fix(upskill,notion-sync): join tracker rows to ranked entries on title, the field entries carry (#614)

Both specs said to match "on company + role" with "casefold + strip on
both fields", but a seen_jobs.json entry has title and no role key (the
job-scraper schema), so read literally the lookup never matched: /upskill
then weighted the same job twice, once as a tracker row and once as a
ranked entry - the double-count its dedupe step exists to prevent - and
/notion-sync split one job into two rows with different Keys.
tools/rank_state.py already does the cross-name join (tracker role
against entry title); both specs now say so.

Two derived tests read the entry schema and require the join wording to
name title for the entry and role for the tracker; they fail on master.

.claude/commands/notion-sync.md

@@ -40,7 +40,7 @@ Validate the cheap, local precondition before creating anything external. A run
 
 1. Read `job_scraper/seen_jobs.json` and `job_search_tracker.csv` (either may be missing).
 2. Select `seen_jobs.json` entries with status `ranked` whose `rank_score` meets the threshold from Step 0. `--all` lifts the threshold entirely.
-3. Every tracker row joins the sync set (an applied-to job always syncs, ranked or not), matched to `seen_jobs.json` entries case-insensitively on company + role where possible. Tracker rows with no `seen_jobs.json` entry sync too - build their Key as `<company>_<role>` lowercased with underscores.
+3. Every tracker row joins the sync set (an applied-to job always syncs, ranked or not), matched to `seen_jobs.json` entries case-insensitively on company + title where possible (the tracker's `role` column against the entry's `title` field — entries carry `title`, not `role`). Tracker rows with no `seen_jobs.json` entry sync too - build their Key as `<company>_<role>` lowercased with underscores.
 4. **Status precedence:** the tracker wins. A job that is `ranked` in `seen_jobs.json` but `interview` in the tracker syncs as `interview`. Jobs only in `seen_jobs.json` keep their stored status. **Deadline precedence: the tracker wins too** - the tracker's `deadline` (written by `/apply` from the posting the application was actually built on) overrides the `seen_jobs.json` value; jobs only in `seen_jobs.json` keep the scraper's stored deadline. Omit the property when neither states one, and **never reconcile the two by picking the earlier or later date** - both were read from the posting at different times, and the safe-looking `min()` substitutes a date the user never applied against.
 5. **If the sync set is empty** (no ranked entries meet the threshold and there are no tracker rows), say "Nothing to sync - run `/scrape` and `/rank` first" (or, when jobs exist but all score below the threshold, say so and suggest `--min-score`/`--all`) and **stop**.
 6. State the counts before touching the destination: how many rows will be created or checked, and the threshold in effect.

.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 + title: the tracker's `role` column against the entry's `title` field (`seen_jobs.json` entries carry `title`, not `role`), casefold + strip on both sides — the same match `/notion-sync`'s Step 2 describes and `tools/rank_state.py` implements for the tracker exclusion. 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.