claude-lifecycle

Lifecycle marketing and CRM strategy as a Claude Code skill: customer journey design, behavioral segmentation, lifecycle messaging and channel copy for email, push, SMS, in-app and WhatsApp.

Journeys are a function of your data: most tools pretend otherwise. A store tracking add_to_cart → purchase can run a branched 8-step cart recovery; a startup with three tracked events cannot. claude-lifecycle is a Claude Code plugin that scores what your data actually supports first, then generates a portfolio of customer journeys (onboarding and activation, retention, churn prevention, win-back), plus rule-checked, sector-aware CRM copy for every step, sized to that reality instead of a template.

Live output from the zero-install demo: every card, color, and number on this canvas comes from the sample dataset, not a mockup.

Why this exists

Every lifecycle tool ships the same five template flows, regardless of what data backs them. Handing a startup with three tracked events the same branching journey as a mature e-commerce store produces automations that can neither trigger nor be measured. This engine makes that constraint explicit instead of hiding it:

• Data quality is scored, not assumed. A 0–100 Data Quality Score decides whether you get 3-step time-based flows or 10+ step behavioral branching.
• Journeys are a portfolio, not a listicle. Eligibility is computed per pattern from required-event signatures; what your data can't support becomes a tracking plan telling you exactly which events unlock which journeys.
• Copy is an engineered artifact. Channel files carry hard limits and banned words; sector lexicons decide vocabulary; a reviewer agent adversarially checks every message before you see it.

Questions this answers

• Which lifecycle journeys can my analytics data actually support today?
• What events do I need to start tracking before cart recovery, win-back or replenishment can work at all?
• How deep should a journey be — three time-based steps, or ten with behavioural branching?
• What belongs in a welcome, onboarding, retention, churn-prevention or win-back flow for my sector?
• How do I write CRM copy that respects each channel's character limits, banned words, consent rules and quiet hours?
• How do I measure a lifecycle journey honestly, holdout group included?
• How do I turn a journey's audience into a BigQuery query or a CDP trait?
• We are switching CRM tools — how do I describe our journeys in a way that survives the move?

How it works

Full walkthrough with design decisions: docs/architecture.md

Zero-install demo: the two HTML deliverables, rendered with sample data: journey canvas · channel copy canvas

Quickstart

# as a Claude Code plugin (marketplace or local)
/plugin install claude-lifecycle

# or clone and use as a project
git clone https://github.com/ali-demirbas/claude-lifecycle && cd claude-lifecycle && claude

# or install individual skills with the skills CLI (https://skills.sh)
npx skills add ali-demirbas/claude-lifecycle --all

Already have ab-test-playbook too? Add claude-skills once instead of each repo separately: /plugin marketplace add ali-demirbas/claude-skills.

Using Gemini CLI instead? .gemini/extensions/claude-lifecycle/ ships the same skills, rules and agents, generated from the same source files by scripts/build_gemini.py:

git clone https://github.com/ali-demirbas/claude-lifecycle.git
cd claude-lifecycle/.gemini/extensions/claude-lifecycle && gemini extensions link .

Then, inside Claude Code:

/lifecycle connect          # score your data (GA4 via MCP, or point at a CSV)
/lifecycle journeys         # generate the portfolio
/lifecycle copy             # channel copy for the generated journeys

No data at all? "/lifecycle journeys, my sector is fintech, no data" works too: you get the sector playbook's priority journeys in their simple form, plus the tracking plan that upgrades them.

The three tiers

What's inside

Example output (excerpt)

A T1 e-commerce run produces a portfolio like:

…where every ✅ is a full journey doc (trigger, audience, exit criteria, step table, KPIs + holdout, Mermaid diagram) and every 🔒 lands in the tracking plan with the event that unlocks it. See examples/ecommerce-full-ga4/.

Design principles

1. One engine, data-driven sectors. No if industry == "fintech" in skills; sector behavior lives in playbook/lexicon files, so extending the engine is a content contribution.
2. Deterministic where it matters. Eligibility, prioritization, and depth follow written rules (DQS rubric); the model's creativity goes into copy and sequencing, not into deciding whether a journey is possible.
3. Honest by construction. No fabricated benchmarks, no fake urgency, no journeys pretending untracked events exist. The never-do lists in every skill are load-bearing.

Real-world validation

Beyond the eval suite, see docs/real-world-validation.md for what happened when someone ran this against a real product: what held up, and what gaps it surfaced that got folded back into the engine.

Contributing

New industries, patterns, and sharper channel rules are welcome; see CONTRIBUTING.md and docs/adding-an-industry.md. Run bash scripts/validate.sh before opening a PR.

License

MIT