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Found 278 Skills
MANDATORY when working with geographic data, spatial queries, geometry operations, or location-based features - enforces PostGIS 3.6.1 best practices including ST_CoverageClean, SFCGAL 3D functions, and bigint topology
Manages Ahrefs API usage in Python using `ahrefs-python` library. Use when working with SEO / marketing related tasks or with data including backlinks, keywords, domain ratings, organic traffic, site audits, rank tracking, and brand monitoring. Covers `ahrefs-python` usage including AhrefsClient / AsyncAhrefsClient, typed request/response models, error handling, and all API sections.
This skill should be used when building data processing pipelines with CocoIndex v1, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex v1 is Python-native (supports any Python types), has no DSL, and is currently under pre-release (version 1.0.0a1 or later).
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "replication lag", "partitioning strategy", "consistency vs availability", or "stream processing". Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.
Generate professional data reports with charts, tables, and visualizations
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
Analyze stock liquidity using bid-ask spreads, volume profiles, order book depth, market impact estimates, and turnover ratios via Yahoo Finance data. Use this skill whenever the user asks about liquidity, trading costs, bid-ask spread, market depth, volume analysis, slippage, market impact, turnover ratio, or how easy/hard it is to trade a stock without moving the price. Triggers: "how liquid is AAPL", "bid-ask spread", "volume analysis", "order book depth", "market impact of a large order", "turnover ratio", "slippage estimate", "can I trade 100k shares without moving the price", "liquidity comparison", "spread analysis", "ADTV", "Amihud illiquidity", "dollar volume", "execution cost estimate", "liquidity score", penny stocks, small caps, or thinly traded securities.
Search DuckDB and DuckLake documentation and blog posts. Returns relevant doc chunks for a question or keyword using full-text search against a locally cached index.
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
Meteomatics Weather API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Meteomatics Weather API data.
Research Xiaohongshu accounts from validated recent-post surfaces, then aggregate account-level content signals without pretending follower or bio metrics are available when the validated profile actor is empty.
Earnings estimate revision analysis for listed companies via Longbridge — tracks analyst consensus revision direction (upgrade / downgrade), earnings surprise (SUE = standardised unexpected earnings), PEAD post-earnings drift signals (consecutive beats + upward revisions = positive momentum), and management guidance revision impact. Builds on raw data from longbridge-consensus. Triggers: "预期修正", "盈利修正", "分析师上调", "分析师下调", "超预期", "低于预期", "PEAD", "财报后漂移", "业绩意外", "管理层指引", "預期修正", "盈利修正", "分析師上調", "分析師下調", "超預期", "低於預期", "財報後漂移", "業績意外", "管理層指引", "earnings revision", "estimate revision", "analyst upgrade", "analyst downgrade", "beat miss surprise", "SUE", "PEAD post-earnings drift", "guidance revision", "estimate cut raise".