メニュー
Test-driven development workflow (red → green → refactor) for features, bug fixes, and refactors. Use when implementing behavior changes and you want to drive design via tests, prevent regressions, and keep code modular and well-factored.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | test-driven-development |
| description | Test-driven development workflow (red → green → refactor) for features, bug fixes, and refactors. Use when implementing behavior changes and you want to drive design via tests, prevent regressions, and keep code modular and well-factored. |
Write the test first, then write the smallest change that makes it pass, then refactor safely.
references/testing-anti-patterns.mdAutoGPT platform for building, deploying, and running continuous autonomous agents. Use for visual workflow agents, persistent automation, and multi-step AI systems.
LangChain framework for building LLM apps with agents, chains, and RAG across providers. Use for chatbots, tool-calling agents, and retrieval workflows in prototypes or production.
LlamaIndex framework for data-centric LLM apps and RAG. Use for document ingestion, indexing, querying, and building retrieval-driven chatbots or knowledge tools.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Toolkit for styling artifacts with consistent themes (slides, docs, reports, HTML landing pages). Includes 10 preset color/font themes and a workflow for applying or generating a custom theme.