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 forming-an-openrig-mental-modelgit 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/forming-an-openrig-mental-model)<a href="https://agentmods.dev/skills/mvschwarz/openrig/forming-an-openrig-mental-model"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/forming-an-openrig-mental-model/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/forming-an-openrig-mental-model"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/forming-an-openrig-mental-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 177 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 238 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00117 | $0.03810 |
| Opus 5 | $0.00059 | $0.01905 |
| Sonnet 5 | $0.00023 | $0.00762 |
| Haiku 4.5 | $0.00012 | $0.00381 |
Grade A, and why
forming-an-openrig-mental-model 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 today.
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 — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forming an OpenRig Mental Model
You're new to OpenRig — or returning after time away — and you need to quickly understand what kind of system this is, what your seat is, and what the moves are. This skill is the fast on-ramp.
For depth, read the canonical reference docs the skill points to. This skill's job is to get you oriented — accurate enough to operate, fast enough to be useful — not to replace the canonical docs.
The 60-second mental model
OpenRig is a local control plane for multi-agent coding topologies. You declare a topology of agents in YAML, boot it with one command, and OpenRig manages tmux sessions, harness lifecycles, transcripts, snapshots, and restoration. When the system goes down, OpenRig snapshots; when it comes back, agents resume their conversations.
The product loop:
down (auto-snapshot) → up <rig-name> (auto-restore) → work → repeat
The unit of work is the rig — a topology of agents working together as a single system.
The four-layer model (where you live)
Everything in agent engineering happens at one of four layers. OpenRig operates at Layer 3.
| Layer | Name | Analogy | What it is |
|---|---|---|---|
| L0 | Model | CPU | Foundation model — Claude, GPT, Gemini. Stateless tokens-in/tokens-out. |
| L1 | Agent Core | Process loop | The reason-and-act cycle: observe, plan, choose, act, repeat. |
| L2 | Harness | Container / OS | Tools, memory, lifecycle around the model. Examples: Claude Code, Codex CLI. |
| L3 | Rig | Docker Compose / Terraform | Multi-agent topology — what agents exist, how they relate. OpenRig. |
You are an agent at L1 inside an L2 harness, configured by L3 OpenRig. OpenRig manages your harness; the harness wraps the model; the model generates your tokens.
Three pillars of context
OpenRig is built on three context-engineering pillars. When you're oriented, you should know which pillar you're operating in:
| Pillar | What it is | Where it lives |
|---|---|---|
| Ontology | What exists. Curated knowledge — facts, code maps, as-built docs. | Shipped public context packs plus project-authored docs; discover with rig context list. |
| Epistemology | Why an agent believes what it believes — reasoning, instincts, decisions. | Transcripts (auto-captured). Session logs. ADRs. |
| Topology | How agents are connected — pods, edges, communication paths. | OpenRig itself. RigSpec YAML. |
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.
- today Changed · +1 lines cff1ca1640fe
- 4d ago Changed 04353dbcf299
- 10d ago First seen · 319 lines · 117 tokens per session scan A 9199fc9c5ff5
forming-an-openrig-mental-model is a skill published in the GitHub repository mvschwarz/openrig (66 stars, last pushed yesterday), licensed Apache-2.0. It adds 117 tokens to every session and 3,810 once invoked, about $0.0006 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.
Other skills, from other repositories
dynamic-resources
Example skill loaded from resourcesdiscover.
interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
verify
Exercise the real app/API/CLI and collect observable evidence; tests alone do not count as end-to-end verification.
autoprompt
Explicit-only useful-first orchestration. Invoke /autoprompt to turn a mission into one executable roadmap, build dependency-safe lanes, and verify the result with independent reviewers. Never infer invocation from ordinary requests. Never resume from leftover artifacts without an explicit resume instruction.
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…
bundle-safety
Bundle transform safety — minification variant selection, consumer-constraint verification, identifier preservation, and namespace re-export coverage for build output.