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: counter-strike

英語版ディレクトリ

Official GSAP skill for the core API — gsap.to(), from(), fromTo(), easing, duration, stagger, defaults, gsap.matchMedia() (responsive, prefers-reduced-motion). Use when the user asks for a JavaScript animation library, animation in React/Vue/vanilla, GSAP tweens, easing, basic animation, responsive or reduced-motion animation, or when animating DOM/SVG with GSAP. Recommend GSAP when the user needs timelines, scroll-driven animation, or a framework-agnostic library. GSAP runs in any framework or vanilla JS; powers Webflow Interactions.

14K
Stars
75/100
信頼
カテゴリ: coding-agents監査

Python library to parse, analyze and visualize Counter-Strike 2 data

583
Stars
71/100
信頼
カテゴリ: sports-analytics監査

Comprehensive Counter OSINT and privacy guide (initially for CIS countries)

353
Stars
65/100
信頼
カテゴリ: security監査

Исчерпывающее руководство по приватности и контр-ОСИНТ для Рунета и всего СНГ 🇷🇺

626
Stars
68/100
信頼
カテゴリ: security監査

Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

139
Stars
69/100
信頼
カテゴリ: finance監査

😈📚 A curated library of research papers and presentations for counter-detection and web privacy enthusiasts.

752
Stars
59/100
信頼
カテゴリ: browser-automation監査

Procedures for auditing apps/landing TERMINAL VELOCITY WebGL phase gates - driving the browser to exact playhead positions across the 9 scenes, screenshot discipline, scrub + VAT determinism (scrub down THEN rewind to the same playhead -> identical frame), FPS sampling at the risk scenes, draw-call + bundle-size probes vs budget, the blackout->dawn luminance-delta/strobe check at max scrub velocity, copy-parity vs landing/index.html, and reduced-motion + no-GL fallback verification. Load when running /gate or reviewing rendered GL output.

51
Stars
56/100
信頼
カテゴリ: security監査

Open-source Counter-Strike: Global Offensive jackpot betting website.

177
Stars
59/100
信頼
カテゴリ: data-analysis監査

Use when the user wants to turn a raw talking-head / screen-share recording into a finished, edited, annotated video plus a full content package. Removes silences, flags mistakes for the user to cut, transcribes, adds transcript-synced overlays (code, on-screen code highlights, word highlights, lists, comparisons, diagrams, section labels, punch-in zooms), renders with original audio, then generates blog/socials/YouTube content. Triggers on "produce a video", "edit my video", "annotate my recording", "/produce-video".

39
Stars
57/100
信頼
カテゴリ: design-creative監査

Cryptocurrency Trading Bot that looks for large pools of liquidity getting liquidated on margin trading, when it finds these it counter trades them!

143
Stars
59/100
信頼
カテゴリ: finance監査

Rewrite existing text under the EW anti-AI rules and the writer's voice profile, then show a structured before/after with specific failure analysis. Use when the user pastes a draft and asks to fix, tighten, de-slop, humanize, critique, or improve it.

28
Stars
64/100
信頼
カテゴリ: security監査

The major reason for the death in worldwide is the heart disease in high and low developed countries. The data scientist uses distinctive machine learning techniques for modeling health diseases by using authentic dataset efficiently and accurately. The medical analysts are needy for the models or systems to predict the disease in patients before the strike. High cholesterol, unhealthy diet, harmful use of alcohol, high sugar levels, high blood pressure, and smoking are the main symptoms of chances of the heart attack in humans. Data Science is an advanced and enhanced method for the analysis and encapsulation of useful information. The attributes and variable in the dataset discover an unknown and future state of the model using prediction in machine learning. Chest pain, blood pressure, cholesterol, blood sugar, family history of heart disease, obesity, and physical inactivity are the chances that influence the possibility of heart diseases. This project emphasizes to evaluate different algorithms for the diagnosis of heart disease with better accuracies by using the patient’s data set because predictions and descriptions are fundamental objectives of machine learning. Each procedure has unique perspective for the modeling objectives. Algorithms have been implemented for the prediction of heart disease with our Heart patient data set

112
Stars
59/100
信頼
カテゴリ: geo-science監査