Total 50,473 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Build credit scoring models to predict default probability from borrower characteristics. Use this skill when the user needs to assess creditworthiness, build a credit scorecard, or evaluate lending risk — even if they say 'predict default risk', 'credit scoring', or 'loan approval model'.
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
Summarizes descriptive concepts for max pain options theory, covered-call style crypto ETFs, crypto arbitrage families and risks, and bull/bear flag chart patterns—always as non-prescriptive education. Use when the user asks about max pain, premium income ETFs, arbitrage, funding rates, flash loans, or bull/bear flags in crypto trading context.
Validate, format, and convert between JSON, YAML, and TOML. Parse and query structured data files. No API key required.
Search patent databases and academic literature for prior art relevant to an invention. Use when user says "现有技术检索", "prior art search", "专利检索", "check patents", or wants to find relevant prior art.
Analyze A-share stocks using fixed scripts, supporting the maintenance of local watchlists and position pools, fetching individual stock and concept sector data, querying risks such as financial report disclosures and share reduction announcements, and outputting structured recommendations including Buy, Watch, Hold, Reduce Position, and Sell. Suitable for scenarios where you need to update local stock pools, fetch real-time data, compare the top three strongest stocks in a concept sector, or generate transaction analysis with risk prompts.
Extract quantities from BIM/CAD data for cost estimation. Group by type, level, zone. Generate QTO reports.
Research TikTok Shop listings, shops, pricing, and benchmark products through a local normalize-and-analyze workflow.
High-level entry point for cross-platform public social data extraction when the user has not named a platform-specific workflow yet.
Convert normalized timed transcript data into subtitle artifacts such as SRT and VTT. Use this when a stable normalized transcript JSON already exists and the main job is subtitle chunking, timing normalization, and export packaging.
Record transaction flow in accordance with unified rules. Save records by individual stock in Markdown format, and simultaneously write to SQLite for statistics and quantitative review.
Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-skill.