Total 50,502 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Serverless GDS sessions on Neo4j Aura — covers GdsSessions, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection, gds.graph.project.remote, gds.graph.construct, algorithm execution (mutate/stream/write), async job polling, result retrieval, and session lifecycle. Use when running graph algorithms on Aura Business Critical or VDC, processing graph data from Pandas/Spark, or using the graphdatascience Python client in AGA (serverless) mode. Covers all three data source three source modes (AuraDB-connected, self-managed Neo4j, standalone from DataFrames). Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.
TOC router for PyGraphistry tasks. Use when a request involves PyGraphistry and you need to choose the right workflow: loading/ETL shaping, visualization/layout/sharing, GFQL queries (Cypher, chain-lists, Let/DAG, GRAPH constructors), AI/UMAP/embed/semantic-search workflows, or connector-specific ingestion.
[Hyper] Investigate websites with Playwriter plus CDP to choose a crawl strategy, capture API/auth evidence, document findings under `.hypercore/crawler/[site]/`, and generate crawler code only after discovery is grounded.
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.
Use this skill when the user asks to "set up parsing", "create parsing rule", "extract fields from logs", "regex extraction", "log parsing", "enrich logs", "add context to logs", "custom enrichment table", "lookup table", "geo enrichment", "create metric from logs", "events to metrics", "convert logs to metrics", "generate metrics from events", "recording rule", "precomputed metrics", "PromQL recording", "configure data pipeline", "transform log data", "data processing rules", "rule group", "enrichment settings", "E2M definition", "labels cardinality", "bulk delete rules", "enrichment limits", "search enrichment table", or wants to configure how Coralogix processes, enriches, or transforms ingested data.
ClickHouse integration. Manage data, records, and automate workflows. Use when the user wants to interact with ClickHouse data.
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
Rare disease genomics research -- disease identification via Orphanet, causative gene discovery, gene-disease validity assessment via GenCC, pathogenic variant lookup via ClinVar, HPO phenotype mapping, epidemiology and prevalence data, clinical trial search, and literature review. Use when users ask about rare diseases, orphan diseases, genetic causes of rare conditions, Orphanet codes, HPO phenotypes, gene-disease validity, rare disease prevalence, or treatment options for rare genetic disorders.
Reconcile general ledger to subledger for a trade date or period — match at the position or transaction level, surface breaks, and classify each break by likely cause. Use for daily or month-end recon runs across asset classes.
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".
Systematic 100-point financial health scorecard for listed companies via Longbridge — five dimensions: profitability (ROE / net margin / gross margin), growth (revenue & net income YoY), financial health (debt ratio / current ratio / interest coverage), cash quality (operating CF / net income), operating efficiency (inventory & AR turnover days). Outputs a report card. Triggers: "财报体检", "财务体检", "财务评分", "财务健康", "盈利能力", "偿债能力", "运营效率", "现金质量", "财务综合评分", "上市公司体检", "財報體檢", "財務體檢", "財務評分", "財務健康", "盈利能力", "償債能力", "運營效率", "現金質量", "financial health check", "financial score", "profitability analysis", "solvency analysis", "operating efficiency", "cash quality", "company financial checkup".