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 exec-summarygit 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/exec-summary)<a href="https://agentmods.dev/skills/mvschwarz/openrig/exec-summary"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/exec-summary/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/exec-summary"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/exec-summary.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 | $0.00029 | $0.00664 |
| Opus 5.5 | $0.00012 | $0.00266 |
| Sonnet 5.5 | $0.00006 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
exec-summary 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You generate an executive summary for a feature that's ready for development. The summary orients three audiences from one document: sales (pitch), leadership (strategy), and engineering (behavioral overview).
What You Produce
A single executive-summary.md file in the feature folder. 600-800 words excluding mockups and FAQ. Written in present tense as if the feature already exists.
Sources to Read
Before writing, silently gather all context from the feature folder:
- SPEC.md — acceptance criteria, business rules, scope, user stories
- background.md — customer drivers, competitive context, regulatory considerations
- validation.md — demand evidence, status quo, desperate user, narrowest wedge
- supporting/mockup-ascii.md — visual mockups (pull 2-3 key screens)
Output Structure
---
title: "Executive Summary: [Feature Name]"
feature: [feature-folder-name]
status: [ready | in-progress | shipped]
jira: [ticket key]
updated: [today's date]
---
# [Feature Name]
## The Problem
[2-3 sentences. Customer pain in their language. Name real customers/prospects.]
## The Solution
[2-3 sentences. What we built, present tense. No jargon.]
## Who It's For
[Primary personas + named customers/prospects waiting for this.]
## Why Now
[Deals it unblocks, competitive gap it closes, what it enables next.]
## How It Works
[5-8 key capabilities. Describe actual user-facing behavior — enough for a dev to understand what to build.]
## Key Decisions
[5-8 non-obvious business rules and design choices. Focus on surprising or counter-intuitive ones.]
## Visual Preview
[2-3 ASCII mockup screens from supporting/ — the key screens that tell the story.]
## What We're NOT Building
[5-8 key out-of-scope items with brief reason.]
## Dependencies & Sequencing
[What must ship first. Cross-feature dependencies. Prerequisites.]
## Success Metrics
[2-4 measurable outcomes. Mix of usage and business metrics.]
## FAQ
### For Sales
[2-3 questions. When available, what to tell prospects, competitive positioning.]
### For Leadership
[2-3 questions. Opportunity cost, strategic fit, risks.]
### For Engineering
[2-3 questions. Dependencies, known risks, what's deferred, data model implications.]
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 ada0f4233792
- 1mo ago First seen · 85 lines · 29 tokens per session scan A 9c06b089b4de
exec-summary is a skill published in the GitHub repository mvschwarz/openrig (7,545 stars, last pushed yesterday), licensed Apache-2.0. It adds 29 tokens to every session and 664 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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