{"slug":"mvschwarz-forming-an-openrig-mental-model","name":"forming-an-openrig-mental-model","description":"Use when the system around you does not make sense yet: you just booted into a seat and do not know how the pieces fit; someone said rig, pod, seat, fleet, topology, or slice and you are not certain what they mean here; you are unsure what kind of rig you are in or what it is for; you do not know how skills reach you or where context comes from; or you are about to act on a guess about how OpenRig works. Gives the runtime mental model fast, so you stop guessing.","long_description":"---\nname: forming-an-openrig-mental-model\ndescription: >-\n  Use when the system around you does not make sense yet: you just booted into a seat and do not\n  know how the pieces fit; someone said rig, pod, seat, fleet, topology, or slice and you are not\n  certain what they mean here; you are unsure what kind of rig you are in or what it is for; you do\n  not know how skills reach you or where context comes from; or you are about to act on a guess\n  about how OpenRig works. Gives the runtime mental model fast, so you stop guessing.\nmetadata:\n  cli_surfaces_referenced:\n    - ask\n    - capture\n    - down\n    - ps\n    - send\n    - transcript\n    - up\n    - whoami\n  openrig:\n    stage: factory-approved\n    sibling_skills:\n      - openrig-user\n      - openrig-operator\n      - openrig-builder\n      - openrig-architect\n      - openrig-upgrade\n      - agent-operated-workflows\n      - agent-operated-software\n---\n\n# Forming an OpenRig Mental Model\n\nYou're new to OpenRig — or returning after time away — and you need to\nquickly understand what kind of system this is, what your seat is, and what\nthe moves are. This skill is the fast on-ramp.\n\nFor depth, read the canonical reference docs the skill points to. This\nskill's job is to get you *oriented* — accurate enough to operate, fast\nenough to be useful — not to replace the canonical docs.\n\n---\n\n## The 60-second mental model\n\nOpenRig is a **local control plane for multi-agent coding topologies**. You\ndeclare a topology of agents in YAML, boot it with one command, and OpenRig\nmanages tmux sessions, harness lifecycles, transcripts, snapshots, and\nrestoration. When the system goes down, OpenRig snapshots; when it comes\nback, agents resume their conversations.\n\nThe product loop:\n\n```\ndown (auto-snapshot) → up <rig-name> (auto-restore) → work → repeat\n```\n\nThe unit of work is the **rig** — a topology of agents working together as\na single system.\n\n---\n\n## The four-layer model (where you live)\n\nEverything in agent engineering happens at one of four layers. **OpenRig\noperates at Layer 3.**\n\n| Layer | Name | Analogy | What it is |\n|---|---|---|---|\n| L0 | Model | CPU | Foundation model — Claude, GPT, Gemini. Stateless tokens-in/tokens-out. |\n| L1 | Agent Core | Process loop | The reason-and-act cycle: observe, plan, choose, act, repeat. |\n| L2 | Harness | Container / OS | Tools, memory, lifecycle around the model. Examples: Claude Code, Codex CLI. |\n| L3 | Rig | Docker Compose / Terraform | Multi-agent topology — what agents exist, how they relate. **OpenRig.** |\n\nYou are an agent at L1 inside an L2 harness, configured by L3 OpenRig.\nOpenRig manages your harness; the harness wraps the model; the model\ngenerates your tokens.\n\n---\n\n## Three pillars of context\n\nOpenRig is built on three context-engineering pillars. When you're oriented,\nyou should know which pillar you're operating in:\n\n| Pillar | What it is | Where it lives |\n|---|---|---|\n| **Ontology** | What exists. Curated knowledge — facts, code maps, as-built docs. | Shipped public context packs plus project-authored docs; discover with `rig context list`. |\n| **Epistemology** | Why an agent believes what it believes — reasoning, instincts, decisions. | Transcripts (auto-captured). Session logs. ADRs. |\n| **Topology** | How agents are connected — pods, edges, communication paths. | OpenRig itself. RigSpec YAML. |\n\nOpenRig manages topology and exposes public context through `rig context`.\nProject-authored sources supply project-specific knowledge; transcripts retain\nrecorded work. These sources already coexist. Discover the configured library\nand selected task context rather than assuming a particular private corpus.