The useful part of an automation often lives outside the script: knowing when to run it, what inputs it expects, and how to recognize a good result.
GitHub’s November 12 guide to agent skills describes a way to capture repeatable instructions in files that teams can review, version, and reuse. That connects with my work across Python automation, APIs, reporting, and enterprise applications.
Consider a recurring data export. The code retrieves records, but the surrounding instructions explain which period to select, how to handle incomplete results, and what checks are needed before sharing the file. Capturing that knowledge makes the workflow easier to hand over. It also gives an AI agent a clearer task to follow. For a first reusable skill, I would choose one bounded job and document its inputs, steps, expected output, and stopping conditions. Project-specific settings would stay separate so another team could adapt the workflow. Then I would ask a colleague to try it and note every assumption the instructions left unstated. That handoff is a practical test: can someone use the workflow without needing its original author to explain it again?
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