analyze-processes
Find a person's repeated tasks from their screen activity, and what each one is worth automating, with the time and money saved.…
Summarize what you've been doing in the last 30 minutes.
Claude Code
$ npx -y skills add deusXmachina-dev/memorylane --skill recent-activity --agent claude-code
Codex
$ npx skills add deusXmachina-dev/memorylane --skill recent-activity -a codex -y
Cursor
$ npx skills add deusXmachina-dev/memorylane --skill recent-activity -a cursor -y
Gemini CLI
$ npx skills add deusXmachina-dev/memorylane --skill recent-activity -a gemini-cli -y
GitHub Copilot
$ npx skills add deusXmachina-dev/memorylane --skill recent-activity -a github-copilot -y
OpenCode
$ npx skills add deusXmachina-dev/memorylane --skill recent-activity -a opencode -y
Windsurf
$ npx skills add deusXmachina-dev/memorylane --skill recent-activity -a windsurf -y
name: recent-activity allowed-tools: mcp__memorylane__browse_timeline, mcp__memorylane__search_context, mcp__memorylane__get_activity_details description: Summarize what you've been doing in the last 30 minutes.
Summarize the user's recent screen activity.
This skill reads live data from the desktop app, so it uses only the local tools (`search_context`). Never use the remote tools here, even if local returns nothing: remote data syncs daily, so it can't show the last 30 minutes.
Tools can load on demand, so search for them first. If the local tools don't turn up, stop and reply with exactly:
> Recent activity needs the MemoryLane desktop app on this computer. In the app, open **Settings > Integrations** and click **Connect** or **Reconnect** next to **Claude Cowork**, then run this again. Connector data syncs daily, so it can't show the last 30 minutes.
Do not suggest plugin settings, Retry, or toggling the plugin.
Call `browse_timeline(startTime="30 minutes ago", endTime="now", limit=50, sampling="recent_first")`.
From the returned entries:
1. Identify distinct apps from the activity summaries. 2. Cluster entries by task — use the summary text to group related activities together. 3. Estimate approximate time spent on each cluster from the timestamps of its entries. 4. Order groups by recency (most recent first).
Only call `get_activity_details(ids)` when a summary is ambiguous and the exact on-screen text would genuinely help clarify what the user was doing. Do not fetch OCR speculatively.
Format as a brief narrative followed by bullet points:
**Last 30 minutes** (N activities recorded) You were primarily working on [main task]. - **[App Name]** (~X min) — [what you were doing] - **[App Name]** (~X min) — [what you were doing]
If the results came from the wider 2-hour window, adjust the heading accordingly.
Find a person's repeated tasks from their screen activity, and what each one is worth automating, with the time and money saved.…
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