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Found 2,505 Skills
Population genetics research using the 1000 Genomes Project (IGSR) -- search populations by superpopulation ancestry (AFR, AMR, EAS, EUR, SAS), retrieve samples by population code, list available data collections, and integrate with GWAS tools for population stratification analysis. Use when users ask about 1000 Genomes populations, sample ancestry, allele frequency variation across continental groups, population-specific GWAS interpretation, or IGSR data collections like the 30x high-coverage resequencing or HGSVC.
This skill should be used when completing LBO (Leveraged Buyout) model templates in Excel for private equity transactions, deal materials, or investment committee presentations. The skill fills in formulas, validates calculations, and ensures professional formatting standards that adapt to any template structure.
Use when writing, fixing, editing, or refactoring React tests with Testing Library, user-event, component rendering, accessibility queries, async UI, mocks, brittle fixtures, test data builders, or behavior coverage.
Leverage the market statistics capability of SellerSprite to output a market statistics dashboard by category node, including metrics such as average rating, average price, BSR, sales volume, number of sellers, and new product-related indicators for top Listings. It is suitable for quickly judging the market quality and competitive landscape of a certain category. This skill is triggered when the user mentions category market statistics, market selection dashboard, market foundation assessment, node market quality, top product statistics, SellerSprite market statistics, or category statistics. Even if the user does not explicitly mention "SellerSprite", this skill should be triggered as long as the requirement is to view aggregated statistical results by category node.
Static basic information for all Longbridge-tradable securities — stocks, ETFs, options, warrants: company name, listing date, exchange, industry classification, total shares, circulating shares, market cap, IPO price, website, address. Futures / bonds / funds have limited coverage. Triggers: "基础信息", "股票信息", "上市日期", "总股本", "流通股", "IPO价格", "标的信息", "品种信息", "基礎信息", "股票資料", "上市日期", "總股本", "流通股", "IPO價格", "基本資料", "basic info", "stock info", "listing date", "shares outstanding", "IPO price", "symbol info", "static data", "security info", "exchange listing", "total shares".
URL search param and hash state management. Use when adding or modifying URL search params, working with useSearchParams, setSearchParams, useSearchParamState, or navigate() with query strings or hash fragments, or fixing browser back/forward button issues.
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.
End-to-end automated operation for publishing Zoom recordings as lectures on PORSEO LMS / AI PLAY GUILD, then automatically handing off to note membership article creation. It also supports a branch where Zoom recordings are not uploaded as lectures, but only converted into note articles with eye-catching images and screenshots. Responsible for searching unpublished Zoom recordings, matching with lecture candidates, retrieving VTT transcripts and chat logs, creating summaries/lecture data, generating YouTube-style thumbnails and applying Convex Storage, importing to Mux, publishing to production Convex, notifying Discord forums, handing off to note articles, and deleting incorrectly published videos. Used when requested with commands like "Turn this Zoom video into a lecture", "Find and publish unpublished videos", "Create and link lecture thumbnails", "Notify Discord about the video", "Create a note article after publishing", "Turn this Zoom recording into only a note article", "Don't upload it as a lecture", "Include note thumbnails and screenshots", "Delete this lecture video".
Academic literature orientation skill that searches papers via Consensus, builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a professionally formatted Word document (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — not a finished review, but an orientation guide that lets a researcher dive in confidently. Triggers: 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'. Do NOT trigger for single one-off paper searches where the user just wants a quick list — that's a plain Consensus search.
Decision-grade entity research skill — produces a hypothesis-tested dossier on a specific company, person, nonprofit, or government org, not a generic profile. Forcing intake makes the user state their hypothesis upfront (what they already believe and want to verify or disprove) so the dossier tests it rather than confirms it. Output is an editable Word document (.docx) with verdict on the hypothesis, identity facts, 12-month activity timeline, network signals, reputation signals, red flags, 3-5 conversation hooks tied to specific findings, and source-provenance audit log. Uses WebSearch + WebFetch + free APIs (SEC EDGAR, GitHub, ProPublica Nonprofit Explorer) as workhorses; optional BYOK MCPs (LinkedIn, Crunchbase, Apollo, Pitchbook, SimilarWeb) enhance coverage. Triggers: 'research [company]', 'dossier on [person/company]', 'background check on [entity]', 'prep me for a meeting with [person/company]', 'due diligence on [company]', 'what should I know about [entity]', 'research [person] before I [meet/hire/invest]', 'competitor research on [company]', 'investor diligence [company]', 'interview prep for [company]'. Honors sensitivity exclusions for journalism + personal-vetting contexts.
Python backend testing patterns with pytest for FastAPI applications. Use when writing Python tests: unit tests for services and repositories, integration tests for API endpoints with httpx.AsyncClient, fixture creation, factory setup with factory_boy, async testing with pytest-asyncio, mocking strategies, and parametrized tests. Covers test organization (tests/unit, tests/integration), conftest hierarchy, and coverage requirements. Does NOT cover frontend tests (use react-testing-patterns) or E2E browser tests (use e2e-testing).
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.