/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.
$ npx -y skills add lingzhi227/agent-research-skills --skill atomic-decomposition --agent claude-codeHow it fires
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/atomic-decomposition
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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.
SKILL.md
atomic-decomposition.SKILL.mdname: atomic-decomposition
description: 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.
argument-hint: [idea-or-paper]
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
{
"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
- Upstream: [research-planning](../research-planning/), [idea-generation](../idea-generation/)
- Downstream: [experiment-code](../experiment-code/), [algorithm-design](../algorithm-design/)
- See also: [math-reasoning](../math-reasoning/)
Read more
name: atomic-decomposition description: 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. argument-hint: [idea-or-paper]
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
{
"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
- Upstream: [research-planning](../research-planning/), [idea-generation](../idea-generation/)
- Downstream: [experiment-code](../experiment-code/), [algorithm-design](../algorithm-design/)
- See also: [math-reasoning](../math-reasoning/)
31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.
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Open skill - /code-debugging
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Open skill - /data-analysis
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Open skill - /deep-research
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
Open skill

