Turn selected research conversations or notes into themes supported by evidence and counterexamples. Distinguish observed behavior from preferences and hypotheses, assess confidence, and suggest a small next experiment with an observable measure of success.
Synthesize repeated themes from selected research conversations or notes.
Review the instructions the agent will receive before you install.
Confirm the source set and research question. Extract observations with source pointers before grouping them. A repeated claim in one conversation is one source, not multiple participants. For each theme, state the supporting evidence, counterexamples, confidence, and practical implication. Separate observed behavior from preferences and hypotheses. Do not force consensus or invent prevalence. Recommend a small next experiment with an observable success criterion. Use role labels in shareable output and avoid identifying quotes unless explicitly requested.
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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.