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Found 184 Skills
Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search for terms, retrieve details, navigate hierarchies (parents, children, ancestors), look up properties and individuals, get autocomplete suggestions, or access ontology metadata and statistics.
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles, interprets PRS percentiles, and assesses disease predisposition across conditions including type 2 diabetes, coronary artery disease, and Alzheimer's disease. Use when asked to calculate polygenic risk scores, interpret genetic risk for complex diseases, build custom PRS from GWAS data, or answer questions like "What is my genetic predisposition to breast cancer?"
Fetch current, hourly, and daily weather forecasts and display required attribution using WeatherKit. Use when integrating weather data, showing forecasts, handling weather alerts, displaying Apple Weather attribution, or querying historical weather statistics in iOS apps.
Clean up old tracking data and reset statistics
Fetches trending skills directly from skills.sh, generates statistics, and creates a trend summary. Optimized for ultra-fast, direct execution without complex setup.
Guides quantitative research for markets and finance—research question framing, data sourcing and quality checks, descriptive and inferential statistics, time series and panel methods (high level), factor and signal research, backtest design and pitfalls (lookahead, survivorship), risk metrics (volatility, drawdown, Sharpe limitations), regime and stress analysis, and reproducible notebooks or reports with explicit limitations and uncertainty communication. Use when the user mentions "quantitative research", "quant researcher", "factor research", "signal backtest", "time series analysis", "panel regression", "alpha research", "Sharpe ratio analysis", "survivorship bias", "lookahead bias", "econometric analysis", or "risk factor model". Not for production ML pipelines (data-scientist, ml-research-engineer), equity narrative reports (equity-research skills), SOX accounting (financial-statements), legal investment advice, or trading execution systems (senior-software-engineer).
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
Data journalism workflows for analysis, visualization, and storytelling. Use when analyzing datasets, creating charts and maps, cleaning messy data, calculating statistics or building data-driven stories. Essential for reporters, newsrooms and researchers working with quantitative information.
Kalshi prediction markets — events, series, markets, trades, and candlestick data. Public API, no auth required for reads. US-regulated exchange (CFTC). Covers soccer, basketball, baseball, tennis, NFL, hockey event contracts. Use when: user asks about Kalshi-specific markets, event contracts, CFTC-regulated prediction markets, or candlestick/OHLC price history on sports outcomes. Don't use when: user asks about actual match results, scores, or statistics — use football-data or fastf1 instead. Don't use for general "who will win" questions unless Kalshi is specifically mentioned — try polymarket first (broader sports coverage). Don't use for news — use sports-news instead.
NFL data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, win probability, injuries, transactions, futures, depth charts, team/player stats, leaders, and news. Zero config, no API keys. Use when: user asks about NFL scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, depth charts, team/player statistics, or NFL news. Don't use when: user asks about football/soccer (use football-data), college football (use cfb-data), or other sports.
Convert flomo memos from local desktop auth/API into grouped Markdown files for AI/NotebookLM reading, plus human-readable Markdown tag statistics with tree totals. Use when a user asks to export flomo notes to Markdown, split memos by month/quarter/year, generate NotebookLM-friendly archives, or produce flomo tag counts/aggregation.
Embedded CAN/CAN-FD debugging tool for interface scanning, message monitoring, test frame transmission, log recording, database file decoding, and bus statistics. Automatically triggered when users mention CAN, CAN-FD, DBC decoding, bus packet capture, USB-CAN joint debugging, message transmission, bus statistics, PCAN, Vector, slcan, CAN interface scanning, CAN ID filtering, ASC logs, BLF files. Also compatible with explicit invocation via /can. Even if users only say "check CAN messages", "send a test frame" or "decode DBC", this skill should be triggered as long as the context involves CAN bus communication.