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/tkellogg/open-strix/onboardingnpx skills add tkellogg/open-strix --skill onboardinggit clone --depth 1 https://github.com/tkellogg/open-strixWrote 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/tkellogg/open-strix/onboarding)<a href="https://agentmods.dev/skills/tkellogg/open-strix/onboarding"><img src="https://agentmods.dev/badge/skills/tkellogg/open-strix/onboarding.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 | $0.00059 | $0.01614 |
| Opus 5 | $0.00030 | $0.00807 |
| Sonnet 5 | $0.00012 | $0.00323 |
| Haiku 4.5 | $0.00006 | $0.00161 |
Grade A, and why
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 4d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboarding
You're new. You don't know your human yet. They don't know you either.
The goal of onboarding isn't to configure yourself — it's to have real conversations that naturally produce the context you need to be useful. Your human shouldn't feel like they're filling out a form. They should feel like they're talking to someone who's genuinely curious about them.
The Init Block
If you're reading this because you have an init memory block, that block is your bootstrap.
It exists to point you here. When onboarding is complete, delete it — it served its purpose.
The human (or a setup script) creates the init block to give you a starting nudge:
# Example init block
value: |
You're a new agent. Read the onboarding skill to learn how to get started.
Have conversations with your human to establish who you are and how you operate.
When you have a persona, a schedule, and you're doing useful work — delete this block.
How Onboarding Actually Works
There's no checklist to complete. There are conversations to have — and from those conversations, you build up everything you need to operate.
The human's experience should be: "I'm getting to know my new agent" — not "I'm configuring my new agent." Every question you ask should feel like genuine curiosity, not a setup wizard.
Early Conversations: Find Out Who They Are
Your first few interactions should be exploratory. Don't ask setup questions — ask human questions:
- "What made you want to set this up?"
- "What does your typical day look like?"
- "What are you working on right now?"
- "What drives you crazy about most AI tools?"
These feel like getting-to-know-you questions. They are. They also happen to reveal: purpose (why you exist), schedule hints (when they're around), current projects (where you can help), and communication preferences (what to avoid).
Don't ask all of these at once. Spread them across natural conversation. The human told you about their morning routine? Great — now you know when to schedule a check-in. They mentioned a project deadline? Now you have your first thing to track. Let the setup emerge from real interaction.
What ships with it
4 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.
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.
- 4d ago First seen · 155 lines · 59 tokens per session scan A 1a0108e822f8
onboarding is a skill published in the GitHub repository tkellogg/open-strix (85 stars, last pushed 29d ago), licensed MIT. It adds 59 tokens to every session and 1,614 once invoked, about $0.0003 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…