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Found 2,039 Skills
42-skill marketing division for AI coding agents. 7 specialist pods covering content, SEO, CRO, channels, growth, intelligence, and sales. Foundation context system + orchestration router. 27 Python tools (all stdlib-only). Works with Claude Code, Codex CLI, and OpenClaw.
MUST READ before writing or modifying ADK agent code. ADK API quick reference for Python — agent types, tool definitions, orchestration patterns, callbacks, and state management. Includes an index of all ADK documentation pages. Do NOT use for creating new projects (use adk-scaffold).
Complete guide for building MCP servers with FastMCP 3.0 - tools, resources, authentication, providers, middleware, and deployment. Use when creating Python MCP servers or integrating AI models with external tools and data.
Fetch financial and market data using the yfinance Python library. Use this skill whenever the user asks for stock prices, historical data, financial statements, options chains, dividends, earnings, analyst recommendations, or any market data. Triggers include: any mention of stock price, ticker symbol (AAPL, MSFT, TSLA, etc.), "get me the financials", "show earnings", "what's the price of", "download stock data", "options chain", "dividend history", "balance sheet", "income statement", "cash flow", "analyst targets", "institutional holders", "compare stocks", "screen for stocks", or any request involving Yahoo Finance data. Always use this skill even if the user only provides a ticker — infer intent from context.
Use this skill when you need to work with context7 through its generated async Python app, call its MCP-backed functions from code, or inspect available functions with the mcp-skill CLI.
Guidelines for building RoboCorp RPA automation with Python, emphasizing functional programming, Pydantic validation, and async operations.
Validates and scores Claude Code skill packages for quality, completeness, and best practices compliance. Tests Python scripts, checks YAML frontmatter, and generates quality reports. Use when creating new skills, validating skill packages, or auditing skill quality.
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
This skill should be used when the user asks to "use marimo", "create a marimo notebook", "debug a marimo notebook", "inspect cells", "understand reactive execution", "fix marimo errors", "convert from jupyter to marimo", or works with marimo reactive Python notebooks.
Python CLI harness for WireMock HTTP mock server administration
cuTile Python DSL kernel implementation patterns, CtKernel runtime wrapper, suitability gate, and cuTile-specific pitfalls. Use when: (1) creating or modifying a cuTile Python DSL kernel version, (2) implementing an optimization that still fits within cuTile's exposed control surface, (3) deciding whether cuTile is still the right DSL, (4) reviewing cuTile-specific runtime patterns. Always also load /design-kernel for shared naming, versioning, and workflow.
Serverless GDS sessions on Neo4j Aura — covers GdsSessions, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection, gds.graph.project.remote, gds.graph.construct, algorithm execution (mutate/stream/write), async job polling, result retrieval, and session lifecycle. Use when running graph algorithms on Aura Business Critical or VDC, processing graph data from Pandas/Spark, or using the graphdatascience Python client in AGA (serverless) mode. Covers all three data source three source modes (AuraDB-connected, self-managed Neo4j, standalone from DataFrames). Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.