onboarding

Onboarding

Quick Start

Use this skill for onboarding. Start from the user’s concrete objective and available evidence; do not substitute generic marketing advice for task-specific analysis.

Skill Contract

• Reads: user-provided context; relevant project files; .agents/product-marketing.md when present; approved public or connected data sources.
• Writes: recommendations and artifacts in the response by default. Persistent file/account changes require explicit request or authorization.
• Evidence: label consequential claims as measured, user-provided, calculated, estimated, or proxy. Never upgrade uncertainty silently.
• Side effects: do not publish, send, spend, delete, mutate accounts, or persist registry truth without user authorization.
• Freshness: verify current platform rules, search eligibility, ad policies, model/tool capabilities, laws, pricing, and other time-sensitive claims before acting.

Instructions

1. Define the exact onboarding objective, audience/scope, constraints and success metric before recommending action.
2. Load shared product-marketing context when it materially changes the answer; ask only for missing facts that block a decision.
3. Collect the minimum evidence needed for onboarding. Distinguish direct observations from assumptions and proxies.
4. Execute the onboarding analysis or artifact using the domain checklist below; prefer specific outputs over generic best-practice lists.
5. Prioritize actions by impact, confidence, effort and dependency. Identify what would falsify important assumptions.
6. For external side effects, publishing, sending, spend changes, account changes or persistent writes, obtain authorization first.
7. Finish with decision-ready output, evidence labels, open loops, and no more than three next-best skills.

Activation without leakage

Define signup time zero, an early activation window, and a distinct later retention outcome. Use all eligible signups with full follow-up; immature cohorts remain pending. Exclude actions from the outcome window and post-outcome features. Treat correlated actions as hypotheses, validate on later mature cohorts, and test whether an intervention improves retention rather than claiming that forcing a correlated action causes it.