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/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.

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agent-research-skills
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$ npx -y skills add lingzhi227/agent-research-skills --skill algorithm-design --agent claude-code

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  • 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

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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.md
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: 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` / `\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
Ships withagent-research-skills

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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