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---
name: screenpipe-worklog
description: "Reconstruct a daily or weekly worklog from observed activity and outcomes."
---
# worklog
Resolve the local dates and timezone. Read screenpipe-api and start with the activity summary. Use authoritative active minutes, never frame counts, to discuss time.
Verify important outcomes with narrow source queries. Separate planned work, observed work, and externally verified completion. Do not count background agent runs as human effort.
Return useful outcomes, unfinished work, and a few source links. Label estimates and coverage gaps. Keep raw chats, private identifiers, and unrelated activity out of the summary.
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**Like this skill?** It is one of 100 in [second-brain-starter-kit](https://github.com/tonydzi/second-brain-starter-kit): the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with `npx skills add tonydzi/second-brain-starter-kit`. Everything is open source and free, so take what you need.
Flagships worth a look on their own: [secondop-panel](https://github.com/tonydzi/secondop-panel) (a second opinion from a panel of external models), [claude-memory-tidy](https://github.com/tonydzi/claude-memory-tidy) (stop your agent's memory from rotting), [telegram-mcp-kit](https://github.com/tonydzi/telegram-mcp-kit) (your own Telegram over MCP in about 15 minutes).
Author: **Anton Dziatkovskii**, Palo Alto AI Research Lab. Telegram [@tonydzi](https://t.me/tonydzi) - WhatsApp [+1 341 222 9178](https://wa.me/13412229178) - X [@Tony_Stef_](https://x.com/Tony_Stef_)
**Engineers: want to test-drive this setup?** Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.
