openrig-architect — Claude Code skill: design and install

---
name: openrig-architect
description: Use when designing multi-agent topologies that run ON OpenRig — authoring RigSpec and AgentSpec files for new rigs, creating agent startup content (guidance / skills / culture), or diagnosing why a launched rig's agents aren't behaving as intended. NOT for changing OpenRig itself (use developing-openrig); NOT for ordinary CLI operation of an existing rig (use openrig-user). Covers the full authoring lifecycle from user intent to validated, launchable rig.
metadata:
  cli_surfaces_referenced:
    - agent validate
    - capture
    - daemon start
    - ps
    - send
    - spec validate
    - specs ls
    - up
    - whoami
  openrig:
    stage: factory-approved
    sibling_skills:
      - openrig-user
      - openrig-operator
      - developing-openrig
      - openrig-upgrade
      - forming-an-openrig-mental-model
      - ai-dev-workflows

---

# OpenRig Architect


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: select the relevant sources

Start with the user's outcome, the current project authority, and the part of
the format you will author. Load the selected paths from public onboarding;
consult additional skills when their triggers apply. A design task does not
require reading the whole command library.

- Read `topology-naming.md` before choosing new identities: rig = purpose, pod =
  domain, member = role. Keep names stable across runtime choices and match every
  edge, preset and workflow target to the declared IDs.
- Read the relevant sections of `rig-spec.md` and `agent-spec.md` before writing
  those declarations. Consult `agent-startup-guide.md` for startup/loadout work
  and `edge-types.md` when selecting relationships. Resolve the installed
  reference root from the actual OpenRig installation; `~/.openrig/reference/`
  is a default, not a fixed location. If it is absent, use the matching source
  repository docs or report the missing reference. Reading does not require
  starting or changing a daemon.
- Use current `rig <command> --help` for command shape. Load `openrig-user` for
  the specific CLI surface needed, through the installed skill/context catalog.
- Inspect relevant starter specs with `rig specs ls`. Treat them as examples to
  validate against the current environment, not proof that your new rig works.
- If the environment declares host or project doctrine, read the actual
  applicable authority and reconcile it with the task. Do not assume a filename,
  section number, rig classification or fixed authoring SOP. Missing authority
  is a question to resolve when it changes the design.
- Load domain-specific guidance for specialist roles. When authoring an
  agent-facing tool, consult `building-agent-software` if available.

Keep the spec, startup layering, role responsibilities and selected proof
standard explicit. Scale the reading and checks to what the design changes.

## The Design Process

### Step 1: Understand the User's Intent

Before touching YAML, understand what the user actually needs:

- **What is the goal?** Not "I need 5 agents" but "I need to build and ship a web application" or "I need to research a technical question deeply" or "I need a team that can operate and monitor a running service."
- **What are the workflows?** How does work flow from intent to completion? Who does what? Where are the handoffs?
- **What is the project?** What codebase, what tech stack, what domain? This shapes agent specialization and startup content.
- **What runtimes are available?** Does the user have Claude Code? Codex? Both? Runtime availability constrains topology design.
- **How autonomous should it be?** Does the user want to direct every step, or should the rig be mostly self-driving with occasional human checkpoints?

Ask clarifying questions if the intent is ambiguous. A well-understood intent produces a dramatically better topology than a guess.

### Step 2: Identify Bounded Contexts → Pods

Every rig is organized into pods — bounded context groups where members share a workflow concern. The question is: what are the natural groupings?

**Common pod patterns:**

| Pod | Purpose | When to use |
|-----|---------|-------------|
| Orchestration | Coordination, dispatch, monitoring | Almost always — any rig with 3+ agents needs an orchestrator |
| Development | Implementation, testing, quality | Any rig that writes code |
| Review | Independent code review, architecture review | When quality gates matter (production code, security-sensitive work) |
| Research | Deep investigation, analysis, synthesis | When the work requires research before implementation |
| Design | UX, interaction design, product decisions | When the work has a user-facing interface |
| Specialist | Domain-specific operations (Vault, DB, infra) | When a specific technology needs dedicated expertise |

**Sizing principles:**

- **Solo agent:** Only when the task is genuinely single-person (quick script, simple question). No rig needed.
- **Pair (2 agents):** The minimum effective unit for quality work. One does, one verifies. The built-in `starter` team's shape.
- **Small team (3-5 agents):** Orchestrator + one or two working pods. Good starting point for focused projects.
- **Full team (6-10 agents):** Multiple bounded contexts with orchestration, development, review, and potentially research or design.
- **Large team (10-40+ agents):** Complex projects with many concerns. Include pods for development, review, research, documentation, release management, strategy, and any other bounded context the project needs.

