Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: intermediate

英語版ディレクトリ

Docker - Beginners | Intermediate | Advanced

8.0K
Stars
77/100
信頼
カテゴリ: devops監査

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.

34K
Stars
67/100
信頼
カテゴリ: research監査

Machine Learning Journal for Intermediate to Advanced Topics.

2.4K
Stars
71/100
信頼
カテゴリ: ml-automation監査

cairo-vm is a Rust implementation of the Cairo VM. Cairo (CPU Algebraic Intermediate Representation) is a programming language for writing provable programs, where one party can prove to another that a certain computation was executed correctly without the need for this party to re-execute the same program.

585
Stars
73/100
信頼
カテゴリ: web3-analytics監査

📚 This guide is designed to help you learn UI/UX design, and is divided into three levels: Beginner, Intermediate, and Expert. It includes learning resource, guides and tools that cover all aspects of designing user interfaces and user experiences.

1.4K
Stars
73/100
信頼
カテゴリ: design-creative監査

test on Windows, enterprise CA, corporate certificate, GPO cert, TLS fetch failed, Windows sandbox, daytona windows, self-hosted cert. Use when validating iPolloWork Windows enterprise TLS/OS-trust fixes in a Daytona Windows sandbox.

4.5K
Stars
66/100
信頼
カテゴリ: coding-agents監査

Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while `samber/cc-skills-golang@golang-performance` provides the optimization patterns.

3.0K
Stars
67/100
信頼
カテゴリ: research監査

YASA is an open-source static program analysis project. Its core innovation lies in a unified intermediate representation called UAST, designed to support multiple programming languages. Built on top of UAST, YASA provides a highly accurate static analysis framework.

290
Stars
69/100
信頼
カテゴリ: security監査

Every other tool executes. This one decides what to execute. Chief of Staff skill for Claude Code.

24
Stars
63/100
信頼
カテゴリ: utility監査

Terraform - Beginners | Intermediate | Advanced

423
Stars
57/100
信頼
カテゴリ: devops監査

Create visual identity systems: color palettes, font pairings, style direction, and rendered preview PDFs. Use when the user needs a cohesive design system before building a deck, presentation, document, or any visual project. Works for consulting engagements, product pitches, personal brands, or any context requiring a unified visual language. Produces a markdown design brief, a JS config, and a preview PDF.

54
Stars
56/100
信頼
カテゴリ: design-creative監査

Use when the user wants to deeply learn a new topic from scratch. Runs a pre-interview (current knowledge, end-goal proficiency, depth, practice load, background, scope), researches online (articles, niche-influencer blogs, canonical docs, subtopic landscape), then produces a structured markdown course with mandatory visual diagrams, evidence-based learning-science features (retrieval practice, spaced callbacks, worked-example fading, concept ledger, jargon gate, analogy hygiene), and a self-contained interactive HTML mini-course. Triggers on /teach-me, "teach me about X", "I want to learn X", "deep dive on X", "create a course on X", "study X with me".

39
Stars
61/100
信頼
カテゴリ: research監査