Atomic Decomposition logo

Atomic Decomposition

Community
lingzhi227
atomic-decomposition

Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.

Overview

Publisherlingzhi227
Repositoryagent-research-skills
Skill nameatomic-decomposition
Stars
426
Forks
46
Bundled files
1
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 lingzhi227 on GitHub. Read the source before you install it.

Installation

Install the Atomic Decomposition 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/lingzhi227/agent-research-skills/tree/main/skills/atomic-decomposition
  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/lingzhi227/agent-research-skills.git /tmp/agent-research-skills
mkdir -p .claude/skills
cp -r /tmp/agent-research-skills/skills/atomic-decomposition .claude/skills/atomic-decomposition
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Atomic Decomposition 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 Atomic Decomposition 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 Atomic Decomposition 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.

Atomic Decomposition

Decompose research ideas into atomic concepts with math formula <-> code implementation mapping.

Input

  • $0 — Research idea, paper, or method description

References

  • Decomposition prompts and workflow: ~/.claude/skills/atomic-decomposition/references/decomposition-prompts.md

Workflow (from AI-Researcher Survey Agent)

Step 1: Break Down into Atomic Definitions

Analyze the research idea and decompose into atomic, self-contained concepts:

  • Each atom should be a single concept
  • Must have clear mathematical foundations
  • Must be implementable in code
  • Must be traceable to specific papers

Step 2: For Each Atomic Definition

A. Paper Survey (Math Formula)
  • Search papers for the mathematical formulation
  • Extract the exact LaTeX formula
  • Note assumptions and constraints
  • Record reference papers
B. Code Survey (Implementation)
  • Search codebases for implementations
  • Extract the corresponding code
  • Note implementation details and variations
  • Record reference repositories
C. Create Knowledge Entry
json
{
  "definition": "Kernelized Gumbel-Softmax Operator",
  "math_formula": "Z = \\text{softmax}((\\log \\pi + g) / \\tau), g \\sim \\text{Gumbel}(0,1)",
  "code_implementation": "def gumbel_softmax(logits, tau=1.0): ...",
  "reference_papers": ["Paper Title 1"],
  "reference_codebases": ["github_user/repo_name"],
  "assumptions": ["Differentiable relaxation of discrete sampling"],
  "connections": ["Used in Component X of the proposed method"]
}

Step 3: Compile Knowledge Base

  • Merge all atomic definitions into a structured knowledge base
  • Verify consistency: every math formula has a code implementation
  • Verify completeness: every code module traces to a formal definition
  • Identify any gaps (formulas without code, or code without theory)

Rules

  • Each atomic definition must be specific enough to trace to concrete formulas and code
  • Do not skip or combine definitions — analyze each separately
  • If unsure about atomicity, err on the side of breaking down further
  • Document breakdown reasoning before analysis
  • Every mathematical concept in the paper must have verified code
  • Every code module must trace back to a formal mathematical definition

Related Skills

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 Atomic Decomposition AI skill do?

Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.

Why use Atomic Decomposition on TypingMind?

Because you install it once and use it with any model. Atomic Decomposition 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 Atomic Decomposition in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lingzhi227/agent-research-skills/tree/main/skills/atomic-decomposition. 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 Atomic Decomposition?

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 Atomic Decomposition?

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

Is the Atomic Decomposition AI skill free?

It is published on GitHub by lingzhi227. Check the repository for licensing terms. 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 👇