Specification System

CommunityPopular

mvschwarz

specification-system

Use when authoring rig specs, agent specs, workflow specs, startup/context fragments, operating-mode declarations, or designing the user spec library. Covers the 4 failure modes (spec instantiates topology but not workflow/mode; spec depends on local paths and fails on another host; agents modify specs as one-off files instead of preserving reusable intent; validation proves YAML shape but not whether topology can run) and the validation-vs-runtime-realization distinction.

Overview

Publisher	mvschwarz
Repository	`openrig`
Skill name	`specification-system`
Stars	5K
Forks	366
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by mvschwarz on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/specification-system
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/specification-system

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/specification-system .claude/skills/specification-system

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Specification System 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 Specification System 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 Specification System 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.

Specification System

The declarative primitive family for OpenRig intent: rig specs, agent specs, workflow specs, startup/context fragments, operating-mode declarations, and the user spec library that stores and reuses them.

Specs are how humans and agents describe repeatable topology and behavior without re-explaining it in chat. They are also shareable artifacts: a user should be able to publish a spec or spec family so another user can instantiate the same rig shape, role structure, or workflow pattern.

Without a dependable spec primitive, OpenRig depends on manual startup prompts and tribal memory. That blocks repeatability, product demos, rig bundles, and autonomous rig construction.

Use this when

Authoring a RigSpec / AgentSpec / workflow spec
Designing a startup/context fragment
Reasoning about spec-library lifecycle (validation, sharing, upgrade semantics)
Auditing a spec for portability (does it run on another host?)
Distinguishing spec vs bundle vs extension cleanly

Don't use this when

The work is one-off and won't be reused. Manual rig assembly is fine for one-shot work.
The intent is to package a topology + workflow as a shareable artifact. That's rig-bundles-and-shareable-artifacts.
The intent is to add runtime behavior. That's extension-and-user-workspace.

Failure modes (4)

A spec can instantiate a topology but not the workflow or operating mode needed to use it. Topology is necessary but not sufficient — workflow and operating mode must be declared too.
A shared spec depends on local paths or hidden startup fragments and fails on another host. Specs must be self-describing for portability.
Agents modify specs as one-off files instead of preserving reusable user/library intent. Specs are reusable; treating each instance as one-off destroys the primitive's value.
Validation proves YAML shape but not whether the declared topology can actually run. Structural validation is not enough; runtime realization is the real proof.

Proof standard

Proof should:

Author a spec
Validate it (structural)
Install it (into spec library)
Instantiate it on a clean OpenRig environment
Show both structural validation AND runtime realization

Validation alone is insufficient.

Spec / bundle / extension boundary

Concept	Declares	Example
Spec	Topology / role / workflow shape (declarative intent)	`rig.yaml`, `agent.yaml`, `workflow.yaml`
Bundle	Spec(s) + supporting fragments packaged for shareable instantiation	A Velocity Team bundle
Extension	Runtime behavior added to user workspace	RigX command, custom view

Don't conflate them. The contract should distinguish spec, bundle, and extension cleanly.

Currently shipped surfaces

OpenRig already has:

RigSpec / AgentSpec authoring (agent.yaml, rig.yaml formats; see openrig-architect skill)
Workflow specs (markdown/YAML files, daemon read-through cache via workflow_specs table; see workflow-runtime skill)
Bundle/spec command surface (rig bundle / spec / agent / specs ls/show/preview/add/sync/remove/rename)
Spec library (filesystem-backed at packages/daemon/specs/ + ~/.openrig/specs/ per cli-reference.md)

Not yet shipped:

Spec library lifecycle (validation, sharing, upgrade) treated as a first-class primitive
Cross-host spec sharing
Marketplace / public registry

Frequently asked questions

What does the Specification System AI skill do?

Why use Specification System on TypingMind?

Because you install it once and use it with any model. Specification System 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 Specification System 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/specification-system. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Specification System?

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 Specification System?

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

Is the Specification System 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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Specification System

Overview