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WolfXL GTM Plan

Objective

Position WolfXL as the fastest credible path for existing Python Excel workflows by focusing on one wedge:

  • openpyxl-compatible API
  • reproducible performance + fidelity benchmarks
  • low migration friction

Core Strategy

T0 — Proof-First Developer GTM

  1. Publish reproducible benchmark methodology and raw results.
  2. Make migration friction near-zero (3-minute quickstart + import-swap examples).
  3. Recruit 5-10 design partners and produce outcome-backed case studies.
  4. Launch across developer channels with evidence-first messaging.

Why this strategy

For developer tools, trust beats reach. Adoption grows when claims are reproducible and switching risk is low.

Landing Page Draft

Hero

  • Headline: Make Excel automation faster without rewriting your code.
  • Subheadline: WolfXL is an openpyxl-compatible engine backed by Rust, built for high-fidelity Excel reads/writes with reproducible benchmarks.
  • CTA 1: Get Started in 3 Minutes
  • CTA 2: See Reproducible Benchmarks

Proof Strip

  • Openpyxl-compatible API
  • Reproducible benchmark suite
  • Public fidelity + performance tracking

How It Works

  1. Swap imports.
  2. Run your workflow (read/write/modify).
  3. Measure impact on your files.

Trust Section

  • No black-box claims (methodology + commands + fixtures published)
  • Known limitations documented
  • Compatibility path for legacy excelbench_rust imports

Install + Migration Snippets

pip install wolfxl
# before
from openpyxl import load_workbook

# after
from wolfxl import load_workbook

Launch Post Drafts

Post A (X / LinkedIn)

We just launched WolfXL: a Rust-backed, openpyxl-compatible Excel engine focused on speed + fidelity.

What it is:
- Drop-in style API for common openpyxl workflows
- Reproducible benchmark methodology (not cherry-picked screenshots)
- Public results + known limitations

Why we built it:
Excel automation at scale can get painfully slow, and rewrites are expensive.
WolfXL aims to reduce runtime without forcing teams to rebuild pipeline logic.

Try it:
[GitHub URL]
[PyPI URL]
[Benchmark methodology URL]

Post B (HN / Reddit style)

Show HN: WolfXL — openpyxl-compatible Excel I/O with reproducible fidelity + performance benchmarks

I built WolfXL to speed up Python Excel workloads without requiring a new programming model.

What’s different:
- API designed to feel familiar for openpyxl users
- Benchmark results are reproducible (fixtures, commands, hardware context disclosed)
- Compatibility transition path included

I’d love feedback on:
1) Real-world files where performance regresses
2) Fidelity edge cases we should prioritize
3) Missing migration docs/examples that would block adoption

Repo + docs:
[GitHub URL]

Design Partner Outreach Template

Subject: Can we benchmark your Excel pipeline with WolfXL? (hands-on support)

Hey [Name] — I’m building WolfXL, a faster openpyxl-compatible Excel engine.

I’m onboarding a small set of design partners and can help migrate one real workflow end-to-end.
Goal: measurable runtime improvement without changing your business logic.

What I’m asking:
- 1 representative workbook/pipeline
- 30–45 min kickoff
- feedback on migration friction + correctness

What you get:
- before/after benchmark report on your workload
- direct support on migration issues
- priority fixes for blockers you hit

If useful, I can send a 1-page technical brief and proposed test plan.

— [Your Name]

30-Day Execution Plan

Week 1 — Trust Foundation

  • Publish benchmark methodology page.
  • Publish known limitations + compatibility notes.
  • Finalize 3-minute quickstart.

Week 2 — Activation Assets

  • Publish 3 runnable examples:
    • read-heavy analytics
    • write-heavy reporting
    • modify-mode workflow
  • Publish comparison notebook/script.

Week 3 — Design Partner Sprint

  • Onboard 5-10 teams.
  • Capture before/after: runtime, memory, migration effort.

Week 4 — Public Launch

  • Publish launch thread + Show HN/Reddit post.
  • Publish first 2-3 case studies.
  • Share roadmap informed by partner feedback.

KPI Dashboard (Weeks 1-4)

North Star

  • Activated users/week (install + first successful workflow)

Acquisition

  • PyPI installs/week
  • GitHub stars/week
  • Landing page -> install conversion

Activation

  • % first successful read
  • % first successful write/save
  • Median time-to-first-success

Retention

  • 7-day retention
  • 28-day retention
  • Active design partners after week 2

Trust/Quality

  • Reported fidelity issues/week
  • Reported perf regressions/week
  • Independent public validation posts

Launch Week Checklist (Operational)

Day 1

  • Ship methodology + limitations pages.
  • Verify all benchmark commands run clean from a fresh environment.

Day 2

  • Publish quickstart and migration examples.
  • Internal dry run of partner onboarding flow.

Day 3

  • Send partner outreach to first 15 targets.
  • Schedule 5 calls.

Day 4

  • Publish social launch post.
  • Publish Show HN / Reddit post.

Day 5

  • Aggregate feedback.
  • Prioritize top migration blockers and correctness gaps.

Messaging Guardrails

  • Never claim universal speedups; always include context + reproducible commands.
  • Lead with compatibility + correctness first, speed second.
  • Explicitly document where WolfXL is not faster or not yet feature-complete.

Immediate Next Steps

  1. Replace placeholders ([GitHub URL], [PyPI URL], [Benchmark methodology URL]).
  2. Publish benchmark methodology + limitations pages before external posts.
  3. Start outreach with a shortlist of 15 high-fit design partner prospects.