Agent skill

Openrig Operating Model

by mvschwarz in mvschwarz/openrig

A skill your agent uses when you do not know WHERE something belongs: you learned a lesson and are unsure which file takes it; you are about to create a new doc, folder, or convention; you are…

Apache-2.0Auto-check passedAgent Workflows

Install Openrig Operating Model

skills CLI
$ npx skills add mvschwarz/openrig --skill openrig-operating-model -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mvschwarz/openrig openrig-operating-model --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mvschwarz/openrig.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/daemon/assets/plugins/openrig-core/skills/openrig-operating-model .claude/skills/openrig-operating-model && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
openrig-operating-model
GitHub stars
5.9k
Token cost
~6.2k tokens
SKILL.md length
3,651 words
Files
11 (incl. scripts)
Skills in repo
49
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when you do not know WHERE something belongs: you learned a lesson and are unsure which file takes it; you are about to create a new doc, folder, or convention; you are…

  • Works in 10 steps: The model in one breath → THE GRID — what exists at each level,… → Why this exists (the failure it fixes) → …
  • You do not know WHERE something belongs: you learned a lesson and are unsure which file takes it
  • SKILL.md covers 1. The model in one breath, 2. THE GRID — what exists at…, 3. Why this exists (the… and 4. LEARNED.md — the living…, plus 8 more sections
  • Runs Shell and Python scripts from its folder

What it does

Openrig Operating Model is an agent skill from mvschwarz/openrig. Use when you do not know WHERE something belongs: you learned a lesson and are unsure which file takes it; you are about to create a new doc, folder, or convention; you are asking "should this be a skill, a note, or a chain file?"; you inherited a seat and want to know what SHOULD exist; or you are about to duplicate knowledge that already has a home. Also the trace-to-root: how a seat orients by reading one filename at each level from where it stands up to the fleet.

Its SKILL.md is about 6.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts (for example `scripts/compose.py`, `scripts/scaffold.sh` and `scripts/trace-due.sh`).

It sits in Agent Workflows. The repository describes itself as: Build your own network of agents from Claude Code, Codex and Pi: persistent teams with roles, shared context and owned work. The licence is Apache-2.0.

When your agent uses it

  • You do not know WHERE something belongs: you learned a lesson and are unsure which file takes it
  • You are about to create a new doc
  • You are asking should this be a skill
  • You inherited a seat and want to know what SHOULD exist

Example prompts

  • “should this be a skill, a note, or a chain file?”
  • “/openrig-operating-model”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. The model in one breath
  2. THE GRID — what exists at each level, and who keeps it true
  3. Why this exists (the failure it fixes)
  4. LEARNED.md — the living file (all altitudes; seat shown)
  5. The trace — deliberate reorientation
  6. Writing — two principles
  7. Composed views
  8. Where knowledge goes — the placement rule
  9. Standing the structure up
  10. Pitfalls (only what isn't taught above)

What it can do on your machine

Read from SKILL.md and the folder at commit 1f69831. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 5 files in scripts/ (Shell and Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Openrig Operating Model loads about 6.2k tokens when it runs. Until then it costs about 124 tokens; SKILL.md has 3,651 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
When it runs · the whole SKILL.md, loaded when a task matches
~6.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mvschwarz/openrig at commit 1f69831, republished under its Apache-2.0 licence (© mvschwarz). 3,651 words, ~6,187 tokens.

Download SKILL.mdSave it as .claude/skills/openrig-operating-model/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
openrig-operating-model
description
Use when you do not know WHERE something belongs: you learned a lesson and are unsure which file takes it; you are about to create a new doc, folder, or convention; you are asking "should this be a skill, a note, or a chain file?"; you inherited a seat and want to know what SHOULD exist; or you are about to duplicate knowledge that already has a home. Also the trace-to-root: how a seat orients by reading one filename at each level from where it stands up to the fleet.