**Important:** Agents do NOT all need to be busy at the same time. A rig is a network, not an assembly line. Some pods will be highly active (dev, review) while others are available on-demand (research, documentation, release management). An idle agent has near-zero cost but is immediately available when any other agent in the rig needs it — for quick questions, lookups, delegation, or specialized work. Design for availability, not constant utilization.

**Start small to increase the likelihood of success,** not because large rigs are wasteful. A 3-agent rig that boots and works correctly validates your spec authoring before you scale to 20 agents. Once the core topology works, expand with additional pods as needed.

### Step 3: Design Agent Roles → Members

Each pod member needs a clear role. The role determines:
- What agent spec to reference (builtin or custom)
- What profile to use
- What guidance and startup content to provide

**Builtin agents shipped with OpenRig:**

| Agent | agent_ref (in shipped starters) | Purpose |
|-------|-------------------------------|---------|
| orchestrator | `local:agents/orchestration/orchestrator` | Rig orchestration lead |
| implementer | `local:agents/development/implementer` | TDD implementation agent |
| qa | `local:agents/development/qa` | Quality assurance agent |
| independent-reviewer | `local:agents/review/independent-reviewer` | Independent code reviewer |
| product-designer | `local:agents/design/product-designer` | Product designer |
| pm | `local:agents/product-management/pm` | Product manager |
| analyst | `local:agents/research/analyst` | Research analyst |
| synthesizer | `local:agents/research/synthesizer` | Research synthesizer |
| vault-specialist | `local:agents/apps/vault-specialist` | Vault domain specialist |

To verify the current builtin set on this host, run `rig specs ls` and look for entries with type `agent` and source `builtin`.

**Path resolution:** The `local:` prefix means relative to the rig spec file's directory. In shipped starters, these paths resolve against the builtin specs directory inside the OpenRig installation. When authoring a custom rig spec outside the installation, you have two options:
- **Reference your own agent specs** with `local:` paths relative to your rig spec file
- **Use `path:` with an absolute path** to reference builtins inside the OpenRig installation (look under the `specs/agents/` directory near where `rig` is installed)

**When to create a custom agent spec:**
- The builtin doesn't match the role (e.g., you need a documentation specialist, a security auditor, a data scientist)
- The role needs domain-specific skills that no builtin carries
- The role needs custom guidance that goes beyond what startup files can provide

**When to reuse a builtin:**
- The role maps cleanly to an existing builtin (most implementation, QA, review, and orchestration roles)
- You can customize behavior through startup files and culture without changing the agent spec

### Step 4: Choose Runtimes and Models

Each member needs a `runtime` and optionally a `model`.

Choose from the installed, authenticated runtimes and the project's current
execution policy. `claude-code` and `codex` are agent runtimes; `terminal` is an
infrastructure process. Their model availability, hooks, approval behavior and
continuation support differ. Check the relevant installed interfaces rather
than ranking vendors permanently in a reusable role skill.

Pin a model when the work or environment requires it, and verify the active
runtime reports that model before relying on its result. Select reviewers and
support roles by consequence, competence and the declared policy. Runtime
diversity can provide different methods; it does not by itself prove independence
or make any model suitable for a task.

### Step 5: Design Edge Topology

Edges define relationships between members. See `~/.openrig/reference/edge-types.md` for the full reference.

**Practical rules:**
- Every working pod should have at least one `delegates_to` edge from the orchestrator
- Review pods should have `can_observe` edges to the pods they review
- Within a pod, the primary workflow direction should be expressed as `delegates_to` (e.g., impl → qa)
- `delegates_to` and `spawned_by` affect launch order. Use them for dependency chains.
- `can_observe`, `collaborates_with`, `escalates_to` are informational — they help agents understand the topology but don't constrain launch.

**Start simple.** You can always add edges later. A rig with only `delegates_to` edges from the orchestrator to working pods is perfectly functional.

### Step 6: Design Startup Content Strategy

This is where most rigs succeed or fail. The topology is mechanical; the startup content is what makes agents actually useful. See `~/.openrig/reference/agent-startup-guide.md` for the full guide.

