feat: Tier B operator scaffolding — bundle, copy SoT, posts, emails

Pick up and finish yesterday's cut-off Tier B pass.

- build/: PyInstaller scaffold (datatools.spec + launcher.py +
  hook-streamlit.py + README) — folder-mode bundle, locked
  127.0.0.1, per-OS recipe
- marketing/COPY.md: single source of truth for every customer-facing
  string — landing H1/sub/CTAs, demo CTAs, email subjects, Gumroad
  listing, banned phrases
- marketing/community-posts/: 9 drafts (3 posts × 3 niches:
  bookkeeper, revops, shopify-pet) — story / tip / soft-offer
- marketing/emails/: 18 drafts (Gumroad delivery + 5-touch
  onboarding × 3 niches), per-niche segmentation guidance
- docs/NEXT-STEPS.md: flip 2.2 / 2.4 / 3.1 / 3.4 to done with
  pointers to the new assets; add Phase 0 inventory rows
- .gitignore: narrow `build/` ignore so PyInstaller spec + launcher
  + hooks get tracked, only generated artifacts (build/build/,
  build/__pycache__/, build/dist/) stay ignored

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

marketing/emails/revops/04-day14.md

@@ -0,0 +1,34 @@
+# RevOps · Day 14 — Two-minute trick: the confidence tiers
+
+**Subject:** Two-minute trick: the confidence tiers
+**Send:** Day 14
+**Goal:** surface the manual-review queue — non-obvious, high-value
+
+---
+
+Hi {{first_name}},
+
+The single most-skipped feature in DataTools is also the one with the highest payoff per minute: the **manual-review queue**.
+
+Here's what's happening under the hood: every dedupe decision DataTools makes has a confidence score (0.0 to 1.0). The dedupe tool by default puts decisions into three buckets:
+
+- **≥0.95** → auto-merge (cleaned CSV)
+- **0.85 - 0.95** → manual-review queue (`<filename>.review.csv`)
+- **<0.85** → unmerged (kept as separate rows)
+
+The 0.85-0.95 bucket is the magic. It's the range where a tuned algorithm catches *most* duplicates but where the wrong choice is a real cost (merging two genuinely different people = lost prospect; not merging two duplicates = paid contact you didn't need).
+
+The 2-minute workflow:
+
+1. Run dedupe.
+2. Open `<filename>.review.csv`. Each row is a candidate merge with: confidence, the two records side-by-side, the rule that fired.
+3. Eyeball each row. Mark `keep_merge` (Y/N) in the rightmost column.
+4. Re-run dedupe with the `--apply-review-decisions <filename>.review.csv` flag (or click "Apply review decisions" in the GUI).
+5. Final cleaned CSV reflects your manual choices.
+
+For a 5,000-row lead list, the review queue is typically 20-60 rows. ~3 minutes of work. The output is dramatically better than auto-merge-everything-≥0.85, which is what most tools (including HubSpot's) do silently.
+
+**Pro move:** save your `keep_merge` decisions over time. After 3-4 campaigns you'll have a corpus of "yes-merges" and "no-merges" you can use to retune the auto-merge threshold for *your* data. Most teams find their sweet spot is somewhere in 0.88-0.92.
+
+— Michael
+{{support_email}}