The OpenRig Operating Model

Canonical term: the Operating Model. How an OpenRig topology organizes every kind of context so that any agent — cold, fresh, or five generations in — can find what it needs and knows where to write what it learns. Authored context stays in addressed Markdown and manifests. Supported writers record semantic judgments; their read models remain rebuildable from those artifacts.

This skill owns the structure: trees, chains, tracing, and placement of authored knowledge. Resolve installed context through its current library addresses and the project's selected loadout; a private corpus is not a required public dependency.

Companion implementation assets in this skill's folder:

  • templates/ and scripts/ — starters and working tools, described in §8.

1. The model in one breath

Everything lives on one of two trees, and every kind of context is a chain: the same filename at every level of a tree. To know anything, you trace from where you are toward the root, reading that one filename at each level — nearer levels refine or override farther ones. The trace (§5) is the act of doing that ascent deliberately, with your own file reads.

  • Topology tree — how work is done: fleet → instance → rig → pod → seat.
  • Work tree — how the product gets built: project → mission → slice → proof item.

An instance is one OpenRig daemon and the rigs it manages; a fleet is all instances. Where a daemon runs is a deployment fact, not an altitude — say the instance's name when you mean a particular one.

The one-parent law: the trace follows the directory path and NOTHING else — no pointer fields, no link-following, no branching, ever. The trace is the instrument confused agents reach for, so it must be simpler than anything it corrects: path-only ascent fails only in obvious ways. A child that relates to a second parent gets an also-serves: ANNOTATION (read by humans and agents, walked by nothing) — and a genuine two-parent tie usually means the item belongs one level up, under the common ancestor, with mentions pointing down. The operational many-to-many lives in the queue's tags, which are maintained by use. Legacy serves: lines are transition bridges for the flat layout only: never required, never walked, deleted as work nests properly.

The filename law: one name per chain, identical at every level. The folder tells you whose it is; the filename tells you what kind it is. A seat named pm-lead in two different rigs has two different LEARNED.md files — the path is the identity. Never invent per-level names (no SEAT.md, no RIG.md): that breaks the trace, the self-description, and every tool at once.

2. THE GRID — what exists at each level, and who keeps it true

This table is the model. If you internalize one thing, internalize this.

Topology tree

The chains carry what is true of a position. How a kind of thing works — the operating model — ships in the mode-neutral openrig-core plugin; an operating-mode plugin (openrig-lab, openrig-factory, or openrig-hq) may refine it.

LevelCULTURE.md — valuesLEARNED.md — what THIS ONE has learnedkept true by
fleetdefault culture (ships with OpenRig)fleet-level lived practiceoperator agent
instance(inherits)what is true of every rig on this daemonthe instance's operator
rigthe rig's constitutionthis rig's lived practicethe rig's orchestrator
pod(inherits)this pod's lived practice — the context domain: anything useful to anyone in this podthe pod's lead
seat(inherits)this seat's lived knowledge — the file that fixes handoversthe seat itself

Chain files sit on nodes, not on the shelves that hold them. rigs/, pods/, seats/, missions/ and slices/ are shelves — the trace passes through them and expects nothing there.

Work tree

ONE authored node file: SPEC.md. Intent lives in its FRONTMATTER; the specification lives in its body. Alongside it sit three files with different jobs and different writers:

filewhat it iswho writes it
SPEC.mdthe node — intent: composes, body specifiesthe node's owner
NOTES.mdLIVED — what actually happened doing it, in the doer's own wordswhoever is doing it
PROOF.mdevidence the thing does what was intendedthe prover
PROGRESS.mdauthored narrative and historical checklist marks; current proof readiness is derived from attributed judgmentsthe scope's owner and provers
proof/judgments/retained item judgment receipts written by rig proof judgejudges selected by the owning proof policy

A scaffold may create NOTES.md and its starter instructions; its lived entries are never generated or projected. It is the work tree's lived file, the way LEARNED.md is the topology tree's — the field-notes rung, where raw observation goes before anything has earned a place in the node itself. Keeping lived files out of the render path is what makes them safe to write freely in.

