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Found 52 Skills
Create publication-quality matplotlib/seaborn charts with readable axes, tight layout, and curated palettes.
Build interactive data applications and dashboards with pure Python - no frontend experience required
Use this for exploratory data analysis (EDA), generating visualizations, finding trends, and deriving insights from datasets using Python (Pandas/Seaborn/Plotly) or SQL.
This skill should be used when the user asks to "use NumPy", "write NumPy code", "optimize NumPy arrays", "vectorize with NumPy", or needs guidance on NumPy best practices, array operations, broadcasting, memory management, or scientific computing with Python.
Tushare is a financial data interface package with rich data content, including market data such as stocks, funds, futures, digital currencies, and fundamental data such as corporate finance and fund managers. This skill is designed for proxy access scenarios, and the interface calling syntax is consistent with the official Tushare Pro.
SQL and Python-based employee performance analytics with KPI aggregation, departmental insights, and HR dashboard generation
Sets up and operates Airbyte Agent Connectors — strongly typed Python packages for accessing 51+ third-party SaaS APIs through a unified entity-action interface. Supported services include Salesforce, HubSpot, Stripe, GitHub, Slack, Jira, Shopify, Zendesk, Google Ads, Notion, Linear, Intercom, Gong, and 36 more connectors spanning CRM, billing, payments, e-commerce, marketing, analytics, project management, helpdesk, developer tools, HR, and communication platforms. Make sure to use this skill when the user wants to connect to any SaaS API, install an airbyte-agent connector package, integrate third-party service data into a Python application or AI agent, query or search records from any supported service, or configure Airbyte MCP tools for Claude. Covers Platform Mode (Airbyte Cloud) and OSS Mode (local Python SDK).
Converts JSON data snippets into Python Pydantic data models.
Best practices for developing tools, dashboards and interactive data apps with HoloViz Panel. Create reactive, component-based UIs with widgets, layouts, templates, and real-time updates. Use when developing interactive data exploration tools, dashboards, data apps, or any interactive Python web application. Supports file uploads, streaming data, multi-page apps, and integration with HoloViews, hvPlot, Pandas, Polars, DuckDB and the rest of the HoloViz and PyData ecosystems.
Use when writing Python that processes biological sequences (DNA/RNA/protein) with the seqpro package — encoding, one-hot, k-mer shuffling, reverse complement, GC content, variable-length sequence batches, or anything involving seqpro's `Ragged` array. Covers the seqpro API surface and the conventions you need to use it correctly.
Develop and deploy Data Cloud Code Extensions using SF CLI plugin. Use this skill when creating custom Python transformations for Data Cloud, deploying code extensions, or testing data transformations. Supports init, run, scan, and deploy operations.
Decide where files live in an ML experimentation project: reusable code in `src/<pkg>/`, one `# %%` script per experiment in `experiments/`, design notes + index in `journal/`, reports in `reports/`, agent-only probes in `scratch/`, narrative digest in `overview/summary.md`. Owns the layout, the file-creation rules (one file per experiment, ask before editing), and the jupytext `# %%` script convention. Never imposes `data/` — the user owns that. TRIGGER — any of: - Starting a new ML project / scaffolding a workspace. - About to create the first experiment file in a project. - About to create `src/<pkg>/data.py` / `features.py` / `pipeline.py` / `evaluate.py` for the first time. - About to write a `.ipynb` for experimentation — redirect to a `# %%` script under `experiments/`. - User asks where something should live, how to organize the project, or how to set up the workspace. - About to add a new experiment iteration — decide new file vs edit existing (ask the user). SKIP when: the file is clearly part of an already-populated module (e.g., adding a function to existing `features.py`); pure refactor inside a single existing file; pipeline declaration mechanics (`build-ml-pipeline`); evaluation mechanics (`evaluate-ml-pipeline`); skore symbol lookup (`python-api`). HOW TO USE: **first run the Detection table** below — if any signal matches, glue to existing conventions (do not rename or move folders). If no signal matches, scaffold the default layout. **Emit the Pre-flight checklist as visible text and read the Stop conditions before any file is created or edited.** Use templates in `templates/`; copy and adapt, do not rewrite from scratch.