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: guideline

英語版ディレクトリ

A comprehensive C code style guide packaged as an AI agent skill with clang-format integration, enabling agents to write, edit, and format C code consistently.

1.3K
Stars
86/100
信頼
カテゴリ: coding-agents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

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

4.7K
Stars
70/100
信頼
カテゴリ: ml-automation監査

Project & code-style guideline of Mobillium iOS Team.

181
Stars
65/100
信頼
カテゴリ: devops監査

A collection of MIT-licensed AI agent skills for food and nutrition science research, including literature review, systematic review, and journal formatting.

16
Stars
65/100
信頼
カテゴリ: research監査

Whole-repo audits in eight modes. Codebase — merged structural + correctness audit, should this exist AND does it do what it promises. Triggers "nuclear review", "code judo", "whole codebase review", "should this exist", "adversarial audit", "fable audit", "correctness audit", "expectation gaps". Docs/Process — doc drift, walkable journeys. Triggers "audit the docs", "doc drift", "process audit", "walk the journeys". Performance — measured-only perf audit; no finding without a number. Triggers "perf audit", "performance audit", "why is it slow", "bundle audit", "build is slow". Threat-model — abuse paths. Triggers "threat model", "STRIDE", "attack surface". Motion — animation audit. Triggers "motion audit", "audit the animations". SEO — discoverability + AEO. Triggers "seo audit", "aeo", "answer engine", "llms.txt", "rank better". Debt — `SHORTCUT:` ledger. Triggers "debt ledger", "shortcut ledger". Owns bare "audit the codebase"; single-page CWV fix loops go to /lighthouse.

42
Stars
60/100
信頼
カテゴリ: security監査

Create a new SigNoz alert rule from a natural-language intent — threshold, anomaly, log-volume, error-rate, latency, or absent-data alerts across metrics, logs, traces, and exceptions. Make sure to use this skill whenever the user says "alert me when…", "notify me if…", "set up monitoring for…", "page me on…", "create an alert for…", or asks for a new alert/notification rule, even if they don't say the word "alert" explicitly. Also use it when someone asks to be notified about error rates, latency spikes, log volume, CPU/memory pressure, or anomalous behavior on a service or host.

15
Stars
65/100
信頼
カテゴリ: data-analysis監査