Legacy name: older workspaces may use MISSION_NOTES.md. Keep legacy content addressable; use NOTES.md for the chain because its name works at every altitude.

LevelSPEC.md — frontmatter intent: (why) + body (what must be built)progresskept true by
projectintent: only — stable, changes at real pivotsderived roll-upthe project's PM
missionintent: and proportional mission-level specification; organizes slicesderived child readiness; distinct outcome judgmentthe mission's PM
sliceintent: and the concrete slice specificationderived proof readinessthe slice's owner
proof itemauthored promise in the proof contractattributed evidence-backed judgment on that revisionthe selected judge

Current acceptance: Scope relationships and policy are authored in manifests; an item judgment is recorded once against its promise, evidence and policy revision. Slice, mission and project readiness derive upward. A distinct outcome or publication decision remains its own authority. Queue ownership and generic done are custody facts, not proof acceptance. Historical checkbox marks stay readable without acquiring current item authority.

Use rig proof --help for the supported write/read path. The project owner selects proofPolicy.judges in project.yaml, mission.yaml or slice.yaml (nearest wins), using exact actor addresses. There is no implicit fixed-role review conveyor. rig proof judge mission/slices/slice#1 --verdict accept --reason 'Observed the outcome' --evidence proof/result.md resolves the item identity, revision, evidence digests and retry identity. Evidence may be an existing legacy artifact or a non-code outcome. A patch-equivalent subject also names its actual comparison/adoption receipt with --comparison; the agent owns that judgment. rig proof show returns the current basis and retained receipt; it does not publish anything.

Correct with reject, or withdraw when prior acceptance no longer stands. History stays in the addressed proof home. A repeated request returns its original receipt and current readiness; --replace deliberately reaffirms an old identical judgment after a correction. Changed promises, policy or evidence require a current judgment. Explicit optional <!-- proof-item: stable-id --> markers preserve identity across wording edits; the product otherwise derives identity from the full promise and reports ambiguity.

Intent composes; the body does not. The trace reads the intent: FIELD at every altitude, so four levels unfurl as four sentences rather than four documents. The body is read only when you are standing on that node.

compose.py up <node> --name SPEC.md --field intent --root <project>

A level whose file exists but lacks intent: is reported as a gap — a mission with no intent is real information, never silently skipped.

Legacy README.md nodes stay valid indefinitely — the resolver prefers SPEC.md and falls back, so nothing is forced to migrate and dormant missions need no attention.

PROGRESS stays separate. Narrative and legacy marks are retained testimony. For a project with selected proof policy, current readiness comes from rig proof show and the shared existing views, not hand-maintained parent checkboxes or copied status prose. scripts/compose.py progress remains a legacy checklist renderer; it does not certify attributed acceptance. Intent composes downward; proof readiness aggregates upward.

The axis behind the columns: every context kind has a template half (what ships — SOP, the default culture) and a learned half (what living in it taught — LEARNED, culture amendments). Only the pace of change differs: values change rarely and deliberately, like a constitution; practice changes constantly and cheaply, like working notes; intent changes at real pivots. Keep each file's pace; don't constitutionalize your notes or scribble on the constitution.

3. Why this exists (the failure it fixes)

Seat knowledge needs a durable home that successors and other runtimes can read. A private runtime memory or an urgent handover packet alone can omit standing duties and the reasons behind a practice. Keep general operating craft in the shared skill and position-specific knowledge in the seat's LEARNED.md chain. At a handover, read the current chain alongside the packet and record any missing job context before claiming readiness. Each occupant maintains what the seat has learned; the handover packet carries the current transition.

