feat(claude): add gather-recent-context and sync-twenty commands, remove deprecated gmail-sync and notion-sync

.claude/skills/upskill/SKILL.md

@@ -54,7 +54,7 @@ Extract required and preferred technical skills from each job source:
 ### Aggregate mode
 This mode now merges two sources — tracker rows (Step 2.1) and ranked postings from `seen_jobs.json` (Step 2.3) — so the same job is never double-counted and recorded gaps are preferred over inferred ones:
 
-1. **Dedupe.** Match tracker rows against ranked entries on case-insensitive company + role (casefold + strip on both fields) — the same match `/notion-sync`'s Step 2 describes. A job present in both counts once.
+1. **Dedupe.** Match tracker rows against ranked entries on case-insensitive company + role (casefold + strip on both fields) — the same match `/sync-twenty`'s Step 2 describes. A job present in both counts once.
 2. **Recorded gaps beat inferred skills.** For any job that has a recorded `gaps` array (from a ranked entry, or from a tracker row that matched one), use those gap bullets directly as the skill list for that job instead of inferring from `role`/`sector`/`notes`. For a ranked-only job with no `gaps` (already skipped and counted in Step 2.3) or a tracker-only row, fall back to inferring likely required skills from `role`, `sector`, and `notes` — optionally WebFetch the row's `source` URL for more detail, but skip if the URL is missing or dead.
 3. **One weight per job**, both 0–100 on the same scale: `(100 - fit_rating) / 100` for tracker rows, `(100 - rank_score) / 100` for ranked-only rows. If a job is in both (Step 3.1 matched it), prefer the tracker's numeric `fit_rating` for the weight. A **blank or non-numeric `fit_rating`** (rows `/outcome` creates for applications made outside the workflow never got a fit evaluation) contributes no weight: fall back to a matched ranked entry's `rank_score` when Step 3.1 found one, otherwise skip the row, count it, and report the count once in the terminal — the same treatment Step 2.3 gives a missing `gaps` field, and for the same reason. Never treat a blank as 0: that reads as weight 1.0, the maximum, and lets the one job the framework knows nothing about dominate the heatmap.
 4. **Score.** Build a **skill frequency map**: for each extracted skill (recorded gap bullet or inferred skill), count how many jobs mention it, then multiply each job's contribution by its weight from Step 3.3. Track whether each contribution came from a recorded gap or an inferred one, for Step 5's provenance column.