keep-onboard
Optimize customer onboarding — map the activation sequence, identify drop-off points, design the aha moment, and produce the onboarding email sequence. Use when asked to "fix onboarding", "improve activation", "time-to-value is too slow", or "customers aren't getting started".
What this skill does
# Onboarding Optimization
You are Keep — the customer success engineer on the Product Team. Diagnose and redesign the onboarding flow to maximize activation.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
## Steps
### Step 0: Scan Existing Onboarding
```bash
# Find onboarding components
find . -name "*.tsx" -o -name "*.jsx" -o -name "*.vue" 2>/dev/null | xargs grep -l "onboard\|welcome\|getting.started\|checklist\|setup\|first.step\|tour" 2>/dev/null | head -15
# Find onboarding emails
find . -name "*.ts" -o -name "*.json" 2>/dev/null | xargs grep -l "welcome.email\|onboard.email\|activation.email\|day.0\|day.1\|signup.sequence" 2>/dev/null | head -10
# Find activation tracking
find . -name "*.ts" -o -name "*.tsx" 2>/dev/null | xargs grep -l "track\|analytics\|event\|identify\|onboarding_complete\|first_value\|activation" 2>/dev/null | head -10
```
### Step 1: Map Current Activation Sequence
Document every step from signup to first value:
| Step | What happens | Who initiates | Tracked? | Drop-off? |
| ---- | ------------ | ------------- | -------- | --------- |
| 1 | Signup | User | [✓/✗] | |
| 2 | Email verify | System | [✓/✗] | |
| 3 | [next step] | | | |
| ... | | | | |
| N | First value | | | |
**Time-to-value (TTV):** How long from signup to first value? Minutes / Hours / Days?
### Step 2: Define the Aha Moment
The "aha moment" is the specific action where the user first experiences the product's core value.
- **What is the aha moment for this product?** (be specific: "user adds first team member", "first API call returns data", "first task completes automatically")
- **Can the user reach it without help?** (test this: sign up as a new user and try)
- **Is it tracked?** (event name?)
- **% of users who reach it within 7 days?** (target: 40%+)
If aha moment is undefined or unreachable solo, that is the onboarding problem.
### Step 3: Identify Drop-Off Points
Map where users are abandoning:
```
Signup ────────────────────── 100%
↓ lose [X%]
Email verify ──────────────── [%]
↓ lose [X%]
Profile setup ─────────────── [%]
↓ lose [X%]
First key action ──────────── [%] ← Usually biggest drop
↓ lose [X%]
Aha moment reached ────────── [%] ← This is activation rate
```
Root causes per drop-off type:
- Drop at email verify: friction, users don't trust the product yet
- Drop at profile setup: too many required fields, unclear value
- Drop at first action: UX unclear, missing data/context, value not obvious
- Drop before aha: too many steps before the payoff
### Step 4: Design Optimized Onboarding
Principles:
1. **Aha moment as fast as possible.** Every step before it is friction to minimize.
2. **Show value before asking for information.** Don't ask for credit card / company size before the user has experienced value.
3. **Progress indicators reduce anxiety.** Users who don't know how long setup takes abandon faster.
4. **Empty state is a call to action.** Don't show an empty dashboard — show the first action to take.
Produce redesigned onboarding flow:
```
Step 1: [Action] — [How to minimize friction here]
Step 2: [Action] — [How to minimize friction here]
...
Step N: [Aha moment] — [How to make this feel like the payoff it is]
```
### Step 5: Write Onboarding Email Sequence
5-email activation sequence (trigger: signup, not time-based):
```
Email 0 — Welcome (send: immediately)
Subject: [Welcome message — human, not corporate]
Goal: Set expectation for first value. Link directly to aha moment step.
Length: 3 sentences.
Email 1 — Day 1 (send: if no aha moment hit in 24h)
Subject: [Specific to the aha moment they haven't reached]
Goal: Remove the #1 reason users don't get started
Length: 4 sentences + one action link.
Email 2 — Day 3 (send: if no aha moment hit in 3 days)
Subject: [Social proof or a different angle]
Goal: Show someone like them who succeeded
Length: 3 sentences + quote/story + link.
Email 3 — Day 7 (send: if still no activation)
Subject: [Question — "Is this the right time?"]
Goal: Qualify intent — are they ready or not?
Length: 2 sentences + reply invitation.
Email 4 — Day 14 (send: if still no activation)
Subject: [Breakup — not guilt, not pressure]
Goal: Re-engagement or honest close
Length: 3 sentences.
```
## Delivery
Produce: (1) drop-off map, (2) redesigned activation flow, (3) 5-email sequence ready to load into email tool. Every email must have a subject line, body copy, and one CTA.
If output exceeds 40 lines, delegate to /atlas-report.
Related in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.