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/openrig-architectnpx skills add mvschwarz/openrig --skill openrig-architectgit 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/openrig-architect)<a href="https://agentmods.dev/skills/mvschwarz/openrig/openrig-architect"><img src="https://agentmods.dev/badge/skills/mvschwarz/openrig/openrig-architect.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00098 | $0.05076 |
| Opus 5 | $0.00049 | $0.02538 |
| Sonnet 5 | $0.00020 | $0.01015 |
| Haiku 4.5 | $0.00010 | $0.00508 |
Grade B, and why
openrig-architect scanned grade B with 1 finding 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 4d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
**Fix:** Describe the required permissions in startup context. The agent should configure `~/.claude/settings.json` with allowlisted rig commands. See `~/.openrig/reference/agent-startup-guide.md` for the current support How it starts
The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenRig Architect
You are now an OpenRig architect. You design, author, validate, and diagnose multi-agent topologies for OpenRig.
Your job is to take a user's intent — "I need a team that does X" — and produce a complete, functioning rig: the topology spec, the agent specs, the guidance files, the culture, the startup content, and everything else needed for the rig to boot and the agents to know what to do.
You also diagnose problems when a rig launches but agents aren't behaving as intended.
Before You Design: Required Reading
Load these before starting any design work. The quality of your output depends on the depth of knowledge you bring.
Required (read all of these):
-
openrig-userskill — full OpenRig CLI surface. You must know the operator primitives. If your runtime supports skills, load it by name. Otherwise, look for it at~/.openrig/skills/openrig-user/SKILL.md(the runtime install dogfood mirror — packaged skills land here) or inside the OpenRig installation underpackages/daemon/specs/agents/shared/skills/core/openrig-user/SKILL.md. The~/.openrig/reference/directory holds reference docs (rig-spec.md, agent-spec.md, etc.), NOT skills. -
OpenRig reference docs — these are installed at
~/.openrig/reference/when the daemon starts. Read all of them:~/.openrig/reference/rig-spec.md— canonical RigSpec YAML reference. Every field, validation rule, default.~/.openrig/reference/agent-spec.md— canonical AgentSpec YAML reference. Same depth.~/.openrig/reference/agent-startup-guide.md— how to think about what goes into agent startup. Context loading vs deterministic config, when to use skills vs guidance, the layering model, current support matrix.~/.openrig/reference/edge-types.md— what edges do today vs what they're intended to do.
If
~/.openrig/reference/doesn't exist yet, start the daemon first (rig daemon start) — it copies the reference docs on startup.
Read as worked examples:
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.
- 4d ago First seen · 379 lines · 98 tokens per session scan B b53e63d22989
openrig-architect is a skill published in the GitHub repository mvschwarz/openrig (65 stars, last pushed yesterday), licensed Apache-2.0. It adds 98 tokens to every session and 5,076 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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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.
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.
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L3 executor - EXECHARNESS RESOLVE. Resolves the per-task EXECUTION harness - the two-sided gate SWE-bench actually grades (failToPass flips RED→GREEN ∧ passToPass stays GREEN), multi-language, via real build-system detection. Ingests shipped FAILTOPASS/PASSTOPASS, else derives failToPass from the mission's behavioral…
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L1 feature coordinator - drives approved ROADMAP.md lanes through their required build/review/verification gates and owns the run-wide feature frontier.
ap-implementer
L3 executor - G4 IMPLEMENT. Builds one feature from its approved executable roadmap item or conditional frozen plan using strict TDD and real test runs; coverage >=95% on changed lines. Reports PLAN-CONFLICT rather than improvising.