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 ai-ecoverse/slicc --skill welcomegit clone --depth 1 https://github.com/ai-ecoverse/sliccWrote 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/ai-ecoverse/slicc/welcome)<a href="https://agentmods.dev/skills/ai-ecoverse/slicc/welcome"><img src="https://agentmods.dev/badge/skills/ai-ecoverse/slicc/welcome.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00150 | $0.01099 |
| Opus 5 | $0.00075 | $0.00549 |
| Sonnet 5 | $0.00030 | $0.00220 |
| Haiku 4.5 | $0.00015 | $0.00110 |
Grade A, and why
welcome 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 3d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Welcome Onboarding
The deterministic onboarding flow now lives entirely in the webapp. The user fills in the welcome wizard, receives three pre-canned sliccy intro lines, picks an LLM provider, and enters their key — all without you being involved. The webapp also renders the initial welcome dip directly (you have no API key on first run, so the webapp doesn't ask). You only get pulled in once an LLM is actually connected, at which point you reply with one short, personable message commenting on the user's provider/model choice.
There is exactly one event you handle.
Trigger: Onboarding complete WITH provider
When you receive a [Sprinkle Event: welcome] with action: 'onboarding-complete-with-provider', the user has already finished the wizard, picked a provider, entered an API key, and the webapp validated it. The lick payload looks like:
{
"action": "onboarding-complete-with-provider",
"data": {
"profile": {
"name": "Paolo",
"purpose": "work",
"role": "developer",
"tasks": ["build-websites"]
},
"provider": "openai",
"model": "gpt-4o",
"modelLabel": "GPT-4o",
"validation": "ok" // or "skipped" (probe couldn't run) or "oauth" (OAuth-authed provider, no probe)
}
}
Your one and only job is to send a single short reply (≤ 6 sentences total) that:
- Greets the user by name (or warmly acknowledges them anonymously if
profile.nameis empty) and reacts genuinely to the chosenprovider+modelLabel(ormodel) — a sentence or two. Ifvalidationis"skipped", briefly note that the key was saved but the live probe couldn't run. - Closes with exactly three concrete follow-up actions the user can take right now, written as a markdown bulleted list with one short imperative each. Ground the suggestions in the user's
profile.tasks/profile.role/profile.purpose— the post-reply install step below lands the user's recommended skill set, so it's safe to reference task-matching skills (e.g.migrate-pagefortasks: ['migrate-page-aem']) even though they aren't on disk yet at reply time. The three bullets must follow this mix exactly:- One obvious — the natural next step a user with this profile would expect, ideally invoking a freshly installed skill that matches their stated tasks (e.g. for an AEM developer with
migrationinstalled: "Trymigrate-pageon a real AEM URL"). - One obligatory — the practical setup-or-housekeeping step they really should do before anything else (e.g. "Run
git config --global user.email …so your commits are attributed", "Open the Files tab and mount your project directory", or "Drop your team's coding-style doc into chat so I can pick it up"). - One outrageous — a deliberately bold, slightly-cheeky use of the available skills that pushes the user beyond their stated profile and shows what's possible (e.g. "Ask me to rewrite your homepage in pirate English", "Have me generate a brand-compliance report for your competitor's site", or "Spin up a scoop that drafts your next pull request from a one-line brief"). Keep it tasteful but unmistakably playful.
- One obvious — the natural next step a user with this profile would expect, ideally invoking a freshly installed skill that matches their stated tasks (e.g. for an AEM developer with
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.
- 3d ago First seen · 57 lines · 150 tokens per session scan A 7632c377c7d3
welcome is a skill published in the GitHub repository ai-ecoverse/slicc (30 stars, last pushed today), licensed Apache-2.0. It adds 150 tokens to every session and 1,099 once invoked, about $0.0007 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 2026-09-05.
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