Audit intermediate Works with: Any AI

GitHub Contributor Experience Audit

Table output Has example

• 🐙 GitHub

Check how welcoming and efficient contributing is.

The Open buttons send the prompt text as currently shown below to that site only when you click. Fill in the placeholders first.

Audit the experience of outside contributors. Data: first-time contributor pull requests with time to first response and outcome, comments on them, and contributor feedback: [CONTRIBUTOR_DATA]. Our contributing guide: [CONTRIBUTING_TEXT].

Show a table with the metric, value, sample size and what it means: median time to first response, merge rate for first-time contributors, abandonment rate, and common reasons for change requests, with arithmetic. For example a median first response of 6 days means a newcomer waits most of a week to learn whether their effort matters, and a 40 percent merge rate invites a look at why the rest closed. State the sample size and limits.

Read a sample of review comments for clarity, kindness and specific guidance, noting good examples to copy, without criticising individuals. Rank barriers (unclear setup, slow reviews, hidden rules) by how many contributors they affect and give the smallest fix for each. Return five improvements, a way to measure them, and a welcome comment that thanks the contributor and explains what happens next with the usual timings.

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Invented data for 18 first-time pull requests. Median time to first response: 4 days. Merged: 8 (44 percent). Abandoned after a change request: 6 (33 percent). Closed by maintainers: 4 (22 percent). Common reasons for change requests: tests missing (7), formatting (5), unclear description (4).
Barriers: slow first response, no formatting tool in the guide. Improvements: reply within 2 working days with a welcome comment; add a formatter command to the guide; label simple tasks. Target next quarter: median response 2 days, merge rate above 55 percent.

• github