Code analysis

Code Analysis

Analyze a project's source code to produce a publication-ready methods section, algorithm pseudocode, and reproducibility documentation.

Model Routing

This skill executes inside the code-agent subagent via its frontmatter (context: fork, agent: code-agent), which is pinned to Sonnet. The fork receives only the task description and the repository, not the whole conversation, so name the codebase paths to analyze and the target manuscript section explicitly in the task.

Workflow

1. Identify scope: ask user which directories/files to analyze, or default to the project root (excluding manuscript/, node_modules/, .git/, venv/)
2. Run codebase analyzer: execute scripts/codebase-analyzer.py on the target directory to extract structure metadata
3. Map code to paper sections: identify which code components correspond to which manuscript sections
4. Generate outputs: methods text, pseudocode blocks, complexity analysis, dependency documentation

Code-to-Paper Mapping

Code Artifact	Paper Section	Output
Data loading / preprocessing	Methods: Data	Description of pipeline, transformations
Model / algorithm definition	Methods: Approach	Pseudocode + prose description
Training / optimization loop	Methods: Training	Hyperparameters, optimization details
Evaluation scripts	Methods: Evaluation	Metrics, test protocols
Config files / CLI args	Methods: Experimental Setup	Hyperparameter tables
Test suites / benchmarks	Results	Evaluation metrics, baseline comparisons

Analysis Dimensions

Algorithms and Data Structures

• Identify core algorithms (sorting, graph traversal, optimization, neural architectures)
• Document data structures and their roles
• Extract computational complexity (time and space) for key operations
• Note any approximations or heuristics

Dependencies and Frameworks

• Parse requirements.txt, pyproject.toml, package.json, Cargo.toml, environment.yml
• List frameworks with versions (e.g., PyTorch 2.1, scikit-learn 1.4)
• Identify hardware requirements (GPU, memory) from code hints

Hyperparameters and Configuration

• Extract all configurable parameters from config files, argparse definitions, or constants
• Organize into a LaTeX table with: parameter name, value, description
• Flag hardcoded values that should be documented

Data Preprocessing

• Trace the data pipeline from raw input to model-ready format
• Document transformations: normalization, augmentation, tokenization, feature engineering
• Note dataset splits and sampling strategies

Evaluation Metrics

• Identify all metrics computed in evaluation code (accuracy, F1, BLEU, RMSE, etc.)
• Document how each metric is calculated
• Extract baseline comparison logic for results section

Output: Methods Section

Generate LaTeX for manuscript/methods.tex with appropriate subsections:

\section{Methods}

\subsection{Data}
% Data collection, preprocessing, splits

\subsection{Proposed Approach}
% Core algorithm/model description with pseudocode

\subsection{Experimental Setup}
% Hardware, software, hyperparameters

\subsection{Evaluation}
% Metrics and evaluation protocol

Pseudocode Generation

Generate algorithm blocks using algorithm2e (default) or algorithmicx (on request):

\begin{algorithm}[H]
\SetAlgoLined
\KwIn{Input description}
\KwOut{Output description}
Initialization\;
\While{convergence criterion not met}{
  Step 1\;
  Step 2\;
  \eIf{condition}{
    Action A\;
  }{
    Action B\;
  }
}
\caption{Algorithm Name}
\label{alg:name}
\end{algorithm}

Write pseudocode at the appropriate abstraction level for a methods section: high enough to convey the approach without language-specific syntax, detailed enough to reproduce the work.

Hyperparameter Table

Generate a booktabs-style table in manuscript/tables/hyperparameters.tex:

\begin{table}[h]
\centering
\caption{Hyperparameters used in experiments.}
\label{tab:hyperparams}
\begin{tabular}{@{}llr@{}}
\toprule
Parameter & Description & Value \\
\midrule
Learning rate & Adam optimizer & 3e-4 \\
Batch size & Training & 64 \\
...
\bottomrule
\end{tabular}
\end{table}

Reproducibility Checklist

After analysis, verify and report:

• All hyperparameters documented
• Random seeds identified and recorded
• Software versions captured
• Hardware requirements noted
• Data preprocessing steps fully described
• Evaluation protocol specified
• Code availability statement drafted

Related Skills

• implementation: generates code from research specifications (inverse of this skill)
• paper-drafting: consumes the methods section output
• latex-tables: formats hyperparameter and results tables
• figure-suggestions: suggests architecture diagrams based on code structure