\n\n---\n\n## The core vocabulary (read these terms literally)\n\n| Term | What it means |\n|---|---|\n| **Rig** | A topology of agents working together as a single system. Defined in YAML (RigSpec). The top-level object. |\n| **Pod** | A bounded context group within a rig. Members of a pod share a context domain and continuity responsibility. Think Kubernetes pod for knowledge. |\n| **Member / Node** | A single agent (or terminal-node service) within a pod. |\n| **Edge** | A relationship between members or pods. Kinds: `delegates_to`, `spawned_by`, `can_observe`, `collaborates_with`, `escalates_to`. |\n| **Topology** | The shape of the rig — how agents are grouped into pods, how edges connect them, how the whole thing fits together. |\n| **AgentSpec** | A reusable agent blueprint. Defines skills, guidance, hooks, profiles, startup. File: `agent.yaml`. |\n| **RigSpec** | The topology YAML. Defines pods, members, edges, culture. File: `rig.yaml`. |\n| **RigBundle** | A portable archive of a RigSpec + vendored AgentSpecs. Move topologies across machines. |\n| **Agent Starter** | A named, reusable starting context bundle. RigSpec member can declare `starter_ref`. |\n| **Skill** | A markdown file with frontmatter that an agent loads at boot or on activation. Cross-runtime standard at `agentskills.io`. |\n| **Profile** | A named configuration within an AgentSpec. The rig spec's member field selects which profile to use. |\n| **Culture** | Rig-wide constitution — how the team communicates, what \"done\" means, escalation rules. File: `CULTURE.md`. |\n| **Snapshot** | Point-in-time capture of a rig — sessions, conversations, state. Restorable. |\n| **Session name** | `{pod}-{member}@{rig}`. The canonical address for tmux sessions and agent-to-agent messaging. |\n\nThe session-name format `{pod}-{member}@{rig}` is your address. When you\nrun `rig whoami --json`, you get back your full topology context: rig name,\npod, member, peers, edges, transcript path.\n\n---\n\n## Rig classes (what kind of rig am I in?)\n\nOpenRig has five rig classes. The class determines authoring discipline,\nsupervision, and lifecycle policy.\n\n| Class | Purpose | Lifecycle |\n|---|---|---|\n| **kernel** | Host-level supervision, intake, authoring. One per host. | Always on; never auto-hibernated. |\n| **project** | Long-lived team bound to a codebase. | Stays hot when active; hibernates on explicit request. |\n| **ephemeral** | Short-lived mission (research, build, migration, spike). | Spawn → work → retire. |\n| **infra-build** | Subclass of ephemeral whose output becomes permanent infrastructure. | Retired only after output verified in place. |\n| **managed-app** | Services-backed rig with specialist agents (e.g., a vault specialist, a skill librarian). | Long-lived; accessed by other rigs. |\n\nYou're probably in a project rig or managed-app rig if you're doing\nsubstantive work. Knowing your class helps you understand the supervisory\nexpectations on your seat.\n\n---\n\n## How skills load (the most important thing to get right)\n\nSkills are an **established cross-runtime standard** at\n`https://agentskills.io/specification`. Both Claude Code and Codex build on it.\n\n### The shape\n\nA skill is a directory containing `SKILL.md` (uppercase). The SKILL.md has\nYAML frontmatter (`name`, `description`) and a Markdown body. Optional\nsibling directories: `references/`, `scripts/`, `assets/`.\n\n### Progressive disclosure (why skills scale)\n\nThe harness reads frontmatter cheaply at boot — names + descriptions of all\navailable skills. Body content loads only when a skill activates. This is\n**ambient awareness** — you know all the skills exist; you only pay token\ncost when you reach for one.\n\n### Where skills come from in OpenRig\n\n- **Per-agent loadout:** your AgentSpec's `profile.uses.skills: [...]`\n  determines what skills get projected into your runtime skill folder\n  (`.claude/skills/` or `.agents/skills/`) before your harness boots. This\n  is the **structural composition** layer.\n- **Cross-pod sharing:** AgentSpecs can `imports: [shared]` to access a\n  shared skill pool. Built-in agents commonly do this.\n- **Belt-and-suspenders:** the spec projects skill files; startup guidance\n  also tells you to load specific skills. Both paths matter — if the\n  projection silently fails, the guidance still tells you what to read.\n\n### Where skills live (sources of truth)\n\n| Home | Purpose |\n|---|---|\n| `<rig-cwd>/.claude/skills/`, `<rig-cwd>/.agents/skills/` | Where the harness actually loads from. Populated by `rig up`. |\n| `~/.claude/skills/`, `~/.agents/skills/` | User-level harness skill directories. Inspect the current projection and harness configuration to determine which skills are installed and where they came from. |\n| `openrig/packages/daemon/{specs/agents/shared/skills,assets/plugins/*/skills}/` | Product skills that ship with OpenRig — the spec pool + the bundled plugin assets (openrig-user, openrig-operator, openrig-architect, forming-an-openrig-mental-model, queue-handoff, claude-compaction-restore, …). |\n| the skills authoring workspace | Skill authoring source (not runtime-loaded). |\n\nThe product already discovers its installed shared skill pool and serves\npackaged context through `rig context list/get`. Discovery and retrieval do\nnot prove that every skill was projected into every harness. Inspect the\nselected profile and actual runtime directories; do not assume a universal\nhome/bootstrap installation from this table.