4. LEARNED.md — the living file (all altitudes; seat shown)

Sections, in order — see templates/LEARNED.md:

  1. Header — coords · which SOP it refines · updated date (from the clock command, never from memory).
  2. MY JOB HERE — this instance's actual function, in plain operational terms.
  3. STANDING DUTIES — every recurring duty, each with its rhythm and where it happens. Duties listed anywhere else die at the next handover; this section is why they survive.
  4. HOW I WORK — practices learned on the job, each with its reason. A practice with its why can be re-judged when the world changes; a bare rule outlives its reason and gets misapplied.
  5. GATES & AUTHORITIES — what this instance may decide alone, what it must never assume. Authority exists in writing or not at all.
  6. KEY RELATIONSHIPS — who it hands to, who reviews it, who it reports to.
  7. TRIGGER POINTERS — "when X happens, read Y." Attach pointers to the moments that need them; lists of boot-time reading decay.
  8. LESSONS — dated, newest first. Periodically distill old entries into HOW I WORK or drop them.

Size: soft guidance, not a rule. Keep it as small as honestly covers the job — attention is the budget, and every reader pays it. Some seats genuinely need more; write what the job needs. The failure mode to watch for is the rulebook that only ever grows. Distill lessons into practices with reasons at deposit boundaries (before clear or handover); use that habit, not a line count.

5. The trace — deliberate reorientation

A trace is walking your chains with your own file reads and writing down where you stand: what am I doing (queue/NOTES) → under what contract (SPEC) → toward what intent (SPEC.md intent field, leaf to root) → by what practice (LEARNED + SOP) → within what values (CULTURE). A few written lines at the end. scripts/compose.py up assembles any chain for you.

Two rules give the trace its value:

  • Your reads are the trace. A trace written from memory is a recitation — if you didn't open the files, you didn't trace, and you will confidently re-derive whatever drift you already have.
  • Report broken links; never obey them. A missing file, a stale date, an intent that contradicts observable reality — say so to the level that owns it. That is how the chains stay true: they are audited by being used. And the chains inform decisions; they never enforce anything by themselves — a stale map must never be able to block true work.

When to trace — one principle: trace when enough has changed that your picture of where you stand may be stale — after a large stretch of work, at a boundary (boot, handover, new mission, confusion), or when someone asks you to reorient. Why not simply "every N hours": identical scheduled prompts fade from an agent's attention with repetition, and idle seats accumulate ritual traces that crowd out real context. Where a schedule fits your context anyway, use one — but prefer gating the action on evidence of change: scripts/trace-due.sh decides "has enough happened since my last trace?" deterministically and stays silent when the answer is no, so a scheduler can fire it as often as it likes.

5b. Trace and rig walk — one idea, two ends

These get confused because they share a word. They are not in conflict; they are the same thing seen from either end, and the relationship is worth holding.

A chain is an ordered sequence of context meant to be absorbed one piece at a time. Two kinds exist and both are chains in that sense:

  • Altitude chain — the same filename at every level of a tree, read by ascending (LEARNED.md; SPEC.md with intent:). The sequence is position: leaf → root.
  • Boot chain — a seat's startup reading sequence. The sequence is order of onboarding.

A chain can be traversed two ways, and that is the only real difference:

who drivesmechanismwhen
PULLthe agentcompose.py up renders the chain; the agent reads itit is awake and oriented enough to look — a trace, a refocus
PUSHan orchestratorrig walk --through <files> --pace <n> sends one piece at a time into the paneit cannot self-start — freshly cleared, re-primed, cold

Pacing is the mechanism in both directions, and it is the load-bearing part. Absorption between pieces is what makes a chain land; a concatenated dump of the same bytes is a failed delivery regardless of content. That is why rig walk elapses --pace between pieces, and why a composed render is meant to be read as a sequence rather than skimmed as a wall.

Use distinct names for the two directions:

  • TRACE — the pull ascent. compose.py up renders a node's chain to the root.
  • rig walk — the push verb for paced delivery into a seat's pane.

