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Found 1,932 Skills
Create ShinkaEvolve task scaffolds from a target directory and task description, producing `evaluate.py` and `initial.<ext>` (multi-language). Use when asked to set up new ShinkaEvolve tasks, evaluation harnesses, or baseline programs for ShinkaEvolve.
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.
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.
Implement one task or scoped change: make the change, add valuable tests, and verify it works.
WhatsApp Web automation via Playwright and Chrome CDP. Use when the user needs to open WhatsApp Web, launch the WhatsApp Web browser, verify phone numbers on WhatsApp, send WhatsApp messages, read recent chat messages or chat history, read the last reply from a contact, list chats in the sidebar, count chats, count pinned chats, list unread chats, count unread messages, check if a number is registered on WhatsApp, add a new WhatsApp contact, save a number to contacts, automate WhatsApp Web login, or perform bulk number verification. Triggers include requests to "open WhatsApp Web", "buka WhatsApp Web", "launch WhatsApp", "check this number on WhatsApp", "send a WhatsApp message", "verify WhatsApp numbers", "read WhatsApp messages", "list WhatsApp messages", "show recent WhatsApp chat", "ambil pesan WhatsApp", "open WhatsApp chat", "batch check numbers", "list my WhatsApp chats", "ada berapa chat", "berapa pinned chat", "show pinned chats", "ada berapa chat yang belum dibaca", "unread chats", "pesan yang belum dibaca", "how many unread messages", "X bales apa", "apa chat terakhir X", "chat terakhir dari X", "last reply from X", "what did X say", "what did X reply", "add to contacts", "save contact", "add new contact", "simpan kontak", "tambah kontak", "save this number", "pin chat", "unpin chat", "pin this chat", "sematkan chat", "lepas sematan", "pin X", "unpin X", "create group", "new group", "buat grup", "bikin grup baru", "make a whatsapp group", "delete group", "hapus grup", "bubarkan grup", "kick all members", "keluar dan hapus grup", "teardown group", "exit group", "leave group", "keluar grup", "keluar dari grup", "delete chat", "hapus chat", "clear chat", "remove this chat", or any task requiring programmatic WhatsApp Web interaction. For any "open/launch/buka WhatsApp Web" request, run `scripts/login.py` WITHOUT `--wait` — the script exits immediately after opening the window so the agent stays responsive. Never use `--wait` unless the user explicitly asks the agent to wait for them to sign in. For reading messages, run `scripts/read_messages.py --from <name>`. For the last reply from a contact (prompts like "X bales apa"), run `scripts/last_reply.py --from <name>`; add `--any-direction` if the user wants the very last message regardless of who sent it (prompts like "apa chat terakhir X"). For listing chats, run `scripts/list_chats.py`. For pinned chats, run `scripts/list_pinned.py`. For unread chats, run `scripts/list_unread.py`. For adding a contact (prompts like "add this number to contacts", "simpan jadi kontak"), ALWAYS ask the user for First Name, Last Name (optional), and whether to sync the contact to the phone before running `scripts/add_contact.py --phone <number> --first-name <first> [--last-name <last>] [--sync]`. For pinning or unpinning a chat (prompts like "pin chat Ezra", "sematkan chat X", "unpin X"), run `scripts/pin_chat.py --to <name-or-number>` or add `--unpin` to unpin. WhatsApp Web allows at most 3 pinned chats — if the pin action becomes a no-op with `already=true`, tell the user the chat is already pinned; if pinning fails due to the 3-pin cap, tell the user they need to unpin something first. For exiting a group without deleting it from the chat list (prompts like "keluar grup X", "leave group X"), ALWAYS ask the user to confirm first ("Keluar dari grup X? Grup tetap ada di chat list sampai kamu hapus manual."), then run `scripts/exit_group.py --name <group-name> --confirm`. For deleting a chat from the sidebar (prompts like "hapus chat Ezra", "delete chat X", "clear chat"), ALWAYS ask the user to confirm first ("Hapus chat X dari sidebar? Ga bisa di-undo."), then run `scripts/delete_chat.py --to <name-or-number> --confirm`. For active groups you want fully gone, prefer `scripts/delete_group.py` (kick-all + exit + delete) over calling exit + delete-chat separately. For deleting a group (prompts like "hapus grup X", "bubarkan grup"), ALWAYS ask the user to confirm first ("This will kick every member, exit the group, and remove it from your chat list. Lanjut?"). Only after the user confirms, run `scripts/delete_group.py --name <group-name> --confirm`. The script refuses to run without `--confirm`. After it returns, report the `status` field back — "deleted" = fully gone; "exited" = you're out but delete didn't finalize; "partial" = something failed mid-way. Also surface the `skipped` list so the user knows which members couldn't be kicked (usually because the caller isn't admin). For creating a new group (prompts like "buat grup baru", "create a group"), ALWAYS ask the user for the group name AND the members. Members can be many — accept comma-separated input and ask again (repeatably) if the user has more to add, stopping when they signal done. Then run `scripts/create_group.py --name <name> --members <a,b,c> [--members ...]`. After the script returns, check the `failed` array — if any member failed to match a contact, tell the user which ones so they can add them manually later. Always keep responses to the user friendly and non-technical (say "Opening WhatsApp Web..." instead of "Starting Chrome with CDP").
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.
Direct visual and creative work for campaigns, photography, illustration, video, and branded experiences. Use this skill whenever the user wants to brief a photographer, direct illustrators, plan a creative campaign, develop visual concepts, write a creative direction document, or evaluate creative work for fit. Triggers on art direction, photo brief, photography brief, illustration brief, campaign concept, creative concept, visual direction, mood board, look and feel, visual treatment, video direction. Also triggers when the user has approved brand identity but needs to extend it into specific creative deliverables.
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".
This skill leverages SellerSprite's market list selection capability to filter Amazon niche markets based on category dimensions, supporting numerous conditions such as market size, competition intensity, head concentration, seller structure, new product proportion, price/rating/gross margin ranges, etc. It is used to discover accessible markets and evaluate product selection directions. This skill is triggered when users mention Amazon market research, niche category research, market opportunity screening, market concentration analysis, new product opportunities, market selection, SellerSprite market research, or category market research. Even if users do not explicitly mention 'SellerSprite', this skill should be triggered as long as their demand is to filter and evaluate Amazon markets by category dimensions.
Buffett-style single-stock moat diagnostic — "Would Buffett buy this stock?" Five dimensions: business & moat / financial health / management & capital allocation / valuation & margin of safety / long-term visibility. Data from Longbridge CLI first, MCP fallback, WebSearch only for gaps. Runs cross-statement reconciliation (勾稽校验) BEFORE scoring; data-source appendix closes with a one-line reconciliation summary. Output: star-rated radar card, dimension detail, Buffett-voice narrative, mandatory holding-period education block. Triggers: "巴菲特", "护城河", "巴菲特会买吗", "价值投资", "好生意", "宽护城河", "定价权", "诊股", "巴菲特诊股", "巴菲特视角", "长期持有", "護城河", "巴菲特會買嗎", "價值投資", "寬護城河", "定價權", "診股", "巴菲特診股", "巴菲特視角", "長期持有", "Buffett", "Warren Buffett", "moat", "economic moat", "wide moat", "pricing power", "value investing", "owner earnings", "would Buffett buy", "Berkshire-style", "quality compounder".