**Minimum for every rig:**
1. Each agent has a `guidance/role.md` — who they are, what they do
2. The rig has a `CULTURE.md` — how the team works together
3. Each agent gets the selected onboarding path and can discover the relevant
   command/skill references when needed

**For serious rigs, also include:**
4. `startup/context.md` per agent — boot-time grounding (project info, environment details)
5. Pod SOP skills — how each pod operates (build-pair SOP, review-pair SOP, etc.)
6. Project-specific documentation in rig-level startup files

**The key principle:** An agent that boots without knowing its role, its team's culture, and its project context will produce generic, unhelpful work. The startup content IS the product value. Invest in it.

### Step 7: Services Integration (If Needed)

If the rig needs managed software (databases, API servers, etc.), add a `services` block. See `~/.openrig/reference/rig-spec.md` for the full services reference.

**When to add services:**
- The agents operate ON software (not just write code)
- The project needs a local dev environment (Postgres, Redis, etc.)
- You're building a managed-app rig (software + specialist agent)

**Services boot before agents.** If health checks fail, no agents start. This is the hard gate — the environment must be healthy before agents can work.

## Authoring: The File Creation Workflow

### Directory Layout

```
my-rig/
  rig.yaml                    # The RigSpec — required
  culture/
    CULTURE.md                # Rig-wide culture — strongly recommended
  agents/
    my-custom-agent/
      agent.yaml              # AgentSpec — if custom agent needed
      guidance/
        role.md               # Role guidance
      startup/
        context.md            # Boot-time context
      skills/
        my-skill/
          SKILL.md            # Custom skill if needed
  docker-compose.yaml         # Only if services block is used
```

For rigs that reuse builtin agents, the agents directory is often unnecessary — the rig spec references the builtins directly.

### Workflow

1. **Write the rig spec** (`rig.yaml`) — define pods, members, edges, optionally services
2. **Write or reference agent specs** — builtins for standard roles, custom for specialized roles
3. **Write CULTURE.md** — the team operating manual
4. **Write role guidance** for each custom agent — who they are, what they do
5. **Write startup context** for agents that need environment grounding
6. **Validate:** `rig spec validate rig.yaml` and `rig agent validate agents/*/agent.yaml`
7. **Confirm the runtime cwd** — do not assume agents should work from the directory where the rig spec is stored. The spec root controls file resolution; the runtime cwd controls trust, project guidance, permissions, and repo context.
8. **Launch:** `rig up rig.yaml --cwd /path/to/project`
9. **Verify:** `rig ps --nodes` — all agents ready? Check `rig capture` on each agent.

### Validation Is Non-Negotiable

Always validate before launching:

```bash
rig spec validate rig.yaml
rig agent validate agents/my-agent/agent.yaml
```

Then run `rig spec audit rig.yaml` for advisory checks such as stale seat references and other cross-file drift that schema validation cannot detect.

If validation fails, fix the errors. Do not try to launch an invalid spec — it will fail with a less helpful error.

## Diagnosis: When Things Go Wrong

### Agent doesn't know its role

**Symptom:** Agent produces generic output, doesn't follow team conventions.
**Root cause:** Missing or insufficient `guidance/role.md`.
**Fix:** Write a clear role guidance file. Include responsibilities, working rhythm, and principles. Reference it in both `resources.guidance` and `startup.files`.

### Agent can't coordinate with peers

**Symptom:** Agent tries raw tmux commands instead of `rig send`, doesn't know peer session names.
**Root cause:** Agent didn't receive `openrig-user` skill or `openrig-start` overlay.
**Fix:** Ensure the agent's profile `uses.skills` includes `openrig-user`. Verify via `rig ps --nodes` that the agent shows expected startup status; check installed skills via direct startup/capture/transcript evidence or the UI node detail (the `rig ps --nodes` projection does not expose installed-resource counts).

### Agent hits approval prompts on rig commands

**Symptom:** Agent stalls on `rig whoami`, `rig send`, etc.
**Root cause:** Claude Code permissions not configured for rig commands.
**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 matrix.

### Agents idle — topology doesn't engage the team

**Symptom:** Orchestrator works with one or two agents, others sit idle.
**Root cause:** Missing `CULTURE.md` or pod SOP content that describes how the full team coordinates.
**Fix:** Write a culture file that explicitly describes the coordination protocol. Include delegation patterns, review gates, and when each pod should be engaged.

