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About this tool
Turn a coding task into a Copilot-ready comment prompt with context, examples and constraints in your language's comment syntax.
This builder turns a plain-English coding task into a structured GitHub Copilot prompt — goal, inputs, expected output, one constraint per line, then a concrete example — rendered as a comment block in your language's syntax with a function stub to complete from. The structure follows GitHub's published prompt-engineering guidance for Copilot: set context first, keep to a single task, and steer with an input → output example.
Open GitHub Copilot Prompt Builder on AltFTool — it loads instantly in your browser.
Paste your code or data sample into the workspace.
Pick the format, conversion, or analysis you need.
Copy the polished result straight back into your project.
Orders the prompt goal → inputs → output → constraints → example, per the official guidance.
Emits the right comment syntax and signature stub for JavaScript through SQL and Bash.
Warns on compound tasks, missing examples, missing return description and unnamed functions.
State the goal in one sentence, then the inputs, the expected output, any constraints, and finish with a concrete input → output example — all as comments directly above your cursor. GitHub's guidance summarises it as: provide context, keep it to a single task, and show an example rather than describing one.
Yes. Inline Copilot builds its completion from the code and comments around your cursor, plus your open editor tabs. A comment block immediately above an empty function signature is the most direct way to specify what the completion should do.
Usually because the prompt is underspecified — the task covers several responsibilities, the return type is unstated, or there is no example. Splitting compound tasks into one prompt each, naming the function descriptively and adding an input → output pair are the three fixes GitHub's documentation recommends first.
Inline comment prompts work best for a single function or block where surrounding code gives context; Copilot Chat suits multi-file changes, explanations and refactors where you need to iterate in conversation. The same goal-inputs-output-example structure improves results in both.