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-engineeringgit 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-engineering)<a href="https://agentmods.dev/skills/mvschwarz/openrig/context-engineering"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/context-engineering/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-engineering"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/context-engineering.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 | $0.00142 | $0.07358 |
| Opus 5.5 | $0.00057 | $0.02943 |
| Sonnet 5.5 | $0.00028 | $0.01472 |
| Haiku 4.5 | $0.00014 | $0.00736 |
Grade A, and why
context-engineering 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 — 480 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Traditional Context Engineering — 2024–2025 Snapshot
Status: provisional historical research snapshot
This skill is a provisional historical research snapshot of context-engineering practice as published in 2024–2025. It is NOT normative. Do not apply it prescriptively to current frontier models without re-verification. On any conflict, OpenRig current skills, explicit user rulings, and directly measured OpenRig practice PREVAIL over this document. Treat these areas as particularly suspect pending re-verification: compaction/summarization guidance, minimal-upfront versus broad orientation, just-in-time lookup assumptions, fixed context ceilings, and single-agent versus multi-agent advice.
Curated distillation of the best publicly available expertise on context engineering for coding agents, drawn from primary sources at Anthropic, OpenAI, and leading practitioners (Manus, Cognition, Chroma, LangChain, Drew Breunig, and others). Load on the moments the description names; it is deliberately not part of any base walk.
1. The mental model: what context engineering is
Definition. Context engineering is "the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference" — everything that lands in the window: system instructions, tool definitions, retrieved data, message history, and tool outputs, not just the prompt text (Anthropic, Effective context engineering for AI agents). Andrej Karpathy's framing, popularized via LangChain: "the delicate art and science of filling the context window with just the right information for the next step" — the LLM is a CPU and the context window is its RAM, and your job is deciding what gets loaded into RAM at each step (LangChain, Context Engineering for Agents).
Why it superseded prompt engineering. A chatbot answers one question with whatever fits in one turn. An agent runs in a loop, accumulating tool results, file contents, and history across dozens or hundreds of steps. The improvements stop coming from rewording instructions and start coming from rewiring — what the agent retrieves, in what order, and what gets evicted when the window fills (Anthropic, ibid.). Philipp Schmid's formulation of the practical consequence: "Agent failures aren't only model failures; they are context failures." Most of the time when a capable model does something dumb, the context it was given made the dumb thing likely (Schmid, The New Skill in AI is Context Engineering).
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 · 480 lines · 142 tokens per session scan A ef0522380604
context-engineering is a skill published in the GitHub repository mvschwarz/openrig (7,545 stars, last pushed yesterday), licensed Apache-2.0. It adds 142 tokens to every session and 7,358 once invoked, about $0.0006 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.
Other skills, from other repositories
handoff
Write a compact, decision-ready handoff so the next session (or the user) can continue without reconstructing the current one. Use when the session is ending, context is running low, the user asks for a handoff / 'pass the baton' / 'hand off', or a long-running operation needs a durable state checkpoint.
forget
Help the user remove a stored fact using Codewhale's Context Lens when they ask to forget or delete a memory.
semantix
Install and use the semantix memory kernel as a middleware in your agent: extract user preferences / workflows / experience from past sessions, retrieve and inject them on demand. One binary + your agent's own tools.
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…
metis
A task-routing and checking workflow for explicitly requested Metis work. It assigns work to defined roles, records checks, and limits recovery when something fails.
continuous-learning
Use when a mistake, correction, or surprise taught the workspace something that must stick — a retro or postmortem, the same agent error corrected twice, a resolved bug's root cause, scattered notes-to-self — and route that lesson to the durable surface that fires next time. NOT a forward choice with alternatives…