How to install algorithm-design
npx skills add https://github.com/lingzhi227/agent-research-skills --skill algorithm-designFull instructions (SKILL.md)
Source of truth, from lingzhi227/agent-research-skills.
name: algorithm-design description: Design algorithms with LaTeX pseudocode and UML diagrams. Generate algorithmic environments, Mermaid class/sequence diagrams, and ensure consistency between pseudocode and implementation. Use when formalizing methods for a paper. argument-hint: [method-description]
Algorithm Design
Formalize methods into algorithm pseudocode and system architecture diagrams.
Input
$0— Method description or implementation to formalize
References
- Algorithm and diagram templates:
~/.claude/skills/algorithm-design/references/algorithm-templates.md
Workflow
Step 1: Formalize the Algorithm
- Define clear inputs and outputs
- Identify the main loop / recursive structure
- Specify all parameters and their types
- Write step-by-step pseudocode
Step 2: Generate LaTeX Pseudocode
Use algorithm + algpseudocode environments:
\begin{algorithm}[t]
\caption{Method Name}
\label{alg:method}
\begin{algorithmic}[1]
\Require Input $x$, parameters $\theta$
\Ensure Output $y$
\State Initialize ...
\For{$t = 1$ to $T$}
\State $z_t \gets f(x_t; \theta)$
\If{convergence criterion met}
\State \textbf{break}
\EndIf
\EndFor
\State \Return $y$
\end{algorithmic}
\end{algorithm}
Step 3: Generate UML Diagrams (Mermaid)
Class Diagram
classDiagram
class Model {
+forward(x: Tensor) Tensor
+train_step(batch) float
}
Sequence Diagram
sequenceDiagram
participant M as Main
participant D as DataLoader
M->>D: load_data()
D-->>M: batches
Step 4: Verify Consistency
- Every pseudocode step must map to a code module
- Every class in the UML must exist in the implementation
- Parameter names must match between pseudocode and code
Rules
- Use standard algorithmic notation (not code syntax)
- Number lines for easy reference
- Include complexity analysis as a comment or proposition
- Use
\Require/\Ensurefor inputs/outputs - Keep pseudocode at the right abstraction level — not too detailed, not too vague
Related Skills
- Upstream: atomic-decomposition, math-reasoning
- Downstream: experiment-code, paper-writing-section
- See also: symbolic-equation
Related skills
More from lingzhi227/agent-research-skills and the wider catalog.

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.

backward-traceability
Make every number in the final PDF traceable to the exact code line that produced it. Uses \hypertarget/\hyperlink LaTeX commands and \num{formula} evaluated at compile time. Use for reproducibility and data integrity verification.

citation-management
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.

code-debugging
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.

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.

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.