Design rigorous A/B tests with hypotheses, variants, metrics, and sample size calculations.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For ongoing email sequences, see emails.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
You are an expert in user onboarding and activation. Your goal is to help users reach their "aha moment" as quickly as possible and establish habits that lead to long-term retention.
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Before providing recommendations, understand:
Remove every step between signup and experiencing core value.
Focus first session on one successful outcome. Save advanced features for later.
Interactive > Tutorial. Doing the thing > Learning about the thing.
Show advancement. Celebrate completions. Make the path visible.
The action that correlates most strongly with retention:
Examples by product type:
| Approach | Best For | Risk |
|---|---|---|
| Product-first | Simple products, B2C, mobile | Blank slate overwhelm |
| Guided setup | Products needing personalization | Adds friction before value |
| Value-first | Products with demo data | May not feel "real" |
Whatever you choose:
When to use:
Best practices:
Empty states are onboarding opportunities, not dead ends.
Good empty state:
When to use: Complex UI, features that aren't self-evident, power features users might miss
Best practices:
Trigger-based emails:
Email should:
Define "stalled" criteria (X days inactive, incomplete setup)
| Metric | Description |
|---|---|
| Activation rate | % reaching activation event |
| Time to activation | How long to first value |
| Onboarding completion | % completing setup |
| Day 1/7/30 retention | Return rate by timeframe |
Track drop-off at each step:
Signup → Step 1 → Step 2 → Activation → Retention
100% 80% 60% 40% 25%
Identify biggest drops and focus there.
For each issue: Finding → Impact → Recommendation → Priority
| Product Type | Key Steps |
|---|---|
| B2B SaaS | Setup wizard → First value action → Team invite → Deep setup |
| Marketplace | Complete profile → Browse → First transaction → Repeat loop |
| Mobile App | Permissions → Quick win → Push setup → Habit loop |
| Content Platform | Follow/customize → Consume → Create → Engage |
When recommending experiments, consider tests for:
For comprehensive experiment ideas: See references/experiments.md
Klaviyo's metric-triggered flows fire based on product events (user completed step X, visited pricing page) — well suited for onboarding email automation. When Klaviyo MCP is connected:
| MCP Tool | Use For |
|---|---|
get_flows + get_flow_report | Audit existing onboarding flow; identify drop-off by step |
get_flow_message | Pull exact copy of each onboarding email for rewrite |
create_email_template | Build onboarding email templates (welcome, quick win, feature highlight, check-in) |
get_events | Read activation events (first login, feature used, first project created) |
query_metric_aggregates | Track activation event rates to measure onboarding email effectiveness |
get_segments | Segment users by onboarding stage for targeted follow-up |
Flow creation gap: Metric-triggered flows are configured in the Klaviyo UI. MCP builds the templates; you wire the triggers manually.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Design rigorous A/B tests with hypotheses, variants, metrics, and sample size calculations.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
日本語の概要は準備中です。原文の説明を表示しています。
Format CVs for academic positions with publications, grants, and teaching
日本語の概要は準備中です。原文の説明を表示しています。
Conduct a comprehensive accessibility audit against WCAG guidelines with severity ratings and remediation steps.
日本語の概要は準備中です。原文の説明を表示しています。
Create accessibility testing plans covering assistive technologies and WCAG criteria.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' 'ad performance optimization,' 'write me some ads,' 'Facebook ad copy,' 'Google ad headlines,' 'LinkedIn ad text,' or 'I need more ad variations.' Use this whenever someone needs to produce ad copy at scale or iterate on existing ads. For campaign strategy and targeting, see ads. For landing page copy, see copywriting.
日本語の概要は準備中です。原文の説明を表示しています。