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Co Math Init

Community
morankor
co-math-init

Initialize a new AI co-mathematician research project. Use when the user wants to start a new math research investigation with AI agent support, or types "/co-math-init", "start a co-math project", "new research project", or similar. Scaffolds a directory with paper.tex, goals.md, decisions.md, workstreams/, and per-project hooks/config that enforce the co-mathematician discipline (no hand-waving, reviewer approvals required for completion).

Overview

Publishermorankor
Repositorytheorist-toolbox
Skill nameco-math-init
Stars
92
Forks
23
Bundled files
6
LicenseMIT
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Co Math Init 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/morankor/theorist-toolbox/tree/master/skills/co-math-init
  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/morankor/theorist-toolbox.git /tmp/theorist-toolbox
mkdir -p .claude/skills
cp -r /tmp/theorist-toolbox/skills/co-math-init .claude/skills/co-math-init
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Co Math Init 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 Co Math Init 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 Co Math Init 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.

co-math-init

Author: Moran Koren, Ben-Gurion University of the Negev (korenmor@bgu.ac.il). Part of the Theorist Toolbox.

Bootstrap a new research project for the personal AI co-mathematician system, modelled after the Google DeepMind AI Co-Mathematician paper (Zheng et al., 2026-05-07).

This skill creates the directory layout, copies the LaTeX template, initializes the per-project hook configuration, and sets up the workstream registry. After init, the user can spawn workstreams via the project-coordinator sub-agent.

When to invoke

Trigger phrases:

  • /co-math-init
  • "start a new co-math project"
  • "init a research project"
  • "new mathematician project"

Steps

1. Gather inputs from the user

Ask via AskUserQuestion:

  1. Project name — short, lowercase-kebab-case (e.g., ambidextrous-sofa). This becomes the directory name.
  2. Initial research question — one sentence. Goes into goals.md. The user can refine later.
  3. Strict mode — strict (default) or pragmatic. Strict means the prover sub-agent must mark every gap with \unproven{...} and never hand-wave. Pragmatic allows informal prose for minor steps. Default is strict unless the user explicitly says otherwise; this default is recorded in user memory and must be respected.
  4. Parent directory — where to create the project. Default: current working directory.

2. Create the directory structure

Inside <parent>/<project-name>/:

paper.tex
goals.md
decisions.md
PROJECT_README.md
co-math-config.json
workstreams/
  .gitkeep
references/
  .gitkeep
failed-explorations/
  .gitkeep
.co-math/
  approvals/
    .gitkeep
  workstream-registry.json
.claude/
  settings.json        # per-project hooks (Phase 3 — empty for now)

3. Populate files from templates

Templates live in this skill's templates/ directory. For each entry below, Read the template, do placeholder substitution, then Write to the project path:

TemplateDestination in project
templates/paper.texpaper.tex
templates/goals.mdgoals.md
templates/decisions.mddecisions.md
templates/PROJECT_README.mdREADME.md
templates/co-math-config.jsonco-math-config.json
templates/claude-settings.json.claude/settings.json

Placeholders to substitute in each file:

PlaceholderReplace with
{{PROJECT_NAME}}the project name
{{RESEARCH_QUESTION}}the user's initial question
{{DATE}}today's date in YYYY-MM-DD
{{STRICT_MODE}}true or false (lowercase, JSON literal)

4. Initialize the workstream registry

Write .co-math/workstream-registry.json:

json
{
  "project": "<project-name>",
  "created": "<DATE>",
  "strict_mode": <true|false>,
  "next_id": 1,
  "workstreams": []
}

5. Print next steps to the user

After scaffolding, output a concise message:

  • Confirm the directory was created (with absolute path).
  • Explain the files: goals.md for refining the question, paper.tex is the living working paper, workstreams/ will fill up as the project-coordinator spawns work.
  • Tell them to invoke the project-coordinator sub-agent (once Phase 2 ships) to start the first workstream, OR — if Phase 2 isn't built yet — to manually edit goals.md and start drafting in paper.tex.
  • Note that hooks are not yet wired up (Phase 3 deliverable).
  • Note the verification ladder available to the coordinator: an informal prover proof (\unproven{} for any gap), or — for results worth machine-checking — a lean-prover workstream that formalises the lemma in Lean 4 and verifies it with lake build, closing the theorem with \leanproved{W{NNN}}. After any proof is APPROVED, a proof-readability pass can polish the exposition without touching the mathematics.

Standard workstream pipeline (including the readability pass)

Every project scaffolded by this skill commits to the following pipeline for any result that is proved. The scaffolding encodes it in co-math-config.json (review_policy.require_readability_pass_after_proof) and documents it in the project README.md; the project-coordinator enforces it.

prover / lean-prover   →   paper-reviewer (correctness/acceptance)
        │                            │
        │                            ▼
        │                  Phase 4.5: readability pass
        │                  W{NNN}-readability-{slug}
        │                  (prover in readability mode, per the
        │                   `proof-readability` skill — exposition only,
        │                   never changes the mathematics)
        │                            │
        │                            ▼
        │                  paper-reviewer re-checks for content
        │                  preservation + plumbing only
        ▼                            ▼
   workstream cannot be marked COMPLETE until the readability
   pass has run and been re-approved

Rules the coordinator must follow:

  • After a prover or lean-prover workstream is APPROVED, dispatch a W{NNN}-readability-{slug} workstream before marking the proving workstream complete. Its instructions.md names exactly the approved theorems/lemmas in scope and points the executing agent at the proof-readability skill.
  • The readability pass is exposition only: it must never strengthen, weaken, or replace any claim. If it surfaces a suspected gap, the workstream returns to prover (it does not patch the math).
  • paper-reviewer re-reviews the readability output for content preservation and plumbing (statement preservation, references resolve, paper compiles) — not for re-proving.
  • A proving workstream's complete state is gated on the readability pass having run and been re-approved. (In strict_mode, this gate is mandatory; in pragmatic mode it may be downgraded to a recommendation, recorded in decisions.md.)

Skip the readability pass only for workstreams that produce no proof (literature surveys, pure computational evidence with no analytic claims).

What this skill does NOT do

  • It does not start any workstream itself. That is the project-coordinator sub-agent's job.
  • It does not invoke any agent. Pure scaffolding.
  • It does not modify global Claude Code settings, only writes a per-project .claude/settings.json.

Notes on respecting prior user decisions

The default strict_mode value is always strict. If the user has previously asked to relax this for a specific project, that is recorded in that project's decisions.md, not as a global default. Each new project starts strict.

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 Co Math Init AI skill do?

Initialize a new AI co-mathematician research project. Use when the user wants to start a new math research investigation with AI agent support, or types "/co-math-init", "start a co-math project", "new research project", or similar. Scaffolds a directory with paper.tex, goals.md, decisions.md, workstreams/, and per-project hooks/config that enforce the co-mathematician discipline (no hand-waving, reviewer approvals required for completion).

Why use Co Math Init on TypingMind?

Because you install it once and use it with any model. Co Math Init 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 Co Math Init in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/morankor/theorist-toolbox/tree/master/skills/co-math-init. 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 Co Math Init?

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 Co Math Init?

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

Is the Co Math Init AI skill free?

Yes. It is published on GitHub by morankor under the MIT 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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