Borrowing it
Nothing to install: this file belongs to lanesket/llm.log. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lanesket/llm.log/main/.agents/skills/onboard/SKILL.mdgit clone --depth 1 https://github.com/lanesket/llm.logWrote 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/lanesket/llm.log/onboard)<a href="https://agentmods.dev/skills/lanesket/llm.log/onboard"><img src="https://agentmods.dev/badge/skills/lanesket/llm.log/onboard.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.01817 |
| Opus 5 | $0.00014 | $0.00908 |
| Sonnet 5 | $0.00006 | $0.00363 |
| Haiku 4.5 | $0.00003 | $0.00182 |
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 8d 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.
This is a copy
89% identical to onboard — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Use the frontend-design skill — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run teach-impeccable first. Additionally gather: the "aha moment" you want users to reach, and users' experience level.
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
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.
- 8d ago First seen · 248 lines · 28 tokens per session scan A 32da37662d42
onboard is a skill published in the GitHub repository lanesket/llm.log (22 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 1,817 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to onboard, differing in 13 lines, and is treated as a copy.
Other skills, from other repositories
qa-design
UI/UX design audit and verification of web best practices, including responsive/mobile-first breakpoints. Trigger when the user wants to audit the design, verify the UI/UX, check responsive behaviour, or improve the user interface.
dev-frontend-design
Distinctive UI design with strong art direction. Trigger when the user wants to create an interface, a landing page, a visual component, or when frontend code creation is detected without a defined design direction.
dev-shadcn
Integration and customization of shadcn/ui (copy-paste React components, Radix + Tailwind). Trigger when the user wants to install shadcn, add components, customize the theme, or when shadcn/ui usage is detected in the project.
options
Present multiple design options as a vertical stack of anchored turns.
qa-test-planner
Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.
visualize
Render a polished visual inline in the chat as part of your answer — a diagram, a chart, an interactive explainer, or a UI mockup. Load it proactively whenever an explanation would land better as a picture than as prose. Do not wait to be asked.