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Found 2,091 Skills
Use when building Elixir applications that need to evaluate JavaScript or TypeScript code, load ES modules, import npm/jsr packages, call JS functions from Elixir, or use V8 snapshots. Triggers on Denox, deno_core, Rustler NIF JS runtime, TypeScript transpilation in Elixir.
Writing style guide for the Singapore Government Design System (SGDS). Use when writing or reviewing UI copy, documentation, labels, error messages, tooltips, or any content that accompanies SGDS components. Covers tone, grammar, spelling, casing, punctuation, and plain language principles.
Curate Claude Code's auto-memory into durable project knowledge. Analyze MEMORY.md for patterns, promote proven learnings to CLAUDE.md and .claude/rules/, extract recurring solutions into reusable skills. Use when: (1) reviewing what Claude has learned about your project, (2) graduating a pattern from notes to enforced rules, (3) turning a debugging solution into a skill, (4) checking memory health and capacity.
Compliance review and testing: evaluate your application against HIPAA, SOC 2, PCI-DSS, and GDPR technical requirements with browser-based validation and YAML regression tests for continuous compliance.
Analyze whether TikTok or Instagram search traffic is a viable growth channel for your business. Uses ScaleBrick's framework to evaluate demand, competition, content fit, and intent categories. Ends with a go/no-go recommendation.
Fast sprint status check. Reads the current sprint plan, scans story files for status, and produces a concise progress snapshot with burndown assessment and emerging risks. Run at any time during a sprint for quick situational awareness. Use when user asks 'how is the sprint going', 'sprint update', 'show sprint progress'.
Guides rollout configuration for experiments: variant splits, overall rollout percentage, and the critical disambiguation when a user mentions a specific percentage. Covers both initial setup and mid-experiment changes. TRIGGER when: user mentions a rollout percentage, asks about variant splits, wants to change distribution on a running experiment, or asks 'who sees what variant?' DO NOT TRIGGER when: user is asking about metrics, analytics, or experiment results.
A hybrid pattern where the system pauses execution to request human approval, input, or disambiguation before proceeding with critical actions. Use when user asks to "add human approval", "require human review", "human-in-the-loop", or mentions approval workflows, human oversight, or escalation.
This skill is to be used when users explicitly request "migrate LaTeX templates", "integrate old projects into ChineseResearchLaTeX", "apply old bids/papers/graduation theses/resumes to the current template", "organize Word/PDF/Markdown/scattered tex files into existing projects", or directly mention `transfer-old-latex-to-new`. The old alias `migrating-latex-templates` is also supported. This skill only migrates the main content to the content layer of the existing templates in the current repository; it must never modify the source code of public packages in `packages/`, nor modify the template styles or entry skeletons in `projects/`, and can only write to content files allowed to carry the main content in the target project.
Online Novel Topic Planning, suitable for user needs such as "I don't know what to write for a novel", "Help me come up with a novel genre", "Find online novel ideas", "Analyze which genres are popular", "Novel topic evaluation", "Which online novel genres are profitable now", "Give me some novel ideas", "Come up with a golden finger for a novel", "Help me find a popular genre", "Which novel genre is easy to become popular", "Online novel market trend analysis", "Help me plan novel topics", etc. It generates multiple sets of topic proposals and market analysis, including golden finger design, core selling points, cool point patterns, and feasibility evaluation.
Decide when DuckLake is the right MotherDuck storage pattern. Use when evaluating fully managed DuckLake, BYOB, own-compute DuckLake access, data inlining, object-storage layout, or file-aware maintenance instead of native MotherDuck storage.
Machine-learning prediction strategy framework via Longbridge Securities — walk-forward rolling training with feature engineering (MACD, RSI, Bollinger Band width, volume change rate) and a scikit-learn classifier (Random Forest / Gradient Boosting); retrains every 60 days, predicts 5-day direction; buy signal when probability > 0.6, sell when < 0.4; evaluates win rate, profit factor, and Sharpe ratio. Triggers: "机器学习", "ML策略", "预测模型", "随机森林", "梯度提升", "深度学习", "AI选股", "walk-forward", "機器學習", "ML策略", "預測模型", "隨機森林", "梯度提升", "machine learning", "ML strategy", "predictive model", "random forest", "gradient boosting", "AI stock selection", "walk-forward", "rolling training", "feature engineering", "scikit-learn", "XGBoost".