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: 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/\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
