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Found 64 Skills
This skill should be used when the user asks to "validate a DataFrame with pandera", "write a pandera schema", "use pandera DataFrameModel", "add data validation to a pipeline", or needs guidance on pandera best practices for data quality.
Generates importable n8n workflow JSON files that sync data between Personize and 400+ apps. Produces ready-to-import workflows for batch sync, webhook ingestion, per-record AI enrichment, and data export — no code required. Use this skill whenever the user wants no-code integrations, visual workflows, n8n automation, or to connect Personize to HubSpot, Salesforce, Google Sheets, Slack, Postgres, or any app without writing code. Also trigger when they mention 'workflow automation', 'scheduled sync without code', 'visual pipeline', or 'connect Personize to [app]' and don't want to write TypeScript.
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.
以全球鎳供給結構為核心，量化各國的主導程度（例如印尼）、主要礦區供給量、以及政策配額/減產情境對全球供需平衡與價格非對稱的影響。
Develops data processing pipelines, integrations, and machine learning scenarios in SAP Data Intelligence Cloud. Use when building graphs/pipelines with operators, integrating ABAP/S4HANA systems, creating replication flows, developing ML scenarios with JupyterLab, or using Data Transformation Language functions. Covers Gen1/Gen2 operators, subengines (Python, Node.js, C++), structured data operators, and repository objects.
Debugs and fixes dbt errors systematically. Use when working with dbt errors for: (1) Task mentions "fix", "error", "broken", "failing", "debug", "wrong", or "not working" (2) Compilation Error, Database Error, or test failures occur (3) Model produces incorrect output or unexpected results (4) Need to troubleshoot why a dbt command failed Reads full error, checks upstream first, runs dbt build (not just compile) to verify fix.
Converts legacy SQL to modular dbt models. Use when migrating SQL to dbt for: (1) Converting stored procedures, views, or raw SQL files to dbt models (2) Task mentions "migrate", "convert", "legacy SQL", "transform to dbt", or "modernize" (3) Breaking monolithic queries into modular layers (discovers project conventions first) (4) Porting existing data pipelines or ETL to dbt patterns Checks for existing models/sources, builds and validates layer by layer.
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.
High-performance Rust web crawler with stealth mode, LLM-ready Markdown export, multi-format output, sitemap discovery, and robots.txt support. Optimized for content extraction, site mapping, structure analysis, and LLM/RAG pipelines.
Integrate the lithium industry chain (mining → refined chemicals → batteries and end demand) into a set of computable proxy indicators; then map these indicators to the component exposure and long-term price trends of lithium-themed ETFs (such as LIT) to form a basis for decision-making.
Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.
Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. Activates for data development tasks.