| name | dayflow-pull |
| description | Pull Henry's Dayflow activity data (what he actually worked on, day by day, card by card, with accurate hours) straight from Dayflow's local SQLite DB — read-only. Use whenever Henry asks to check his Dayflow, compute real work hours, build a weekly/daily report of what he did, verify time spent on a project, or audit his activity. Computes overlap-merged wall-clock hours (Dayflow's own sums double-count) and flags corrupt cards instead of trusting the LLM summaries at face value. |
dayflow-pull
Read Henry's Dayflow data directly from its structured store — do not eyeball
the app or trust Dayflow's own hour totals. Dayflow is a screen-recording time
tracker whose cards are LLM/OCR-generated, so two things must be handled:
- Overlapping cards — Dayflow sometimes emits duplicate/overlapping cards
for the same span (esp. after re-analysis).
SUM(end_ts - start_ts)
double-counts them. Always interval-merge for real wall-clock hours.
- Corrupt timestamps — occasional cards span days (a "System" card of
1400+ hours was observed). Exclude any single card longer than ~3h and flag it.
How to use
Run the helper (read-only; safe while Dayflow is running):
python3 ~/.claude/skills/dayflow-pull/pull.py
python3 ~/.claude/skills/dayflow-pull/pull.py --from 2026-07-08 --to 2026-07-14
python3 ~/.claude/skills/dayflow-pull/pull.py --day 2026-07-13
python3 ~/.claude/skills/dayflow-pull/pull.py --category all
python3 ~/.claude/skills/dayflow-pull/pull.py --obs
Categories: Work (default), Personal, Distraction, Idle, System, all.
Data model (for ad-hoc queries)
DB: ~/Library/Application Support/Dayflow/chunks.sqlite — open mode=ro only.
timeline_cards — the summary cards: day, start_ts/end_ts (unix),
title, summary, detailed_summary, category, subcategory, metadata
(JSON incl. appSites), is_deleted (filter =0).observations — the finer, rawer layer beneath the cards (per-batch),
joined via batch_id; use to sanity-check what a card claims vs. the
underlying evidence (llm_model records which model produced it).llm_calls — Dayflow's own LLM invocations, if you need to audit how a card
was derived (the "OCR-LLM might be wrong" check).journal_entries, day_goals, daily_standup_entries — journal/goals.
Rules
- Read-only, always.
sqlite3 -readonly / ?mode=ro. Never write; the DB is live.
- Report merged hours, not summed. State when you excluded glitch cards.
- Read the cards, don't hand-wave. Cite actual card titles/times, and drop to
observations when a card's claim looks off — that's the point of reading the
structured data instead of guessing from a summary.