Use refocusing for the current trace workflow. A delivery receipt and an agent's read-depth report answer different questions; pacing alone does not establish understanding.

Show full SKILL.md (1,432 more words)Show less

6. Writing — two principles

  1. Your LEARNED.md is yours. You write it, in your own words, as part of doing the job — when you learn something about how to do your work, the file carries it before you move on. When anyone else wants it changed (a correction, new doctrine), they tell you and you write it — knowledge someone else typed into your file was never yours. The one exception: when an instance is empty or broken, whoever is responsible for it writes what's needed, marks those lines as written-for-the-instance, and the next occupant rewrites them in its own words.
  2. Shipped things belong to their authors. openrig-core's operating-model skill and the shipped culture change through their owners, never by an instance editing in place. If it is wrong for everyone, propose the change to its owner; if it is wrong for you, that's what LEARNED.md is for.

Everywhere: date what you write (from the clock), and correct by adding a dated correction rather than silently rewriting history.

7. Composed views

Any node can be rendered: its chains assembled into one document (scripts/compose.py; up = your effective view from a leaf, down = every chain file under a root — run down at a tree root and you get the whole operating picture in one document). Two rules:

  • Rendered documents are generated, never edited. The chain files are the source of truth; a render is a snapshot view of them.
  • Every chain render opens with a TRACE — a tree-shaped orientation header derived at render time (never stored in any file): one line per level showing its state (seeded ✓ / unseeded ⟂ / stale-marker ⚠ / absent ✗) and a "you are here" anchor at the leaf. It shows the shape of your context the way tree shows the shape of a directory — where you sit, and where the screams are, before you read a word of content.
  • When an approval must freeze exact content (for example a plan-lock on a spec), it records the hash of a render — the frozen bytes live in the approval record while the chain files stay live for reading and revision.

The root render + diff is how a high-altitude seat keeps a current mental model of a changing fleet without reading everything: render the tree root, diff against your previous render, read only the diff (scripts/trunk-diff.sh).

8. Where knowledge goes — the placement rule

Context lives at the narrowest scope that needs it; skills are only for what has no scope. Seat-specific knowledge → that seat's LEARNED.md. Rig practice → the rig's files. Only truly scope-free craft (useful to any agent anywhere) belongs in the skill layer.

The one axis that decides file-vs-plugin: KIND or POSITION
  • A chain holds knowledge about a POSITION — this seat, this mission. Unshareable by construction, because the path is the identity.
  • A plugin holds knowledge about a KIND — a seat-type's job, a domain's craft, an operating model. Shareable, versionable, cross-harness.

A plugin cannot hold LEARNED. Plugins are shared; LEARNED is per-instance. Two rigs installing the same orchestrator plugin must not share what one seat learned about its own merge desk. That is why chains exist alongside plugins rather than being replaced by them.

And plugins are the distribution mechanism, which imposes a hard test. A plugin ships skills; a skill may ship a script; that script must run unmodified on a stranger's machine. So it resolves paths from configuration (rig config get workspace.root), never from a literal, and it never asks the agent to work out which of several candidate directories is the real one. If a user points their workspace at a git repo or a projects/ folder, everything keeps working with no further setup. A script that only works for its author is not shippable, however correct it is.

What belongs INSIDE a node — two tests before a sentence goes in

A node holds what is true of THIS position and stays true.

  • Portability. If a sentence would read correctly on a stranger's machine, it is knowledge about a KIND. It belongs in this skill or a plugin, not on the tree. A node body that is fully portable is documentation that wandered onto the chain.
  • Volatility. A value that changes faster than the file gets edited — a SHA, a count, a status, a roster — goes in as the command that derives it, never as the answer.

Then check which tree. Traps, practice and how-we-work are position knowledge on the topology tree (LEARNED.md). What is being built is the work tree (SPEC.md). One rig owns both; they still do not mix.

