mryk814
Community@mryk814
mryk814 maintains Optimization Compass, a canonical knowledge base of optimization methods, benchmark problems, gallery cases, and comparisons published via GitHub Pages.
Agent Skills by mryk814
Showing 7 vetted skills indexed across 1 GitHub repositories.
mryk814add-comparison
Adds or revises comparison definitions in site_comparisons.json using existing traces and renderer families.
mryk814add-content-article
Adds or improves method and concept articles for canonical optimization entities with validation.
mryk814add-gallery-case
Adds Gallery cases to site_gallery.json using existing canonical IDs with validation.
mryk814add-problem-instance
Adds executable optimization problem instances as paired JSON metadata and Python registry entries.
mryk814optimization-compass-maintenance
Maintains Optimization Compass knowledge, data, and GitHub Pages releases through validated canonical workflows.
mryk814grow-data
Prioritizes knowledge-base gaps and routes content growth work to authoring skills.
mryk814article-style
Standardizes writing style, terminology, and headings for Japanese educational Markdown articles.
Frequently Asked Questions About mryk814
FAQPage SchemaWhat tasks can I perform with mryk814's Optimization Compass skills?▼
You can add or revise comparison definitions, method/concept articles, Gallery cases, and executable problem instances; decide which knowledge to grow next based on coverage and seed gaps; enforce article style rules; and validate, publish, or recover the GitHub Pages site safely.
Who are these Optimization Compass skills designed for?▼
They target maintainers and contributors of the Optimization Compass repository—technical writers and optimization practitioners who curate canonical entities, benchmark problems, gallery examples, and comparisons while preserving canonical identity and passing tiered validation.
How does the authoring and validation workflow operate?▼
Each skill edits specific sources—content/**/*.md, data/seeds JSON files, or problem-suite.json plus problem_registry.py pairs—then runs the matching optimization-compass validate task (content/tier-a, comparison or gallery/tier-b, problem/tier-c) before release.
What prerequisites and constraints apply when using these skills?▼
Contributors must reuse existing canonical IDs, traces, and renderer families, follow the article-style writing rules for method and concept pieces, and never edit generated artifacts directly. The grow-data skill routes gaps to the correct authoring skill and validation task.