### Services don't boot

**Symptom:** `rig up` fails before agents launch with a service health error.
**Root cause:** Docker Compose issue, health check failure, or port conflict.
**Fix:** Check `docker compose up` manually with the compose file. Verify health check URLs are correct. Check for port conflicts.

### Agent boots from the wrong project context

**Symptom:** Agent misses expected guidance, trust settings, permissions, or repo context even though the spec validates.
**Root cause:** The rig spec directory was treated as the agent's runtime cwd by assumption.
**Fix:** Confirm the intended cwd before launch. The spec can live in a rig/spec shelf while the agent works from the project or hub directory that carries the relevant `AGENTS.md`, `CLAUDE.md`, trust, and permissions. Use member `cwd` or `rig up --cwd` deliberately.

### Startup content not delivered

**Symptom:** Agent is missing expected guidance/skills.
**Root cause:** File paths in the spec don't resolve, or `delivery_hint` is wrong.
**Fix:** Verify file paths resolve relative to their owning artifact — AgentSpec resource paths are relative to the agent spec directory; RigSpec startup, culture, compose, cwd, and `local:` agent-ref paths are relative to the rig root. Check `delivery_hint` — use `guidance_merge` for pre-boot content, `send_text` for post-boot instructions.

### Agent startup delivered but agent doesn't use skills

**Symptom:** Skills are projected but agent doesn't invoke them.
**Root cause:** Agent wasn't told to load them.
**Fix:** In the startup context or role guidance, explicitly tell the agent which skills to load. The belt-and-suspenders pattern: project the skills via the spec AND tell the agent to read them in the guidance.

## Pattern Catalog

### The Implementation Pair
**2 agents, 1 pod.** The smallest effective development unit. One implements (TDD), one does QA. The implementer proposes, QA approves or rejects, then the implementer commits.

```yaml
pods:
  - id: dev
    label: Development
    members:
      - id: impl
        agent_ref: "local:agents/development/implementer"
        runtime: claude-code
        profile: default
        cwd: "."
      - id: qa
        agent_ref: "local:agents/development/qa"
        runtime: codex
        profile: default
        cwd: "."
    edges:
      - kind: delegates_to
        from: impl
        to: qa
```

**Use when:** Focused feature work, bug fixes, small-to-medium implementation tasks.

### The Orchestrated Team
**5-7 agents, 3 pods.** Orchestration + development + review. The orchestrator dispatches work, the dev pair implements, the review pair validates independently.

**Use when:** Production-quality work that needs coordination and independent review.

### The Research Team
**3 agents, 2 pods.** Orchestrator + research pair (analyst + synthesizer). The analyst investigates deeply, the synthesizer consolidates findings.

**Use when:** Technical research, competitive analysis, architecture exploration.

### The Managed App
**1+ agents, 1 pod, services block.** Software infrastructure (Docker Compose) plus a specialist agent who knows how to operate it.

```yaml
services:
  kind: compose
  compose_file: docker-compose.yaml
  wait_for:
    - url: http://127.0.0.1:8200/v1/sys/health

pods:
  - id: vault
    label: Vault
    members:
      - id: specialist
        agent_ref: "local:agents/apps/vault-specialist"
        runtime: claude-code
        profile: default
        cwd: "."
    edges: []
```

**Use when:** The work involves operating software, not just writing code.

### The Full Product Team
**7 agents, 3 pods.** The kitchen-sink topology: orchestration pair, development pod (impl + qa + design), review pair. See the built-in `factory` spec for the complete worked example.

**Use when:** Full product development with design, implementation, QA, and independent review. Requires strong culture and SOP content to keep all agents engaged.

## Final Notes

**Start simple, add complexity when needed.** A working implementation pair is better than a broken full team. Launch with the minimum viable topology, verify it works, then expand.

**Culture is not optional for team rigs.** Any rig with 3+ agents needs a CULTURE.md. Without it, agents will default to generic behavior and the topology will underperform.

**Validate early and often.** Run `rig spec validate` after every change. Run `rig agent validate` after every agent spec edit. Fix errors immediately — don't accumulate them.

**The startup content IS the product.** The YAML topology is scaffolding. What makes a rig actually useful is the guidance, culture, skills, and startup context that agents receive. Invest your authoring time there.