context-builder

context-builder is a skill for Claude Code, Codex from mvschwarz/openrig. It costs 38 tokens per session (702 once invoked), scanned A, original, Apache-2.0.

A context-gathering skill for turning meetings, competitor information, regulatory material, and internal discussions into feature background. It separates raw reference material from shared summaries and feature-specific notes.

In plain words
What is it for?
Use it to research a feature, gather relevant sources, produce a background.md summary, and maintain shared context documents for later product or engineering work.
Why use it?
It gives a product or development team a consistent place to collect the reasons and evidence behind a feature. It also checks existing validation notes and updates shared context when new findings are useful elsewhere.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to research a feature, gather relevant sources, produce a background.md summary, and maintain shared context documents for later product or engineering work.

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Install with agentmods
npx agentmods add skills/mvschwarz/openrig/context-builder
About the project

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.

mvschwarz/openrig · 7,545 stars · on GitHub · openrig.dev

Install

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.

Any agent
npx skills add mvschwarz/openrig --skill context-builder
Clone the repo
git clone --depth 1 https://github.com/mvschwarz/openrig

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for context-builder

README.md
[![agentmods](https://agentmods.dev/badge/skills/mvschwarz/openrig/context-builder/github.svg)](https://agentmods.dev/skills/mvschwarz/openrig/context-builder)
Your own site
<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.

agentmods 80×15 button for context-builder

Your own site · 80×15
<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>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 702 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash 0134c9b0a9f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

packages/daemon/specs/agents/shared/skills/pm/context-builder/SKILL.md · 88 lines

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

  1. background.md — Feature-specific context summary. Goes in the feature folder. References shared context sources.
  2. 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.]

Read the full file on GitHub · 88 lines

Changes

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.

  1. 1mo ago First seen · 88 lines · 38 tokens per session scan A 0134c9b0a9f1

Subscribe to this mod's changes

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.