Total 50,402 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
Stream Light Protocol account state via Laserstream gRPC. Covers token accounts, mint accounts, and compressible PDAs with hot/cold lifecycle tracking. Use when building custom data pipelines, aggregators, or indexers.
Smart Excel/CSV file parsing with intelligent routing based on file complexity analysis. Analyzes file structure (merged cells, row count, table layout) using lightweight metadata scanning, then recommends optimal processing strategy - either high-speed Pandas mode for standard tables or semantic HTML mode for complex reports. Use when processing Excel/CSV files with unknown or varying structure where optimization between speed and accuracy is needed.
Use when writing R code that manipulates expressions, builds code programmatically, or needs to understand rlang's defuse/inject mechanics. Covers: defusing with expr()/enquo()/enquos(), quosure environment tracking, injection with !!/!!!/{{, symbol construction with sym()/syms(). Does NOT cover: data-mask programming patterns (tidy-evaluation), error handling (rlang-conditions), function design (designing-tidy-r-functions).
Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.
Review new or updated Coral source manifests and source PRs for content, style, product fit, query ergonomics, documentation quality, and consistency with existing Coral sources. Use when Codex is asked to review a sources/core/name or sources/community/name source directory, a manifest.yaml, or a GitHub PR that adds or changes a Coral source.
Buffett-style stock screener — "What would Buffett buy now?" Generates 3–5 candidate stocks from a market / sector / preference query via a two-layer model: hard quant filter (ROE 5y ≥15%, debt/asset ≤50%, FCF positive 3y, listed ≥5y, gross margin ≥30%) → qualitative moat scoring (moat 35% / capital allocation 20% / earnings predictability 20% / valuation 15% / runway 10%). Longbridge CLI first, MCP fallback, WebSearch for gaps only. Output: candidate cards with moat-type tag, quantitative highlights, verdict (🟢 likely buy / 🟡 wait for price / 🔴 not at this price), deep-dive CTA to `longbridge-buffett-moat-analyzer`. Mandatory holding-period education + data-source appendix. Disqualifies airlines, pre-revenue biotech, ST, listing<5y. Triggers: "巴菲特会买什么", "巴菲特选股", "巴菲特风格的股票", "护城河选股", "宽护城河股票", "价值投资选股", "10年不动的股票", "定价权强的公司", "巴菲特會買什麼", "巴菲特選股", "護城河選股", "寬護城河股票", "Buffett screener", "what would Buffett buy", "wide-moat screener", "quality compounder screen", "Berkshire-style screen", "pricing-power screen".
Valuation methodology framework covering absolute (DCF / DDM / SOTP) and relative (PE-Band / PB-ROE / EV-EBITDA / PS) approaches — when to use each, pros/cons, common pitfalls, and practical application with Longbridge data. Triggers: "估值方法", "估值方法论", "DCF", "DDM", "SOTP", "PE估值", "EV/EBITDA", "绝对估值", "相对估值", "估值框架", "估值方法論", "絕對估值", "相對估值", "valuation methodology", "DCF model", "DDM", "SOTP", "PE band", "EV EBITDA", "absolute valuation", "relative valuation", "valuation framework".
Convert CSV/Excel/JSON data into beautiful visualization report pages
Extract text from images using Tesseract OCR
3D structural geological modeling using implicit methods. Create geological models with faults, folds, and unconformities from surface points and orientations. Use when Claude needs to: (1) Build 3D geological models from surface contacts and orientations, (2) Model faults, unconformities, or intrusions, (3) Compute and visualize subsurface geology, (4) Export models to VTK or numpy arrays, (5) Generate gravity forward models, (6) Create cross-sections or 3D visualizations.
Analyze stocks, ETFs, and crypto using Yahoo Finance and Alpha Vantage. Get real-time quotes, fundamentals (P/E, EPS, margins, balance sheet), technical indicators (RSI, MACD, Bollinger), dividends, earnings, options chains, analyst ratings, institutional holders, sector comparisons, and screening. Use when the user asks about stock prices, company financials, market trends, portfolio analysis, or any investment research.
Free 9-week data engineering course covering Docker, Terraform, Kestra, BigQuery, dbt, Spark, and Kafka with hands-on projects