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Openrig Software Factory

CommunityPopular
mvschwarz
openrig-software-factory

Use when a user wants a continuing software team for a real repository, or has a first OpenRig team and needs a repeatable path for reviewed work and later tasks.

Overview

Publishermvschwarz
Repositoryopenrig
Skill nameopenrig-software-factory
Stars
5K
Forks
366
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mvschwarz on GitHub. Read the source before you install it.

Installation

Install the Openrig Software Factory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/openrig-software-factory
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/specs/agents/shared/skills/core/openrig-software-factory .claude/skills/openrig-software-factory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openrig Software Factory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openrig Software Factory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openrig Software Factory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenRig Software Factory

Start with a useful repository outcome and add coordination when it earns its cost. A beginner can complete reviewed work without Workflow. These are choices using existing capabilities, not stages everyone must graduate through.

Choose how the team works

NeedStart hereAdd more when…
One change, close human guidanceManual/team work: give an owner the outcome, use repository instructions, implement and obtain the chosen independent check.Work must survive turns or move between seats.
Continuing work with visible ownershipQueue-supported orchestration: use rig queue create, claim work, then rig queue handoff with the candidate/evidence to the next owner. Record real blockers and the continuation. No Workflow instance is needed.Repeated steps need an explicit dependency graph and permitted exits.
An explicit execution contractWorkflow: inspect rig workflow compile, then deliberately use rig workflow instantiate-lifecycle. Advance its packets through the workflow projection mechanism.The actual project needs reusable profiles, additional roles or gates.

For the concrete queue loop, wake behavior and optional two-slice Workflow, read references/worked-example.md. Installed copy: rig context get skills/core/openrig-software-factory/references/worked-example.md. A roadmap, YAML file or wake does not execute work or authorize a new outcome.

Choose the first project's providers

Ask which working account(s) the user wants: Claude Code, Codex, or both. Reuse an explicit choice and recommend the account they already have working. Use first-project-claude (two Claude), first-project (two Codex), or first-project-mixed (Claude owner, Codex checker). All share the same task and owner/checker culture. Check only selected CLIs/logins; request claude auth login or codex login once when that selected login is missing. No credential copying, unused provider prerequisite or silent model/provider fallback.

Read the compatible getting-started guide's Choose your providers and Kernel startup stays automatic sections before launch. The choice selects the two project agents. Kernel auto-boot independently uses available authenticated accounts, so it may use both even when the project uses one. Do not add an unused-provider login gate or manual kernel setup to this path. An instance-wide provider restriction is a separate request. Preserve an existing kernel and working user rigs.

Show the chosen recipe, resolved runtimes/models and exact rig up <starter> --cwd . --plan / rig up <starter> --cwd . commands. Codex seats retain gpt-6-astra; Claude seats use the configured native default without an OpenRig model override. Confirm that model with the user and its availability; verify the native session's actual model before consequential work. Use the chosen rig name in owner/checker addresses throughout the same task and return.

Establish the working agreement

Read the repository instructions, current work and desired user-visible result. Verify the intended instance, code/work roots, real seat addresses and native readiness. Reuse a suitable small team; an existing agent can bootstrap it. A kernel operator is optional and is not automatically the project owner.

Agree the work boundary, time/spend limit, who answers unresolved choices, and when to stop: checked result, no authorized next work, exhausted budget, or a real user/permission/provider blocker. Background daemon checks are not themselves model turns, but delivered wakes and resumed work can spend tokens. Prefer an event-driven wait to frequent empty reminders. Wakes cannot answer a user question, clear a permission prompt or guarantee progress.

Ask once before launching or assigning work: “Allow your agents to run OpenRig commands without repeated permission prompts?” Yes — recommended / No — keep prompts. Reuse an existing explicit choice for this scope. Explain that this covers all rig verbs, including starting/stopping agents and changing configuration, at personal project scope unless the user explicitly chooses user-wide sessions. It is not global YOLO or permission to invent work. On an actual Yes, follow Applying a permission policy to add existing native rules, preserve stricter/unrelated settings, and verify the target conversation. No or no answer leaves settings alone and continues with existing prompts. Remember the explicit choice and exact additions in the existing onboarding context; “Undo the OpenRig command allowances added by this setup” removes only those additions. Broader access remains a separate opt-in.

