/algorithm-design
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.
$ npx -y skills add lingzhi227/agent-research-skills --skill algorithm-design --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/algorithm-design
Context preview
The summary Claude sees to decide when to auto-load this skill.
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.
SKILL.md
algorithm-design.SKILL.mdname: 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
1. Define clear inputs and outputs 2. Identify the main loop / recursive structure 3. Specify all parameters and their types 4. 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: batchesStep 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` / `\Ensure` for inputs/outputs
- Keep pseudocode at the right abstraction level — not too detailed, not too vague
Related Skills
- Upstream: [atomic-decomposition](../atomic-decomposition/), [math-reasoning](../math-reasoning/)
- Downstream: [experiment-code](../experiment-code/), [paper-writing-section](../paper-writing-section/)
- See also: [symbolic-equation](../symbolic-equation/)
Read more
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
1. Define clear inputs and outputs 2. Identify the main loop / recursive structure 3. Specify all parameters and their types 4. 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: batchesStep 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` / `\Ensure` for inputs/outputs
- Keep pseudocode at the right abstraction level — not too detailed, not too vague
Related Skills
- Upstream: [atomic-decomposition](../atomic-decomposition/), [math-reasoning](../math-reasoning/)
- Downstream: [experiment-code](../experiment-code/), [paper-writing-section](../paper-writing-section/)
- See also: [symbolic-equation](../symbolic-equation/)
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.
Other skills on agent-research-skills.
- /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.
Open skill - /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.
Open skill - /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.
Open skill - /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.
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