\n\n---\n\n## The product loop (your day-to-day)\n\n```\nrig up <rig-name>         # boot or restore the topology\nrig ps --nodes            # see what's running\nrig whoami --json         # know who you are\nrig send <session> \"msg\"  # talk to a peer\nrig capture <session>     # see a peer's terminal\nrig transcript <session>  # read a peer's history\nrig down <rigId>          # snapshot and tear down\nrig up <rig-name>         # restore from snapshot\n```\n\nThe first command in any new seat is `rig whoami --json`. It tells you your\nrig, pod, member, peers, edges, and transcript path. **Treat it as ground\ntruth — your CLAUDE.md or AGENTS.md startup overlay can be wrong; whoami\nis authoritative.**\n\n---\n\n## Cultural posture (how to behave)\n\nOpenRig has a few load-bearing cultural principles. Internalize these:\n\n- **Honesty over convenience.** If resume fails, say so loudly. Don't\n  silently launch fresh.\n- **The agent is the power user.** The CLI is designed for a 10x staff\n  engineer at the terminal. You're that user.\n- **CLI is context engineering.** Every error message and help text gives\n  you information to act on. Read errors carefully.\n- **Convention over invention.** Follow docker/git/kubectl patterns. Agent\n  muscle memory is real.\n- **Semi-deterministic is OK.** Core contracts are solid; edge cases are\n  agent-handled.\n- **Pets, not cattle (today).** OpenRig is currently optimized for long-lived\n  agents that develop instincts over sessions. Cattle support is on the\n  roadmap.\n\n---\n\n## What you should do in your first 10 minutes\n\nIf you're booting into a new seat in an OpenRig rig:\n\n1. **`rig whoami --json`** — recover identity. Know your rig, pod, member,\n   peers.\n2. **Read your role guidance** — typically delivered via startup files.\n   `guidance/role.md` for your specific seat.\n3. **Read the rig's `CULTURE.md`** if it has one — the team operating\n   manual.\n4. **Check what skills you have** — list `.claude/skills/` or\n   `.agents/skills/` in your cwd. Each skill has a frontmatter description\n   that tells you when to reach for it.\n5. **Check your peers** — `rig capture <peer-session>` to see what they're\n   doing.\n6. **Check the transcripts** if you're returning to an in-flight workstream\n   — `rig transcript <session> --tail 100` for recent context.\n7. **Ask `rig ask <rig> \"<question>\"`** if you need cross-cutting evidence\n   from the rig's transcripts and chat.\n\nYou're now oriented enough to start doing useful work.\n\n**Permission policy (at setup):** OpenRig sets only a minimal usability floor on your harness permissions, then offers recommended policies you opt into (Locked / Standard / Open — or YOLO to bypass). If you're creating or bringing up a rig, that's a choice you make, not something OpenRig decides for you — see openrig-user's \"Permission policy — pick one at setup\" and the `applying-a-permission-policy` skill.\n\n---\n\n## Going deeper (canon","tagline":"Use when the system around you does not make sense yet: you just booted into a seat and do not know how the pieces fit; someone said rig, pod, seat, fleet, topo","category":"automation","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["agent-skill"],"author":"mvschwarz","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"recursive skill source sync","sourceDetail":"mvschwarz/openrig","creatorName":"mvschwarz","creatorUrl":"https://github.com/mvschwarz","sourceUrl":"https://github.com/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/forming-an-openrig-mental-model","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/mvschwarz-forming-an-openrig-mental-model#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":66,"forks":11,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":36.48},"quality":{"score":62,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"66","tone":"neutral"},{"label":"Freshness","value":"1mo ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"Apache-2.0","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":60,"base_score":68,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["60/100 Trust Score v5","68/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is missing","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"66 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":48,"weight":0.08,"status":"warn","detail":"66 stars, 11 forks; 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The tracked source changed or could not be synchronized. Review the current source before installing. Do not install or execute repository code in this review. Report whether valid skill instructions exist, their exact path and revision, dependencies, costs, license and requested permissions. Ask for approval before any installation. 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