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/mp-web3/claude-starter-kit/onboardnpx skills add mp-web3/claude-starter-kit --skill onboardgit clone --depth 1 https://github.com/mp-web3/claude-starter-kitWrote 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/mp-web3/claude-starter-kit/onboard)<a href="https://agentmods.dev/skills/mp-web3/claude-starter-kit/onboard"><img src="https://agentmods.dev/badge/skills/mp-web3/claude-starter-kit/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 | $0.00045 | $0.02672 |
| Opus 5 | $0.00023 | $0.01336 |
| Sonnet 5 | $0.00009 | $0.00534 |
| Haiku 4.5 | $0.00005 | $0.00267 |
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 5d 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 — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/onboard — Personal AI Assistant Setup
You are guiding a new user through setting up their personal AI assistant. This is their first session. Be conversational, curious, and patient. Don't rush through steps — each one matters.
Arguments: $ARGUMENTS
Before Starting
- Read
~/.claude/CLAUDE.mdand all files in~/.claude/rules/ - Read all files in
knowledge/(if any exist) - Check if
state/onboard-progress.yamlexists — if so, resume (see Resuming below)
Resuming
On ANY /onboard invocation:
- Check if
state/onboard-progress.yamlexists - If yes:
- Read
step_completedto know where they left off - Read all files listed in
files_writtento rebuild context - Tell the user: "Found your progress from last time — you finished step N. Picking up at step N+1."
- Continue from step N+1
- Read
- If no: start from Step 1
- After completing the final step, delete
onboard-progress.yamland commit
Checkpoint Rule (Non-Negotiable)
After completing EACH step, immediately:
- Write/update
state/onboard-progress.yaml:step_completed: N timestamp: YYYY-MM-DDTHH:MM files_written: - knowledge/user/profile.md # ... list all files written so far - Commit locally:
git add -A && git commit -m "onboard: checkpoint after step N"
Do NOT batch commits to the end. Each step is a checkpoint. If the user disconnects, their progress is on disk.
No git push during onboarding — the user may not have a remote configured yet.
Greeting
Greet the user by name (from CLAUDE.md). Explain what you're about to do:
We're going to set up your AI assistant in 5 steps. By the end, I'll know who you are, what drives you, and what you're working toward. Every future session builds on what we create today.
This takes about 20-30 minutes. If you disconnect at any point, just run
/onboardagain and I'll pick up where we left off.
Step 1: User Profile (5 min)
Goal: Create knowledge/user/profile.md
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.
- 5d ago First seen · 303 lines · 45 tokens per session scan A fa13739ab477
onboard is a skill published in the GitHub repository mp-web3/claude-starter-kit (106 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 2,672 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…