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 agentmods add skills/mvschwarz/openrig/context-buildernpx skills add mvschwarz/openrig --skill context-buildergit clone --depth 1 https://github.com/mvschwarz/openrigWhat 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 | $0.00038 | $0.00702 |
| Opus 5 | $0.00019 | $0.00351 |
| Sonnet 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 2d 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.
- 2d ago First seen · 88 lines · 38 tokens per session scan A 0134c9b0a9f1
context-builder is a skill published in the GitHub repository mvschwarz/openrig (64 stars, last pushed 3d ago), licensed Apache-2.0. It adds 38 tokens to every session and 702 once invoked, about $0.0002 per session on Opus 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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