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Common pandas and matplotlib patterns for data analysis and visualization
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Use this skill when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify snippets. Applies when extracting code blocks from documentation, creating runnable code samples, using snippet delineators, or wiring snippet output into MDX includes.
Build batteries-included agents with planning, context management, subagent delegation, and sandboxed execution. Use for complex, multi-step tasks that need built-in capabilities.
Build agents with a prebuilt architecture and integrations for any model or tool. Use when creating tool-calling agents, switching model providers, or adding structured output.
Build stateful, durable agent workflows with LangGraph. Use when you need custom graph-based control flow, human-in-the-loop, persistence, or multi-agent orchestration.
Trace, evaluate, and deploy AI agents and LLM applications with LangSmith. Use when adding observability, running evaluations, engineering prompts, or deploying agents to production.
Use when the user wants the current date and time written to a file via the bundled script inside the sandbox.
| name | pandas-patterns |
| description | Common pandas and matplotlib patterns for data analysis and visualization |
Use pd.read_csv() for CSV files. Always check df.info() and df.describe() first.
Use matplotlib for bar charts, seaborn for statistical plots.
Save figures with plt.savefig("output.png", dpi=150, bbox_inches="tight").
Write a markdown summary to report.md alongside any generated charts.
