algorithm-design

SKILL.md

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 / \Ensure for 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

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