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Found 2,133 Skills
Central authority for Claude Agent SDK (TypeScript and Python SDKs). Covers SDK installation, authentication (Anthropic key, Bedrock, Vertex), sessions and resumption, forking sessions, streaming vs single mode, custom tools, permissions (allowedTools, disallowedTools, permissionMode), MCP integration, system prompts (CLAUDE.md, appendSystemPrompt, outputStyle), cost tracking, todo tracking, structured outputs, hosting patterns, plugins, and SDK branding guidelines. Assists with building custom agents, configuring SDK options, and troubleshooting SDK issues. Delegates 100% to docs-management skill for official documentation.
Entry point for ASCII CLI banners. Choose the Python built-in font skill or the figlet.js/FIGfont skill depending on needs.
Complete syntax reference for Frappe Server Scripts. Use this skill when Claude needs to write Python code for Server Scripts in ERPNext/Frappe, including Document Events, API endpoints, Scheduler Events, and Permission Queries. Covers sandbox limitations, available frappe.* methods, event name mapping, and correct syntax for v14/v15/v16.
Auto-generates code flow diagrams from Python module analysis. Detects when architecture diagrams become stale (code changed, diagram didn't). Use when: creating new modules, reviewing PRs for architecture impact, or checking diagram freshness. Generates mermaid diagrams showing imports, dependencies, and module relationships.
The foundational library for creating static, animated, and interactive visualizations in Python. Highly customizable and the industry standard for publication-quality figures. Use for 2D plotting, scientific data visualization, heatmaps, contours, vector fields, multi-panel figures, LaTeX-formatted plots, custom visualization tools, and plotting from NumPy arrays or Pandas DataFrames.
VCR.py HTTP recording for Python tests. Use when testing Python code making HTTP requests, recording API responses for replay, or creating deterministic tests for external services.
Access Airtable bases, tables, and records. Use when user mentions Airtable, bases, tables, records, or spreadsheet data. Uses Python pyairtable library for clean, reliable access.
Use `uv` instead of pip/python/venv. Run scripts with `uv run script.py`, add deps with `uv add`, use inline script metadata for standalone scripts.
Expert guidance for HTML/XML parsing using BeautifulSoup in Python with best practices for DOM navigation, data extraction, and efficient scraping workflows.
Jira API operations via Python CLI scripts. AUTOMATICALLY TRIGGER when user mentions Jira URLs (https://jira.*/browse/*, https://*.atlassian.net/browse/*), issue keys (PROJ-123), or asks about Jira issues. Use when Claude needs to: (1) Search issues with JQL queries, (2) Get or update issue details, (3) Create new issues, (4) Transition issue status (e.g., "To Do" → "Done"), (5) Add comments, (6) Log work time (worklogs), (7) List sprints and sprint issues, (8) List boards and board issues, (9) Create or list issue links, (10) Discover available Jira fields, (11) Get user profile information, (12) Download attachments from issues. If authentication fails, offer interactive credential setup via jira-setup.py. Supports both Jira Cloud and Server/Data Center with automatic auth detection.
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
Guide for modernizing legacy Python 2 scientific computing code to Python 3 with modern libraries. This skill should be used when migrating scientific scripts involving data processing, numerical computation, or analysis from Python 2 to Python 3, or when updating deprecated scientific computing patterns to modern equivalents (pandas, numpy, pathlib).