Review recorded activity over a chosen period to understand sustained work and app switching. Get one evidence-based pattern and one small experiment for the next day, with uncertainty stated and no invented productivity scores.
Review focus and context switching over a chosen period without inventing productivity scores.
Review the instructions the agent will receive before you install.
Use screenpipe-api activity-summary for the chosen range and timezone. Check capture coverage. Use active minutes and app/window intervals, not screenshot frequency. Distinguish sustained work, meetings, breaks, and background automation only when the evidence supports it. An app name alone does not establish intent, distraction, or success. Describe one useful pattern and one small experiment for the next day. State uncertainty. Avoid moral judgments, health inferences, or unsupported time-saved claims.
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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.
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.
Create charts, dashboards, scorecards, and styled summaries as standalone HTML reports for the screenpipe viewer. Keep styles, scripts, and images inside the file so the report renders in a sandbox with no network access.
Use Screenpipe’s local REST API, an interface for programmatic requests, to search screen and meeting history, review app activity and active time, retrieve synced-device records, export recordings, and rerun audio transcription.
Search Screenpipe recordings, screen text, audio transcripts, and synced-device history through its local API. Use the evidence to review activity, find meeting details, and inspect saved workflows, including recorded steps that can inform automation.
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.