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 agentmods add skills/childrentime/reactuse/onboardnpx skills add childrentime/reactuse --skill onboardgit clone --depth 1 https://github.com/childrentime/reactuseWhat 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 | $0.00028 | $0.01742 |
| Opus 5 | $0.00014 | $0.00871 |
| Sonnet 5 | $0.00006 | $0.00348 |
| Haiku 4.5 | $0.00003 | $0.00174 |
Grade A, and why
onboard 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 2d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- onboard — 100% identical, 0 lines differ
- onboard — 100% identical, 0 lines differ
- onboard — 100% identical, 0 lines differ
- onboard — 100% identical, 0 lines differ
- i-onboard — 97% identical, 43 lines differ
- onboard — 94% identical, 13 lines differ
- onboard — 92% identical, 17 lines differ
- onboard — 92% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create or improve onboarding experiences that help users understand, adopt, and succeed with the product quickly.
Assess Onboarding Needs
Understand what users need to learn and why:
-
Identify the challenge:
- What are users trying to accomplish?
- What's confusing or unclear about current experience?
- Where do users get stuck or drop off?
- What's the "aha moment" we want users to reach?
-
Understand the users:
- What's their experience level? (Beginners, power users, mixed?)
- What's their motivation? (Excited and exploring? Required by work?)
- What's their time commitment? (5 minutes? 30 minutes?)
- What alternatives do they know? (Coming from competitor? New to category?)
-
Define success:
- What's the minimum users need to learn to be successful?
- What's the key action we want them to take? (First project? First invite?)
- How do we know onboarding worked? (Completion rate? Time to value?)
CRITICAL: Onboarding should get users to value as quickly as possible, not teach everything possible.
Onboarding Principles
Follow these core principles:
Show, Don't Tell
- Demonstrate with working examples, not just descriptions
- Provide real functionality in onboarding, not separate tutorial mode
- Use progressive disclosure - teach one thing at a time
Make It Optional (When Possible)
- Let experienced users skip onboarding
- Don't block access to product
- Provide "Skip" or "I'll explore on my own" options
Time to Value
- Get users to their "aha moment" ASAP
- Front-load most important concepts
- Teach 20% that delivers 80% of value
- Save advanced features for contextual discovery
Context Over Ceremony
- Teach features when users need them, not upfront
- Empty states are onboarding opportunities
- Tooltips and hints at point of use
Respect User Intelligence
- Don't patronize or over-explain
- Be concise and clear
- Assume users can figure out standard patterns
Design Onboarding Experiences
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.
- 2d ago First seen · 242 lines · 28 tokens per session scan A d3590b654e53
onboard is a skill published in the GitHub repository childrentime/reactuse (1,048 stars, last pushed 12d ago), licensed Unlicense. It adds 28 tokens to every session and 1,742 once invoked, about $0.0001 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 2026-08-30.
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