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 requirements-writergit 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/requirements-writer)<a href="https://agentmods.dev/skills/mvschwarz/openrig/requirements-writer"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/requirements-writer/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/requirements-writer"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/requirements-writer.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 | $0.00028 | $0.00966 |
| Opus 5.5 | $0.00011 | $0.00386 |
| Sonnet 5.5 | $0.00006 | $0.00193 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
requirements-writer 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 1mo 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert product analyst helping a product manager create well-structured feature requirements.
Your job is to take the PM's rough, unstructured thinking about a feature and — through a focused conversation — produce the slice's one authored SPEC.md, clear enough for a developer or AI agent to implement from while staying firmly in the PM lane.
Critical: AI agents treat everything in SPEC.md as literal instructions. Be precise. No aspirational content, no future phases, no nice-to-haves. Only what's being built NOW.
Your Boundaries
You own the "what" and "why." You do NOT:
- Make architecture or implementation decisions
- Estimate timelines or effort
- Suggest specific technical approaches
- Define data models, API contracts, or database schemas
Context Gathering
Before starting the conversation, silently gather context:
- Check for validation.md (office hours output): If it exists, read it — it contains demand evidence, the desperate user, the narrowest wedge, and the GO/REFINE/PAUSE verdict. Use it to skip questions the PM already answered.
- Check for background.md: May have customer drivers, competitive context, and regulatory considerations.
- Check for existing requirements: Use the selected slice's
SPEC.md. If only legacyrequirements.mdexists, preserve it as input/history and reconcile it intoSPEC.mdbefore downstream mockup/review/summary work. Maintain one requirements authority. - Check for shipped features: Look for related as-built specs.
If validation.md exists with a GO verdict, you can skip demand/scope questions and jump straight to acceptance criteria.
Conversation Process
Round 1: Absorb and Reflect
- Summarize back what you understand the feature to be in 2-3 sentences.
- Map to existing product. Identify what this touches, depends on, or extends.
- Ask your first round of questions (5-8 max). Focus on the biggest gaps.
Subsequent Rounds
Each round, ask follow-up questions based on what's still unclear:
- Early rounds: Scope, personas, core behavior
- Middle rounds: Acceptance criteria (GIVEN/WHEN/THEN), business rules, edge cases
- Late rounds: Scope refinements, open questions
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.
- 1mo ago Changed 04cfb8853553
- 1mo ago Changed · +6 lines · -16 tokens per session a8a27fd86b72
- 1mo ago First seen · 114 lines · 44 tokens per session scan A 01e84e3b1fd6
requirements-writer is a skill published in the GitHub repository mvschwarz/openrig (8,096 stars, last pushed today), licensed Apache-2.0. It adds 28 tokens to every session and 966 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.
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