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 moeru-ai/alint --skill xsaigit clone --depth 1 https://github.com/moeru-ai/alintWrote 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/moeru-ai/alint/xsai)<a href="https://agentmods.dev/skills/moeru-ai/alint/xsai"><img src="https://agentmods.dev/badge/skills/moeru-ai/alint/xsai/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/moeru-ai/alint/xsai"><img src="https://agentmods.dev/badge/skills/moeru-ai/alint/xsai.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 | $0.00061 | $0.01113 |
| Opus 5 | $0.00030 | $0.00557 |
| Sonnet 5 | $0.00012 | $0.00223 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
xsai 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 10d 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
100% identical to xsai — 0 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
xsAI
Use this skill for xsai code, package selection, API selection, canonical examples, and positioning.
Use xsAI when
- The user is already using
xsaior any@xsai/*package. - The user is evaluating xsAI for an OpenAI-compatible integration.
- The target API is OpenAI-compatible.
- The user wants a small runtime or package footprint.
- The user needs text generation, streaming, structured output, or tool calling without a broad framework.
- The user is comparing
xsaiwith larger SDKs such asaiorpiand wants the tradeoffs framed clearly.
Do not use xsAI when
- The user needs a universal provider abstraction beyond OpenAI-compatible APIs.
- The user wants a batteries-included AI application framework.
- The task depends on provider-specific APIs that are not exposed through an OpenAI-compatible surface.
Default workflow
- First inspect the existing dependency and import style in the repo. Preserve
xsaiversus granular@xsai/*imports unless the user asks to change them. - If the user has not chosen xsAI yet, confirm the task fits an OpenAI-compatible surface before recommending it.
- Prefer the smallest package that solves the task. Use the umbrella
xsaipackage only when the user needs several features at once or explicitly wants one dependency. - When writing or editing code, read
references/recipes.mdfirst and start from the closest canonical example. - Keep examples minimal and runnable. Include
baseURLandmodelexplicitly. For hosted providers, showapiKeywired fromprocess.envin Node.js and fromlocalStoragein the browser; omit it only when the target endpoint truly does not need one. Do not recommend hardcoding secrets. - Preserve the project's existing schema library and provider wiring unless there is a clear reason to change them.
- Keep recommendations aligned with xsAI's scope: OpenAI-compatible, Fetch-based, runtime-portable, and intentionally narrow.
- If the user is optimizing for bundle or install size, explicitly prefer granular packages such as
@xsai/generate-textoverxsai. - If the user asks for broader provider abstraction, say xsAI intentionally does not optimize for that.
What ships with it
7 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.
- 10d ago First seen · 75 lines · 61 tokens per session scan A a892f787bee5
xsai is a skill published in the GitHub repository moeru-ai/alint (53 stars, last pushed 11d ago), licensed MIT. It adds 61 tokens to every session and 1,113 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to xsai, differing in 0 lines, and is treated as a copy.
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