Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add liqiongyu/lenny_skills_plus --skill user-onboardinggit clone --depth 1 https://github.com/liqiongyu/lenny_skills_plusWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/user-onboarding)<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/user-onboarding"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/user-onboarding/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/user-onboarding"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/user-onboarding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00032 | $0.02328 |
| Opus 5 | $0.00016 | $0.01164 |
| Sonnet 5 | $0.00006 | $0.00466 |
| Haiku 4.5 | $0.00003 | $0.00233 |
Grade A, and why
user-onboarding scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Onboarding
Scope
Covers
- Designing the first-time user experience (FTUE) from entry/signup → first value
- Defining activation / the “aha moment” and reducing time-to-value
- Creating a “first 30 seconds” experience and a “first mile” plan (milestones to activation)
- Onboarding mechanics: progressive disclosure, guided setup, empty states, templates, checklists, feedback loops
- Instrumentation + experiments to improve activation and early retention
When to use
- “Activation is low” / “users drop during signup/onboarding”
- “Time-to-value is too long”
- “The first session doesn’t feel magical”
- “We need an onboarding redesign / guided setup / empty state improvements”
- “Define our aha moment and design onboarding to get users there”
- “Create an onboarding experiment backlog + measurement plan”
When NOT to use
- You’re onboarding employees, customers to a service process, or running training -- not product onboarding (use
onboarding-new-hiresfor employee onboarding). - You don’t have a stable value proposition / ICP (use
problem-definitionormeasuring-product-market-fitfirst). - You need a full retention strategy beyond onboarding (use
retention-engagement); this skill covers signup-to-activation, not post-activation lifecycle retention. - You need to validate a prototype with real users (use
usability-testingafter creating a plan here). - You need to design viral/referral loops or acquisition flywheels (use
designing-growth-loops); this skill is about converting new users, not acquiring them. - You want to apply behavioral science frameworks (habit loops, nudge architecture) as the primary design lens (use
behavioral-product-design); this skill uses behavioral insights but is structured around the onboarding journey, not around psychological theory.
Inputs
Minimum required
- Product + ICP/segment(s) (1–2) and the primary job-to-be-done
- Platform + entry point (web/mobile; where FTUE starts)
- Best-available baseline metrics (even rough):
- visit → signup → first key action → activation
- time-to-value (time or sessions to first value)
- D1 retention (if available)
- Current onboarding flow summary (steps/screens) and the biggest drop-off point(s)
- Constraints: timebox, eng/design capacity, allowed channels (in-app/email/push), privacy/legal/brand limits
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- eval/eval_config.json 1.3 KB
- eval/SHOWCASE.md 5.1 KB
- eval/with_skill.md 44 KB
- eval/without_skill.md 22 KB
- README.md 1.7 KB
- references/CHECKLISTS.md 1.6 KB
- references/EXAMPLES.md 1.5 KB
- references/INTAKE.md 2.1 KB
- references/RUBRIC.md 4.3 KB
- references/SOURCE_SUMMARY.md 2.3 KB
- references/TEMPLATES.md 4.1 KB
- references/WORKFLOW.md 1.8 KB
- skillpack.json 357 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 149 lines · 32 tokens per session scan A deddba2e2890
user-onboarding is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,328 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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