Total 55,338 skills, Data Processing has 2830 skills
Showing 12 of 2830 skills
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.
Neo4j Graph Data Science (GDS) plugin — graph projection, algorithm execution, execution modes (stream/stats/mutate/write), memory estimation, and the GDS Python client (graphdatascience v1.21). Use when running gds.pageRank, gds.louvain, gds.wcc, gds.fastRP, gds.knn, gds.betweenness, gds.nodeSimilarity, or any gds.* procedure; projecting named in-memory graphs with gds.graph.project or graph.project; chaining algorithms with mutate mode; computing node embeddings for ML; building recommendation systems with FastRP + KNN. Also triggers on GraphDataScience, GdsSessions, graph catalog operations, ML pipelines, node classification, link prediction. Does NOT cover Aura Graph Analytics serverless sessions — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.
Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN / USING JOIN hints, db.stats.retrieve, SHOW QUERIES, SHOW TRANSACTIONS, TERMINATE TRANSACTION. Does NOT write new Cypher from scratch — use neo4j-cypher-skill. Does NOT cover GDS algorithm tuning — use neo4j-gds-skill. Does NOT cover index/constraint creation syntax details — use neo4j-cypher-skill references/indexes.md.
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation
Exports Amazon RDS or Aurora database snapshots to Amazon S3 in Apache Parquet format for analytics, backup, or data migration. Handles snapshot selection or creation, IAM role setup, KMS encryption, S3 bucket preparation, export task execution, progress monitoring, and data verification. Use when exporting RDS/Aurora data to S3 for Athena, Glue, or Redshift Spectrum consumption.
Research plant genes, pathways, and species using PlantReactome, Ensembl Plants, POWO, UniProt, KEGG, and literature tools. Covers plant pathway analysis, gene function annotation, species identification, crop genomics, and comparative plant biology. Use when asked about plant genes, Arabidopsis, crop improvement, plant pathways, plant metabolism, photosynthesis, plant development, or plant species identification.
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.
Deep financial statement analysis for listed companies via Longbridge — cross-statement reconciliation (IS↔BS↔CF), DuPont decomposition (ROE = net margin × asset turnover × equity multiplier), earnings-quality scoring (accrual ratio), and 10-item financial fraud red-flag checklist. Builds on raw data from longbridge-financial-report. Triggers: "三表勾稽", "杜邦分析", "杜邦拆解", "盈利质量", "应计利润", "财务造假", "财报深度", "财务红旗", "三表分析", "財務深度", "三表勾稽", "杜邦分析", "盈利質量", "應計利潤", "財務造假", "財報深度", "財務紅旗", "DuPont analysis", "accrual ratio", "earnings quality", "financial fraud red flags", "cross-statement reconciliation", "three-statement analysis".
Industry valuation comparison and distribution analysis via Longbridge — cross-peer valuation matrix (PE / PB / PS / dividend yield), industry-percentile ranking, and industry premium / discount for a single stock. Triggers: "行业估值", "行业溢价", "行业折价", "行业对比", "行业百分位", "同行业估值", "板块估值", "行业贵不贵", "行業估值", "行業溢價", "行業折價", "行業對比", "行業百分位", "板塊估值", "industry valuation", "sector valuation", "industry premium", "industry percentile", "peer valuation", "sector PE", "TSLA.US industry valuation", "700.HK sector comparison".
Analyze year-over-year growth in income statement items and financial metrics using Octagon MCP. Use when retrieving YoY Revenue Growth, Cost of Revenue Growth, Gross Profit Growth, Operating Income Growth, Net Income Growth, or comparing financial performance across fiscal periods for any public company.
Write and run AQL (Analytic Query Language) queries to answer data questions. Use this whenever the user asks for data, wants to query a dataset, needs to filter/aggregate/join data, or asks about metrics and dimensions in Holistics.
Design and conduct mixed methods research using convergent, explanatory sequential, or exploratory sequential strategies with genuine integration of qualitative and quantitative strands. Use this skill when the user needs to choose a mixed methods design, integrate qualitative and quantitative data at design, methods, or interpretation levels, justify mixing on pragmatist grounds, or when they ask 'which mixed methods design should I use', 'how do I integrate qual and quant findings', or 'is running both qual and quant enough to be mixed methods'.