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Found 286 Skills
Query and analyze business data in NocoBase via MCP. Use when users want current counts, grouped breakdowns, owner/source distributions, or business summaries across collections, with main data source first and fallback discovery to other enabled data sources.
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.
C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Analyze the BTC market using a custom momentum theory with nested multi-timeframe analysis (2-day/1-day/12h/6h/4h/2h/1h/30min). Identify uptrend segments, downtrend segments, discrete regulation, unit adjustment cycles, continuous gap divergences, and DIF-DEA divergences, and generate momentum reports with detailed attribute judgments and trading signals. Automatically activates when users inquire about BTC momentum, segment status, MACD analysis, cycle judgment, or divergence detection.
Creates custom Docker-based State Transition Functions (STFs) for D6E platform workflows. Use when building containerized business logic for D6E, implementing data processing steps, or creating workflow functions that need database access. Handles JSON input/output, SQL API integration, and multi-language implementations (Python, Node.js, Go).
Best practices for NumPy array programming, numerical computing, and performance optimization in Python
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.
Parse raw text from an Instagram or TikTok Story insights screenshot and format it into a clean, spreadsheet-ready row with labeled fields. This skill should be used when parsing Story metrics from a screenshot, formatting Story insights for a spreadsheet, extracting metrics from a pasted Story screenshot, cleaning up Story analytics data, converting Story insights text into structured data, turning a Story performance screenshot into a row for the tracker, logging Story metrics into a spreadsheet, normalizing Story screenshot data, pulling numbers from a Story insights paste, organizing Story metrics from creator screenshots, processing a batch of Story screenshots into rows, building a Story metrics tracker from screenshots, or entering Story data from a screenshot into a sheet. For normalizing metrics from multiple sources into a unified table, see metrics-normalization-formatter. For calculating engagement rates and comparing to benchmarks, see engagement-rate-calculator-benchmarker.
Data processing expert including parsing, transformation, and validation