Total 55,498 skills, Data Processing has 2835 skills
Showing 12 of 2835 skills
Expert guidance for creating, modifying, and optimizing dbt pipelines for BigQuery. Use this skill whenever user asks for generating or modifying a dbt model or project. Activate this skill when the user - Creates, modifies, or troubleshoots **dbt models or pipelines** - Needs to **optimize SQL** within a dbt project - Is **setting up a new dbt project** or configuring existing one
Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Configure and operate the Neo4j Connector for Kafka (sink + source) and the native Neo4j CDC API. Covers Cypher/Pattern/CUD sink strategies, CDC-based and query-based source, exactly-once semantics, DLQ error handling, Confluent Cloud managed connector, schema registry (Avro/JSON), and native db.cdc.query cursor-loop patterns (Neo4j 5.13+ Enterprise/Aura BC/VDC). Use when streaming Kafka events into Neo4j, streaming Neo4j changes to Kafka, or querying Neo4j change events without Kafka. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle bulk CSV/file import — use neo4j-import-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Detect and classify candlestick patterns from ingested OHLCV data
Logstash integration. Manage data, records, and automate workflows. Use when the user wants to interact with Logstash data.
Drug regulatory and approval research -- FDA substance registry lookup, drug classification by ATC/EPC/MoA via RxClass, Orange Book generic availability and patent status, DailyMed label parsing (adverse reactions, dosing, contraindications), and clinical trial search. Use when users ask about FDA-approved drugs, drug regulatory status, generic availability, patent expiration, drug class membership, drug labeling, or substance identification.
Integrate structural biology data with proteomics for drug target validation. Retrieves protein structures from PDB (RCSB, PDBe), AlphaFold predictions, antibody structures (SAbDab), GPCR data (GPCRdb), binding pocket analysis (ProteinsPlus), and ligand interactions (BindingDB). Use when asked to find structures for a drug target, identify binding site ligands, cross-validate drug binding with structural data, assess structural druggability, or compare experimental vs predicted structures.
Find and evaluate research datasets for any scientific question. Teaches how to reason about data needs, search across public repositories, evaluate dataset fitness, and identify access requirements. Use whenever users ask to find data, search for datasets, identify cohort studies, or need data for analysis. Also use when users ask about a specific survey or cohort (NHANES, HRS, UK Biobank, TCGA, etc.), when they want to know what data exists for a research question, or when they need to compare available data sources. If the user mentions "where can I get data" or "is there a dataset for X", this is the right skill.
Guide pharmacogenomics (PGx) research -- drug-gene interaction lookup, CPIC guideline retrieval, variant-drug annotation, allele function status, FDA biomarker labeling, and clinical dosing recommendations. Covers the full CPIC-to-PharmGKB-to-clinical-recommendation workflow. Use when users ask about pharmacogenomics, drug-gene interactions, CPIC guidelines, genotype-guided dosing, PGx biomarkers, CYP enzyme phenotypes, or star allele interpretation.
Pull every meal you ever logged out of MyFitnessPal — per-food CSV, agent-shaped trends, and a local SQLite store. Trigger phrases: `what did I eat this week`, `export my food diary`, `find every time I logged X`, `top foods driving my protein`, `am I hitting my calorie streak`, `use myfitnesspal`, `run myfitnesspal`.
ETF analysis framework via Longbridge — product screening (AUM/expense ratio/index), tracking error, liquidity (bid-ask spread/volume), premium/discount (NAV vs market price), and A-share ETF allocation insights. Triggers: "ETF分析", "ETF选择", "ETF跟踪误差", "ETF溢价", "ETF流动性", "ETF费率", "ETF规模", "宽基ETF", "行业ETF", "指数基金", "ETF分析", "ETF選擇", "ETF追蹤誤差", "ETF溢價", "ETF流動性", "ETF費率", "ETF規模", "指數基金", "ETF analysis", "ETF selection", "tracking error", "ETF premium discount", "ETF liquidity", "expense ratio", "broad market ETF", "sector ETF", "index fund".
Quantitative strategy generation and optimisation framework via Longbridge — create, modify, and backtest quant strategies: parameter grid search, walk-forward validation, overfitting detection (in-sample vs. out-of-sample), strategy combination (multi-strategy correlation diversification), Sharpe / Calmar ratio optimisation. Generates Python code frameworks for local execution. Triggers: "策略优化", "策略生成", "参数优化", "网格搜索", "回测优化", "过拟合", "walk-forward", "策略回测优化", "策略組合", "策略優化", "策略生成", "參數優化", "網格搜索", "回測優化", "strategy optimization", "strategy generation", "parameter optimization", "grid search", "overfitting", "walk-forward validation", "strategy backtest", "Sharpe ratio", "Calmar ratio".