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 agent-startup-and-context-ingestiongit 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/agent-startup-and-context-ingestion)<a href="https://agentmods.dev/skills/mvschwarz/openrig/agent-startup-and-context-ingestion"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/agent-startup-and-context-ingestion/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/agent-startup-and-context-ingestion"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/agent-startup-and-context-ingestion.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 | $0.00095 | $0.01453 |
| Opus 5.5 | $0.00038 | $0.00581 |
| Sonnet 5.5 | $0.00019 | $0.00291 |
| Haiku 4.5 | $0.00010 | $0.00145 |
Grade A, and why
agent-startup-and-context-ingestion 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 21d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Startup and Context Ingestion
How an agent becomes useful after launch: AGENTS.md overlays, role
files, skills, rig specs, workflow specs, startup checklists, refocus
messages, and the current rig context retrieval and profile surface.
It is the current concrete startup path inside the broader context-engineering-and-retrieval primitive. Startup gets a seat into the right initial shape; context engineering is the larger question of how a seat gets the right context for the work it is doing right now.
Most coordination failures are not tool failures; they are context failures. Agents need to know their role, operating mode, coordination convention, boundaries, and current product intent. If startup context is scattered or stale, agents execute the wrong thing very efficiently.
Use this when
- Authoring a new agent's startup files (role / culture / startup-context)
- Refreshing a seat that's been running on stale guidance
- Auditing whether agents got the current operating mode (not just what's in old files)
- Designing the orchestrator → next-agent context-transmission shape
- Building a startup map for OpenRig-building rigs
Don't use this when
- The agent is being created via Agent Starter — the starter's manifest carries startup context
- The work is artifact-backed mental-model rebuild from a packet — that's
session-compaction-and-restore - The intent is to ship reusable startup content as a skill
Failure modes (4)
- A new agent starts from an old rig spec and misses the current operating mode. Specs go stale; current state must be visible at startup, not just historical config.
- Guidance is written to a file that future agents read, but current agents are never told. File edits don't propagate to running sessions. Cultural rollout (broadcast + fleet-changes-feed) is needed alongside file edits.
- A startup file becomes a dumping ground and loses the map-to-canonical-sources role. Startup should point AT canonical sources; it shouldn't TRY to be one.
- The orchestrator transmits implementation instructions without preserving product intent. Instructions decay; intent travels.
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.
- 21d ago Changed · -7 lines 347f28a9567b
- 1mo ago Changed · +1 lines ed9a3fba75d9
- 1mo ago Changed · +15 lines 724a92274a2d
- 1mo ago Changed eb1011d4ac5f
- 1mo ago First seen · 113 lines · 95 tokens per session scan A 36b6d8f56a91
agent-startup-and-context-ingestion is a skill published in the GitHub repository mvschwarz/openrig (8,192 stars, last pushed today), licensed Apache-2.0. It adds 95 tokens to every session and 1,453 once invoked, about $0.0004 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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