Keep purpose, acceptance, decisions and evidence in existing project files. Deliver selected context and obtain each seat's scope reaction; retrieval alone is not peer delivery. The owner carries the candidate through the chosen check and bounded repairs, reports how to try it, and retains the next authorized task or explicitly reports none. Preserve work and custody before a supported stop.

Grow your factory

Choose the team size separately from the coordination method above:

  1. Use the two-agent starter. Keep the existing owner and independent checker while that pair meets the workload. The owner can implement and coordinate.
  2. Add one or two seats to the running rig. This is the usual next step. Follow Grow the running team for rig grow commands, readiness/context/work assignment, and saving the expanded topology. Existing sessions need no rebuild or down/up cycle solely to add capacity.
  3. Author a custom rig when you want a different structure. Read OpenRig Architect, available through rig context get skills/core/openrig-architect/SKILL.md. Request: “Design a user-owned rig for [outcome] using the compatible RigSpec/AgentSpec guidance. Reuse suitable agents, define responsibilities and context, and validate the files. Preserve the existing rig and agree any new launch.”

As independent work grows, the original owner can concentrate on orchestration, multiple builders can implement separate outcomes, and the checker can retain independent review capacity. Record that division explicitly; adding seats does not assign work, change permissions or create parallelism. Agree file/worktree boundaries and integration ownership, follow the project's existing review policy, and keep active concurrency within the user's time/spend budget. Two seats are an entry point, not a finished factory or a maximum.

Read compatible guidance

Before installation, use this file and companion at the same published tag or commit as the selected package. After installation:

sh
rig --version
rig context list --json
rig context show skills/core/openrig-software-factory --json
rig context get skills/core/openrig-software-factory/SKILL.md

Compare build identity as well as version. Preserve missing, unreadable or older recipe results; do not silently substitute newer main or skip a missing companion.

Find the compatible permission guide

Retrieve the maintained procedure with rig context get skills/applying-a-permission-policy/SKILL.md. For source or archive readers, locate it below; the companion getting-started guide contains optional broader launch-mode recipes under Opt-in permissive operation. These paths are relative to the named root, not this skill:

Reading fromProcedure and guide, at the same version as this recipe
Source checkout, including skills/_canonicalBelow the repository root: packages/daemon/assets/plugins/openrig-core/skills/applying-a-permission-policy/SKILL.md and docs/reference/getting-started.md.
npm installationBelow the matching npm root -g or local npm root: @openrig/cli/daemon/assets/plugins/openrig-core/skills/applying-a-permission-policy/SKILL.md and @openrig/cli/daemon/docs/reference/getting-started.md.
Unpacked npm archiveBelow the extraction directory: package/daemon/assets/plugins/openrig-core/skills/applying-a-permission-policy/SKILL.md and package/daemon/docs/reference/getting-started.md.

For installed guidance, use the npm installation that supplies the selected rig executable; another prefix or local project can contain a different version. If the matching guide or section is missing, report the gap before proceeding; do not substitute current main or guidance from another installation.

Request to give your agent

Help me achieve [observable change] in this repository. Read the compatible Software Factory recipe, choose the lightest useful team/queue/Workflow path, and keep the next owner visible. Preserve existing files and permissions. Agree time/spend limits, perform the authorized work and chosen independent check, and ask only about unresolved decisions or effects outside that scope. Keep publication and destructive changes out of this task.

When commands, defaults or permission semantics change, check this source and companion together and regenerate their existing projections. Website guidance should link to the same versioned recipe, not maintain another procedure.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Openrig Software Factory AI skill do?

Use when a user wants a continuing software team for a real repository, or has a first OpenRig team and needs a repeatable path for reviewed work and later tasks.

Why use Openrig Software Factory on TypingMind?

Because you install it once and use it with any model. Openrig Software Factory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openrig Software Factory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/openrig-software-factory. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Openrig Software Factory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openrig Software Factory?

As many as you like. As long as a model supports skills, you can use Openrig Software Factory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openrig Software Factory AI skill free?

Yes. It is published on GitHub by mvschwarz under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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