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 arnabdeypolimi/claude_code_setup --skill onboardgit clone --depth 1 https://github.com/arnabdeypolimi/claude_code_setupWrote 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/arnabdeypolimi/claude_code_setup/onboard)<a href="https://agentmods.dev/skills/arnabdeypolimi/claude_code_setup/onboard"><img src="https://agentmods.dev/badge/skills/arnabdeypolimi/claude_code_setup/onboard/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/arnabdeypolimi/claude_code_setup/onboard"><img src="https://agentmods.dev/badge/skills/arnabdeypolimi/claude_code_setup/onboard.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 | $0.00048 | $0.01818 |
| Opus 5.5 | $0.00019 | $0.00727 |
| Sonnet 5.5 | $0.00010 | $0.00364 |
| Haiku 4.5 | $0.00005 | $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 1mo 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
98% identical to onboard — 6 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke /frontend-design — 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.
- 1mo ago First seen · 245 lines · 48 tokens per session scan A cd5ec9f3b925
onboard is a skill published in the GitHub repository arnabdeypolimi/claude_code_setup (4 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,818 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to onboard, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
brainstorming
Design discovery process that scales with task size and user tier. Prevents wasted work on wrong assumptions while staying out of the way for small tasks. Enforces design-before-code discipline with architecture-aware constraints.
accessibility
WCAG 2.1 AA baseline — semantic HTML, ARIA labels, color contrast, keyboard navigation, screen reader compatibility, and focus management.
figma-verification
Extract Figma design specs before implementing UI components. Use depth 5, cache to disk, extract ALL properties, map to design system tokens.
interaction-design
Use when designing how a UI feels to use. Covers audience dimensions, discoverability, mode communication, keyboard-first UX, feedback timing, empty states, and information density. Includes WCAG 2.1 AA compliance audit. Companion to visual-design.
figma-extract
Exhaustive Figma node extraction: download every detail down to leaf nodes and cache everything locally. Call Figma once, never twice. Use before any pixel-perfect implementation. Prioritizes complete local dumps over repeated API calls.
visual-design
Audit any application's visual identity and generate a distinctive direction that passes the AI slop test. Use before writing UI code to set color, typography, spacing, and motion tokens.