TulipaEnergyModel.jl
TulipaEnergy/TulipaEnergyModel.jl
An AI agent configuration and developer guide for TulipaEnergyModel.jl, a Julia package for energy system modeling and optimization.
79 37Apache-2.0Updated 2026-08-12
81
GreatOverview
This skill guide provides strict architectural guidelines, performance optimization patterns, style conventions, and testing procedures for developers and AI coding assistants working on the TulipaEnergyModel.jl optimization framework. It details the pipeline flow, data management with DuckDB, model generation via JuMP, and specific instructions for executing tests using TestItemRunner and the Julia Model Context Protocol (MCP).
Capabilities
- ▸Provides codebase layout and pipeline execution steps (high-level and low-level)
- ▸Enforces Julia performance anti-pattern prevention (type instability, allocations, untyped containers)
- ▸Defines code formatting, naming, documentation (Diataxis), and commit message conventions
- ▸Outlines testing instructions with TestItemRunner and tag-based filtering
Best for
Guiding AI coding assistants through TulipaEnergyModel.jl codebase architecture and conventions, Writing and optimizing high-performance Julia code for energy systems modeling, Running targeted package tests using TestItemRunner and Julia MCP, Executing structured data schema migrations using Python and DuckDB scripts
Works with
Claude CodeGitHub CopilotCodex
Evoa Score breakdown= Σ (score × weight)
81Task usefulness20%77 → +15.4
実タスクにどれだけ役立つか（機能の豊富さ・用途の明確さ）。 — AIによるcapabilities/use-cases解析
Code quality15%95 → +14.3
実装・指示の品質。 — AIによるSKILL.md/README解析
Maintenance15%100 → +15.0
リポジトリがどれだけ活発に保守されているか。 — GitHub 最終push日時の新しさ
Documentation12%37 → +4.4
ドキュメントの充実度・分かりやすさ。 — README/独自要約の情報量
Security15%100 → +15.0
危険・不審な挙動が無いか。 — AIによるセキュリティレビュー
Originality10%85 → +8.5
ありふれたラッパーではない独自性。 — AIによる独自性判定
Popularity8%55 → +4.4
コミュニティの採用度。 — GitHub Stars/Forks（対数スケール）
Compatibility5%75 → +3.8
対応AIエージェントの広さ。 — AIによる対応エージェント判定
Weighted total80.7 / 100
ライセンス不明/制限あり（Red）のSkillは総合スコアに0.85倍の補正を適用します。 ランキングはこのScoreのみで決まり、広告で変わりません。 算出方法の詳細 →
Security considerations
The skill guidelines emphasize proper input validation and safe code execution, but running arbitrary optimization models and database operations via DuckDB should be performed within trusted environments.
Categories
Summary and analysis are original content generated by AI Skills Rank. The skill's source text is not reproduced here — view it on the linked repository.