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Found 2,819 Skills
Submit a kid-facing English content PR to the football-english repo (zxkane/football-english). Use this whenever the user asks to "submit this week's quiz", "publish a weekly quiz", "submit a daily reading", "add today's reading", "publish a daily article", "open a daily PR", "open a quiz PR", or hands over essay paragraphs (with optional KET/PET points, vocabulary, audio, image, questions) and expects them to land in the kid-facing site. The repo publishes two kinds of content — weekly quizzes (essay + 3-20 graded questions, one per ISO week) and daily readings (essay + TTS audio + image + structured KET/PET points + optional 0-3 small questions, irregular cadence). This skill picks the right shape, writes the JSON, validates it, and opens the PR end-to-end. Trigger this skill even if the user doesn't use the words "quiz", "daily", or "PR" — any handover of essay paragraphs intended for this repo qualifies.
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.
INVOKE THIS SKILL when auditing an AI agent or LLM app for regulatory compliance. Covers EU AI Act, GPAI Code of Practice, GDPR, NIST AI RMF, Colorado AI Act, HIPAA, and ISO 42001. Scans the codebase for compliance gaps, cross-references Arize instrumentation for audit trail coverage, and produces an actionable remediation checklist tailored to the selected frameworks.
Graham cigar-butt batch screener — runs Benjamin Graham's NCAV / net-net / defensive-investor hard filters across an index or market universe and returns a ranked candidate list with NCAV ratio, PE, PB, dividend yield, debt coverage, 5y earnings stability, Graham buy price, and a dynamic value-trap warning. Longbridge CLI/MCP first; WebSearch fills genuine gaps (PMI, sector outlook). Every figure footnoted to its source. Auto-switches model for banks / insurance / REITs and flags <2y IPOs and suspended names. Triggers: "格雷厄姆筛选", "格雷厄姆选股", "捡烟蒂榜单", "烟蒂股榜", "NCAV筛选", "NCAV排行榜", "净流动资产筛选", "防御型投资者选股", "撿煙蒂榜單", "煙蒂股榜", "NCAV篩選", "淨流動資產篩選", "防禦型投資者選股", "Graham screen", "Graham screener", "NCAV screen", "net-net screen", "net-net list", "cigar-butt screen", "defensive investor screen", "liquidation value screen", "Benjamin Graham screen".
Update Margin Dashboard with Fidelity balance data and calculate margin-living strategy metrics. Monitors margin balance, interest costs, coverage ratios, and scaling thresholds. Triggers safety alerts for large draws and provides time-based scaling recommendations. Use when updating margin, balances, coverage ratio, or margin strategy analysis.
Plan an Israeli wedding from engagement to chuppah, covering venue selection (ulmot, ganot aruim), vendor comparison via Israeli platforms (Celebrate, Engaged, Save A Date, Walla Wedding), budget planning (~100-140K NIS average), Rabbinate registration (tik nisuin, teudat ravakut), halachic requirements (mikveh, ketuba), guest management, per-plate cost optimization, seasonal pricing, and timeline creation. Use when user asks about "chatuna b'yisrael", Israeli wedding planning, wedding budget, "ulam aruim", "ulmot", "ganim", wedding vendors, Rabbinate requirements, "tik nisuin", ketuba, or wedding timeline. Prevents common mistakes like missing Rabbinate deadlines, overpaying on Thursday weddings, or forgetting AKUM fees. Do NOT use for destination weddings abroad, non-Jewish religious ceremonies, or divorce proceedings.
Desktop automation CLI for AI agents (macOS, Linux, Windows). Screenshot, click, type, scroll, drag with native Zig backend. Use this skill when automating desktop apps with computer use models (GPT-5.4, Claude). Covers the screenshot-action feedback loop, coord-map workflow, window-scoped screenshots, and system prompts for accurate clicking.
Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Triggers: 'prior art search for [invention]', 'patent search on [topic]', 'freedom to operate analysis', 'FTO for [product]', 'patent landscape for [field]', 'is [invention] novel', 'patents on [topic]', 'competitive patent analysis', 'prior art for litigation', 'patent diligence on [company]'. Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.
Use major AI models (Claude, ChatGPT, Gemini, DeepSeek, Qwen, etc.) without API tokens by leveraging browser authentication instead of paid API keys
Find a specific CRM record by ID, email, domain, or name fragment, and traverse associations for the full account picture.
Deploy, operate, and integrate the VSS 3.2 GA RT-Embed Video Embedding microservice. Covers Docker Compose bring-up, GPU and storage prerequisites, the `/v1` REST API (file uploads, text and video embeddings, live RTSP streams, health and metrics), Redis/Kafka/OTel integration, common failure modes, and teardown.
How agentmemory is built, the iii engine primitives it runs on, its storage model, ports, and the viewer. Use when reasoning about how memory is stored or retrieved end to end, when extending the system, or when answering how agentmemory works under the hood.