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/community-posts/revops/01-story.md

@@ -0,0 +1,39 @@
+# RevOps · Post 1 — Story
+
+**Where to post:** r/revops, r/sales, RevGenius Slack, Modern Sales Pros,
+Pavilion communities, LinkedIn (your own feed).
+
+**Format:** ~400 words. Tactical war-story style. Don't pitch in the body.
+
+---
+
+## Title
+
+We were paying HubSpot for 4,200 duplicate contacts. Here's the dedupe pipeline that caught them.
+
+## Body
+
+Last quarter I ran a count on our HubSpot instance: ~4,200 contacts that were almost-certainly the same person as another contact already in the system. Our HubSpot bill is per-marketing-contact, so this was a real number. ($X/month — pick your tier.)
+
+The problem is that HubSpot's native "find duplicates" tool is exact-match-only on a small set of fields. It misses:
+
+- "Sarah O'Brien" vs "Sarah Obrien" (apostrophe / no-apostrophe)
+- "+1 (415) 555-0143" vs "415-555-0143" vs "4155550143" (phone formats)
+- "sarah@acme.com" vs "Sarah@acme.com" (case)
+- Same person from a LinkedIn scrape (no phone) + a webform fill (no LinkedIn URL) + a trade-show import (only email + company)
+
+Here's the 4-step pipeline I run before *every* HubSpot import now. You can build the first 3 with Python + pandas + rapidfuzz; the 4th is the one that matters and is the easiest to skip:
+
+**Step 1 — Normalize before comparing.** Lowercase emails, strip phone formatting to E.164, trim whitespace, normalize unicode (NFKC). This alone catches ~40% of dupes.
+
+**Step 2 — Fuzzy-match on name + company, blocked by email domain.** Don't fuzzy-match across the whole list (O(n²) and full of false positives). Block by email domain first — only compare contacts within the same company. Use rapidfuzz token-set ratio at threshold 85.
+
+**Step 3 — Cross-source merge logic.** When LinkedIn-source and webform-source records match, *the LinkedIn one wins on title/company* (more recent), *the webform one wins on phone/email* (verified). Document this rule somewhere your team can read it.
+
+**Step 4 — Confidence tiers, not yes/no.** Don't auto-merge anything below 95% confidence. Auto-merge 95-100. Queue 85-95 for manual review. Drop everything below 85. The manual queue is the magic — it catches the cases the algorithm doesn't dare touch and trains you on what your data actually looks like.
+
+I eventually wrapped all this into a desktop tool I called DataTools because I got tired of re-running the script every campaign. Local-only, $49 if anyone wants it: datatools.app/revops. But the 4-step framework above is the real takeaway — works regardless of what tool you use.
+
+What's your dedupe pipeline look like?
+
+— {{your-name}}

marketing/community-posts/revops/02-tip.md

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+# RevOps · Post 2 — Tip
+
+**Where to post:** LinkedIn, RevGenius Slack #tips channel,
+RevOps Co-op, Modern Sales Pros.
+
+**Format:** ~150 words. Tactical. One idea, one sentence-of-pitch
+at the bottom.
+
+---
+
+## Title
+
+The 30-second pre-import check that catches LinkedIn-scrape duplicates before they hit HubSpot
+
+## Body
+
+Before you import a LinkedIn scrape (Apollo, Lusha, Cognism — same problem) into HubSpot:
+
+Open the file. Sort by `email`. Look for blanks.
+
+LinkedIn-sourced rows often have *no email* — just name + company + LinkedIn URL. If you import them as-is, HubSpot creates a new contact for each one. The next time someone fills your webform with the same name + company, HubSpot creates *another* new contact, because there's no key to match on.
+
+Two-minute fix: before import, generate a synthetic dedupe key as `lower(first_name)|lower(last_name)|domain(company_url)`. Sort by it. Anything with >1 row is a likely dupe — review and merge before HubSpot ever sees it.
+
+If you're doing this monthly across multiple lead sources and want to automate it (plus phone normalization, fuzzy matching, the whole pipeline), I built a $49 desktop tool: datatools.app/revops. Local — your prospect list never goes to a server.
+
+— {{your-name}}

marketing/community-posts/revops/03-soft-offer.md

@@ -0,0 +1,35 @@
+# RevOps · Post 3 — Soft offer
+
+**Where to post:** IndieHackers, r/revops monthly self-promo,
+RevGenius #tools-and-software, LinkedIn (your own feed).
+
+**Format:** ~250 words.
+
+---
+
+## Title
+
+DataTools — a $49 desktop CSV pipeline for the lead-list cleanup you do before every HubSpot import
+
+## Body
+
+Built this for myself first. {{your-context — e.g., "I run RevOps at a 30-person SaaS"}} and the part of the job I dreaded was the pre-import scrub: LinkedIn export + Apollo pull + last quarter's webform list, deduped against each other and against what's already in HubSpot. Six tabs in a Google Sheet, regexes I half-remember, vlookups, an hour and a half.
+
+**DataTools** does the six steps as one pipeline:
+
+- **Format standardizer** — phones to E.164 (50+ country codes, per-row country awareness), emails lowercased, URLs canonicalized
+- **Dedupe** — fuzzy matching with confidence tiers (95+ auto, 85-95 manual queue, <85 dropped), blocked by email domain so it scales to 50k-row lists
+- **Gate** — block bad rows from your import with a per-rule report ("142 rows missing email, 38 rows with malformed phones, 12 rows with corporate-blacklist domains")
+- **Text cleaner** — strips hidden chars, BOMs, weird unicode
+- **Analyzer** — finds problems before you process (mixed encodings, inconsistent delimiters, near-duplicate rows)
+- **Splitter** — chunk huge files for tools with row limits
+
+Runs **locally** — Mac/Win/Linux. Your prospect data never goes to a server. (This was the actual reason I shipped it instead of using Clearbit / cloud tools — legal didn't want third-party touching prospect data after the {{2024 / 2025}} compliance review.)
+
+**$49 one-time.** No subscription. No per-record fee. v1.x updates included.
+
+Demo (with synthetic data) and download: datatools.app/revops
+
+Happy to answer questions in the thread.
+
+— {{your-name}}