Keep specification depth proportional. A project carries stable intent; a mission carries its intent and mission-level specification in the same authored SPEC.md. Slices provide their concrete implementation scope. Follow the selected mission-slice-sop and any explicit mode overlay; mission specification is not a requirement to repeat every slice detail.

Project knowledge: three kinds, three homes

Before you write down something you learned about a project, decide which kind it is.

  • Product facts say what is true of the software at a version: commands, behaviour, formats. They live in the product's own documentation, versioned with the code and checked against source. Anywhere else, link to them rather than copying them; a copy stops following releases.
  • Project judgment says how to work on the project now: intent, workflow, priorities, routes, who decides what. It lives in the project's world pack and its work tree, and it can change without a release.
  • Machine-local state is true of one instance only: paths, hosts, live rosters, counts, credentials. It belongs in configuration, in a seat's LEARNED.md, or as the command that derives it. It never goes into shipped content or a shared world.

Agents find a project's declared context with rig context work-install, which lists what the project declares: its intent, the context files in its project.yaml install block, and its skills. When project knowledge should reach every agent working on the project, add it to that declaration rather than starting a second index.

The other axis: AUDIENCE is not MATURITY

These are independent, and merging them is seductive because the merged version is prettier.

  • Audience decides WHERE knowledge lives — which altitude, which file. Something belongs at pod level because anyone in that pod benefits. Full stop.
  • Maturity is an attribute of a LINE inside that file — the epistemic ladder: data → observation → field note → insight → canon. The vocabulary already ships as the stage: enum (wip | provisional | established | canonical | superseded | retired).

A pod-level item can be raw observation; a seat-level one can be canon. A skill can contain something immature and still be the right home, because audience picked the file.

So promotion to canon is NOT a move up the tree. It is a maturity event and can happen at any altitude — a seat-level observation that is universally true graduates straight to a skill. What earns maturity is evidence: recurrence, independent corroboration, a measured cost, surviving change, surviving an attempt to falsify it. Facts about mechanisms can skip the ladder (backticks substitute in double-quoted shell strings is one command away); inferences about practice must accrue (never broadcast to a large rig took an incident).

LEARNED.md is not a staged item — it is the bed everything lies in. Its gradient is positional: the dated append-log at the bottom is raw observation, the concise sections at the top are what survived. Attach distillation to a trigger: distil at deposit boundaries (pre-clear, pre-handover) where a write is already required and the author still remembers why each line exists; refocus merely notices when the log has outgrown the distilled part.

9. Standing the structure up

scripts/scaffold.sh creates any missing chain files from templates/ and never overwrites or deletes anything — so a brand-new workspace and a living system are the same command with different starting states. Created files are marked UNSEEDED until their real owner writes the first true version. Never mass-produce LEARNED.md content for other instances — each instance writing its own first version is both how the knowledge becomes real and how you discover which seats cannot yet describe their own job.

10. Pitfalls (only what isn't taught above)

  • Editing a rendered document instead of its chain files — your edit is lost at the next render, silently.
  • "Improving" the trace with pointer-following, serves-resolution, or any branching — the trace's entire value is that path-only ascent cannot fail subtly; keep the trace path-only.
  • Summarizing this model for another agent instead of pointing them here — a summary becomes another copy that can drift from its source.
  • Scripts in scripts/ are macOS-flavored in places (stat -f); verify platform compatibility before trusting them on another OS.

Files in this skill

templates/{SPEC,LEARNED,SOP}.md · scripts/{compose.py, trace-due.sh, trace-stamp.sh, trunk-diff.sh, scaffold.sh}

© mvschwarz, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts) in packages/daemon/assets/plugins/openrig-core/skills/openrig-operating-model of mvschwarz/openrig.

