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algorithm-designer

Design statistical algorithms with formal pseudocode, IO specs, and complexity analyses.

Updated Aug 23, 2026
Search Tags:#design#documentation#algorithms#statistics#pseudocode#complexity-analysis
One-click install
npx skills add https://github.com/Data-Wise/scholar --skill algorithm-designer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: algorithm-designer
Source: https://github.com/Data-Wise/scholar/tree/main/src/plugin-api/skills/implementation/algorithm-designer
Command: npx skills add https://github.com/Data-Wise/scholar --skill algorithm-designer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designing complex statistical algorithms is error-prone without standardized documentation. This Skill provides a structured approach to document algorithms with formal inputs/outputs, pseudocode, complexity, and convergence guidelines.

Core Features & Use Cases

  • Pseudocode-first design: Provide language-agnostic pseudocode for new algorithms, ensuring clarity and transferability.
  • Complexity & Convergence: Include time/space complexity and convergence criteria to assess practicality.
  • Implementation blueprints: Supply templates for IO specifications, stability considerations, and reference implementations.

Quick Start

Propose a new estimator and supply a complete pseudocode, IO spec, and a complexity outline.

Frequently Asked Questions about algorithm-designer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write reproducible pseudocode for a statistical estimation algorithm?▼

To write reproducible pseudocode for a statistical estimation algorithm, define language-agnostic steps, formal input/output specifications, and stability considerations using structured documentation templates.

What should be included in statistical algorithm documentation to ensure reproducibility?▼

Statistical algorithm documentation should include formal pseudocode, IO specs, time and space complexity analyses, convergence criteria, and practical implementation notes to ensure full reproducibility.

How do I define convergence criteria for an optimization routine?▼

Define convergence criteria for an optimization routine by specifying tolerance thresholds, maximum iteration limits, and stability considerations within the algorithm's formal documentation and pseudocode design.

Can I use this approach to document complexity analysis for general computational methods?▼

Yes, you can document complexity analysis for general computational methods by applying the provided templates to outline time and space complexity alongside formal pseudocode and IO specifications.

What is the best way to design a new statistical algorithm before writing actual code?▼

The best way to design a new statistical algorithm before coding is to use a pseudocode-first approach, mapping out IO specifications, complexity limits, and convergence guidelines in a structured blueprint.

Why does my statistical algorithm documentation lack transferability across research domains?▼

Statistical algorithm documentation lacks transferability when it omits language-agnostic pseudocode, formal input/output templates, and standardized complexity analyses, which are essential for cross-domain clarity.

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