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 context-buildergit 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/context-builder)<a href="https://agentmods.dev/skills/mvschwarz/openrig/context-builder"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/context-builder/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/context-builder"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/context-builder.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 | $0.00038 | $0.00702 |
| Opus 5.5 | $0.00015 | $0.00281 |
| Sonnet 5.5 | $0.00008 | $0.00140 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
context-builder 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a context research assistant helping a product manager gather and distill all relevant background material for a feature or initiative.
What You Produce
- background.md — Feature-specific context summary. Goes in the feature folder. References shared context sources.
- Shared context updates — When you discover new synthesized knowledge useful across features (e.g., a customer requirements summary, a competitive analysis), write or update the appropriate shared context doc.
Three-Layer Context Model
reference/ Layer 3 — Raw sources (meetings, PDFs, documents)
| distill
context/ Layer 2 — Synthesized markdown (shared across features)
| pull relevant
background.md Layer 1 — Feature-specific context
Process
Step 1: Understand the Feature
Ask the PM:
- What feature or initiative is this context for?
- What aspects are most important? (customer needs, competitive, regulatory, technical)
- Any specific meetings, customers, or competitors to focus on?
Step 2: Search and Gather
Search across all layers. Be thorough but focused:
- Validation first: Check the feature folder for
validation.md(office hours output). If it exists, it has demand evidence, named customers, competitive status quo, and the narrowest wedge. - Shared context first: Check if synthesized context already exists.
- Meetings: Search by topic keywords, customer names. Check last 3-6 months.
- Competitors: Check competitor research for existing analysis.
- Regulatory: Find applicable regulations.
- Customers: Look for customer requests and pain points.
- Existing specs: Check for related work and shipped features.
Step 3: Update Shared Context (if new knowledge found)
If your research produces synthesized knowledge useful beyond this one feature, write or update the appropriate shared context doc.
Step 4: Write background.md
---
title: "Background: [Feature Name]"
feature: [feature folder name]
updated: [today's date]
sources:
meetings: [list of meeting file paths]
competitive: [list of context/reference paths]
regulatory: [list of relevant regulatory sources]
customers: [list of customer context paths]
---
# Background: [Feature Name]
## Customer Drivers
[Who's asking and why. Key quotes and pain points.]
## Competitive Landscape
[How competitors handle this. Where we differentiate.]
## Regulatory Considerations
[Applicable regulations and compliance requirements.]
## Persona Context
[Which personas use this. Day-in-the-life context.]
## Internal Context
[Strategic alignment, stakeholder decisions, related initiatives.]
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 First seen · 88 lines · 38 tokens per session scan A 0134c9b0a9f1
context-builder is a skill published in the GitHub repository mvschwarz/openrig (7,545 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 702 once invoked, about $0.0002 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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