  • SKILL.md
  • scripts/compose.py
  • scripts/scaffold.sh
  • scripts/trace-due.sh
  • scripts/trace-stamp.sh
  • scripts/trunk-diff.sh
  • templates/LEARNED.md
  • templates/NOTES.md
  • templates/PLAYBOOK.md
  • templates/SOP.md
  • templates/SPEC.md

Open the folder on GitHubat commit 1f69831

Compare with similar skills

Openrig Operating Model next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Openrig Operating Model compared with similar skills
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Openrig Operating Model this skillmvschwarz/openrig5.9k—~6.2kAutomated safety check: PassApache-2.0
MCP Server Builderanthropics/skills180k64 repos~2.3kAutomated safety check: PassApache-2.0
Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k11 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers296k2 repos~5.1kAutomated safety check: PassMIT
Claude Code Agent Developmentanthropics/claude-plugins-official38k8 repos~2.8kAutomated safety check: PassApache-2.0

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    Create new skills, modify and improve existing skills, and measure skill performance.

    795 GitHub starsUsed in 89 repos~8.2k tokens
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All 49 skills in this repo
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  • Agent Refocusing

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  • OpenRig Software Factory

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    Helps set up a continuing agent software team for a real repository with OpenRig, choosing between manual work, queue handoffs and an explicit Workflow.

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  • Loads one section of a Markdown file by its path#h2-slug address with a bundled resolver script, for use outside OpenRig's context library.

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  • Separates a stable agent seat's identity from its changing occupant, and records honest, two-part provenance whenever one occupant replaces another.

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  • Cross Host Rig Commands

    mvschwarz/openrig

    A skill your agent uses when addressing a registered remote OpenRig host, choosing its transport, or interpreting a cross-host result.

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Categories

Questions about Openrig Operating Model

What does Openrig Operating Model do?

A skill your agent uses when you do not know WHERE something belongs: you learned a lesson and are unsure which file takes it; you are about to create a new doc, folder, or convention; you are…. Openrig Operating Model is an agent skill from mvschwarz/openrig."; you inherited a seat and want to know what SHOULD exist; or you are about to duplicate knowledge that already has a home.

When should I use Openrig Operating Model?

Openrig Operating Model fits situations like: you do not know WHERE something belongs: you learned a lesson and are unsure which file takes it; you are about to create a new doc; you are asking should this be a skill; you inherited a seat and want to know what SHOULD exist.

How do I install Openrig Operating Model in Claude Code?

Run `npx skills add mvschwarz/openrig --skill openrig-operating-model -a claude-code`. Or copy the skill folder (packages/daemon/assets/plugins/openrig-core/skills/openrig-operating-model in mvschwarz/openrig) into .claude/skills/openrig-operating-model in your project. Claude Code loads it when a task matches its description.

How do I install Openrig Operating Model in Codex?

Run `npx skills add mvschwarz/openrig --skill openrig-operating-model -a codex`. Or copy the skill folder (packages/daemon/assets/plugins/openrig-core/skills/openrig-operating-model in mvschwarz/openrig) into .agents/skills/openrig-operating-model in your project. Codex loads it when a task matches its description.

Can I use Openrig Operating Model in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mvschwarz/openrig --skill openrig-operating-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/openrig-operating-model, .gemini/skills/openrig-operating-model, .github/skills/openrig-operating-model and .opencode/skills/openrig-operating-model in your project.

What does Openrig Operating Model need to run?

Going by SKILL.md and its folder, Openrig Operating Model needs a shell and Python for the scripts in its folder. Our summary lists: Python 3; A Bash shell.

Does Openrig Operating Model access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Openrig Operating Model safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Openrig Operating Model use?

Openrig Operating Model is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Openrig Operating Model use?

About 6.2k tokens (SKILL.md is roughly 25k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Openrig Operating Model?

Skills that share tags, products or a category with Openrig Operating Model: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 296k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Openrig Operating Model?

mvschwarz (a GitHub user) maintains it in mvschwarz/openrig, which has 5,854 GitHub stars. The repository holds 49 skills in this directory. The repository was last updated on October 8, 2026.

Source: mvschwarz/openrig on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.