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 5.5 | $0.00024 | $0.00445 |
| Sonnet 5.5 | $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 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
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.
- 1mo ago First seen · 75 lines · 61 tokens per session scan A a892f787bee5
xsai is a skill published in the GitHub repository moeru-ai/alint (118 stars, last pushed 26d ago), licensed MIT. It adds 61 tokens to every session and 1,113 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
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kv-cache-design
A guide to arranging an AI agent's conversation so previously processed text can be reused. A KV cache stores intermediate results for earlier tokens, while a prompt cache reuses a stable beginning across requests.
reward-design
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agent-evolution
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bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.