Develop and test Windows-specific Screenpipe changes on a disposable Azure virtual machine. The workflow verifies native behavior, preserves logs and desktop video, and handles branch and pull request delivery when authorized.
Turn selected Screenpipe activity into a concise recap for a defined audience. Summarize outcomes from recorded evidence, then review the draft to remove identifying details, private links, sensitive amounts, and unnecessary personal information before sharing.
Search team activity in Screenpipe Enterprise and review records by device and time. Preview, deploy, and schedule managed Pipes, which are AI automations, with explicit targets and checks before changes.
Investigate and maintain documented workflows using Screenpipe evidence and shared drafts. Review whether recorded actions support each procedure, preserve verified steps, and add timing measurements only when clear start and end evidence is available.
Find facts, relevant examples, and prior wording in work history to support a writing draft. Use source evidence to separate completed work from plans and opinions, verify quotations, and remove sensitive details from public drafts unless disclosure is authorized.
Operate Screenpipe from the terminal to set up continuous recording, check capture health, and search local history. Manage Pipes, scheduled AI automations, and configure and test connections to external services.
Prepare screenpipe component releases by updating versions and dependency lock files, running required build checks, and triggering GitHub Actions. Verify build results and release artifacts, then hand public app publication over to a person.
Draft a project status report against stated milestones using recent Screenpipe activity and verified outcomes. Separate work performed from delivered and accepted results, summarize blockers and next actions, and cite dated sources with verification limits.
Find repeated tasks in recorded activity or starred examples, then describe their steps, exceptions, and human decisions. Assess a small automation candidate using captured evidence, available tools, permissions, and clear completion criteria.
Turn a recorded software failure into a draft bug report with timestamps, reproduction steps, expected and actual behavior, and relevant evidence. Separate visible symptoms from possible causes and remove sensitive details from shareable evidence.
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