuable in "smaller," and compaction is lossy — you would trade away the exact capability the starter exists to preserve. (Compaction is a separate last-resort step for a seat genuinely near its context limit, with its own before/after plan — never part of capturing a starter.)
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.
- 14d ago Changed · -6 lines 8e8961f44fcc
- 1mo ago Changed · +2 lines 66cbaa251b05
- 1mo ago First seen · 133 lines · 84 tokens per session scan A dd11173ea046
agent-starters is a skill published in the GitHub repository mvschwarz/openrig (7,806 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 1,471 once invoked, about $0.0003 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.
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