Spec Graph logo

Spec Graph

Organization
JetBrains
spec-graph

Use when locating, reading, creating, updating, or validating project specs, or when work is governed by or may alter a documented boundary, contract, invariant, behavior, or architecture decision.

Overview

PublisherJetBrains
Repositorythinkrail
Skill namespec-graph
Stars
514
Forks
42
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Spec Graph 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/JetBrains/thinkrail/tree/main/packages/spec-graph/skills/spec-graph
  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/JetBrains/thinkrail.git /tmp/thinkrail
mkdir -p .claude/skills
cp -r /tmp/thinkrail/packages/spec-graph/skills/spec-graph .claude/skills/spec-graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec Graph 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 Spec Graph 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 Spec Graph 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.

Spec graph

Specs are the ground truth

  • Specs describe the architecture, decisions, contracts, and boundaries behind the code — the intent that the code alone does not reveal. Treat them as authoritative.
  • Consult the owning spec when it governs the work. When work depends on, checks, explains, or may alter a documented boundary, contract, invariant, behavior, or architecture decision, use spec_grep / spec_get / spec_graph to find the applicable record and align with it. If a change contradicts a recorded decision, surface and reconcile the contradiction rather than silently diverging.
  • Keep lookup proportional. Specs are the map for decisions and boundaries; code confirms the implementation. A localized fix, explanation, or check that changes no documented decision may inspect only the files it needs without reading unrelated specs.
  • Keep them honest. A change that moves or blurs a boundary, or overturns a decision, updates the spec as part of the same change. Specs that drift from the code stop being ground truth.

What a spec is

  • A durable, declarative document. It states the world as it is — the intent, decisions, contracts, and boundaries behind the code — not plans, tasks, phases, or a work journey.
  • Concise and readable. It captures what is not obvious from the code; it never restates the code.
  • The bar: reading the relevant specs should be enough to understand an area and to formulate a task to improve it.

Keep specs lean

  • Explain intent, not inventory. Describe what a module is for, what it owns, and where its boundaries are — not a file-by-file transcript of its directory. The reader can see the files; the spec exists for what the files don't say.
  • Record the edges that matter. State the module's boundary (allowed / forbidden deps) and the dependency edges between its sub-modules. List a part only when its role or its edges aren't obvious from its name — e.g. a small table that carries a real dependency DAG earns its place; a table that just pairs foo.ts with "the foo tool" is noise, so say it in a sentence instead.
  • Say each thing once. A fact lives in exactly one spec; others link to it by id rather than restate it. If a paragraph is being copied between specs, move it to the spec that owns the concept and point at it. Duplicated prose drifts and turns into contradictions.
  • Prefer prose to exhaustive tables, and cut anything that only paraphrases code, filenames, or a sibling spec.

The graph

  • parent links form a hierarchy that mirrors the code structure: a SPEC.md sits beside the module it describes (fractal — a package and its sub-directories each have one), and root documents sit at the repository root.
  • depends-on, references, and implements form a dependency layer across the tree.

Frontmatter

  • Required: id (a unique slug), type, title.
  • Optional: status (lifecycle), parent (single link), depends-on / references / implements (link lists), covers, tags.
  • A file is a spec when its frontmatter carries id and type.
  • status tracks a spec's lifecycle: draft (being written) → active (in force), then stale (drifting from the code), done, or deprecated. It's optional, but keep it current as a spec firms up or ages.
  • Types:
    • goal-and-requirements — the product goal and scope; the root of the graph.
    • architecture-design — system-wide topology, cross-cutting decisions, and invariants.
    • module-design — a package or module's responsibility and boundary.
    • submodule-design — the same, for a directory-level module inside a package.
    • task-spec — a temporary working document for a piece of work; not durable, and removed once the work lands.

Tools

Read:

  • spec_grep — search within specs (content, narrowed by metadata filters).
  • spec_get — a spec's frontmatter, its resolved links, and its path. Read the body with the normal read tool using that path.
  • spec_graph — a bounded slice of the graph: a subtree, ancestors, or a node's neighbors, to a depth.

Manage:

  • spec_create — a new spec with scaffolded frontmatter and headings.
  • spec_update — a spec's frontmatter (fields and links). It does not touch the body.
  • spec_delete — remove a spec.
  • spec_validate — report dangling links, duplicate ids, and parent cycles.

Prose is written and edited with the normal write/edit tools; the spec tools own frontmatter and structure.

Working with specs

  1. Orient when specs govern the work. From a known root or the module you are touching, use spec_graph for the neighborhood, spec_get for a node's metadata, and read for its body. Use spec_grep to find specs by content.
  2. Align. Reconcile the change with the decisions and contracts the specs record; surface contradictions before diverging.
  3. Update. When the change alters a boundary, contract, or decision, update the spec — frontmatter (including status) with spec_update, prose with edit — and add spec_create for a new module.
  4. Check. Run spec_validate after structural changes.

Frequently asked questions

What does the Spec Graph AI skill do?

Use when locating, reading, creating, updating, or validating project specs, or when work is governed by or may alter a documented boundary, contract, invariant, behavior, or architecture decision.

Why use Spec Graph on TypingMind?

Because you install it once and use it with any model. Spec Graph 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 Spec Graph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JetBrains/thinkrail/tree/main/packages/spec-graph/skills/spec-graph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Spec Graph?

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 Spec Graph?

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

Is the Spec Graph AI skill free?

Yes. It is published on GitHub by JetBrains 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