extension-inference
Build Secure Caffeine Inference Features
Caffeine applications need LLM access without exposing provider keys or misconfiguring canister outcalls. This skill provides the supported authenticated Motoko integration pattern.
安装前请停下并请求确认。
请审查安装计划，并在更改文件前获得用户明确同意。
使用我的智能体安装
将此请求复制给你的智能体，其中包含权威 Skill 页面和清单。
Review the Skillstore skill "extension-inference" from https://skillstore.io/skills/caffeinelabs-extension-inference.md and its manifest at https://skillstore.io/api/skills/caffeinelabs-extension-inference/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.你的智能体仍应展示其计划，并按安全策略请求必要确认。
Agent 可读资源
当 AI Agent、爬虫或脚本需要干净上下文，而不是读取完整页面时，请使用这些链接。
测试它
正在使用“extension-inference”。 Add a summarization feature to my Caffeine application without asking users for an API key.
预期结果:
A backend integration plan using platform credentials, a router-based chat request, validated response handling, and a simple pending frontend state.
正在使用“extension-inference”。 Check whether my canister inference method is ready for production.
预期结果:
- Credential handling is backend-only and refreshed for each request.
- Authorization, quotas, rate limits, and prompt-size limits are required before deployment.
- The request must remain non-replicated and cycle usage should be monitored.
正在使用“extension-inference”。 Can this integration generate images and stream partial responses?
预期结果:
No. This host supports chat completions with complete responses, not image generation or streaming.
安全审计
高风险All 59 static findings are false positives caused by Markdown code formatting, documentation links, and relative links. However, the canonical public chat endpoint lacks authorization, quotas, and rate limits, creating a high-confidence resource-abuse risk.
已确认安全问题 (1)
风险因素
⚙️ 外部命令 (50)
🌐 网络访问 (2)
📁 文件系统访问 (2)
分享与引用此报告
分享版本化评估报告、中性徽章、嵌入卡片和引用。Skillstore 只报告证据，不判断该 Skill 是否安全。
复制报告链接
https://skillstore.io/skills/caffeinelabs-extension-inference/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown 徽章
[](https://skillstore.io/skills/caffeinelabs-extension-inference?utm_source=security_passport_badge)HTML 徽章
<a href="https://skillstore.io/skills/caffeinelabs-extension-inference?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/caffeinelabs-extension-inference/security.svg" alt="Skillstore security assessment" loading="lazy"></a>嵌入卡片
<iframe src="https://skillstore.io/embed/skills/caffeinelabs-extension-inference.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>学术引用 (APA · BibTeX · CFF)
APA 引用
caffeinelabs. (2026). extension-inference security audit report (audit version 1) [Author version 0.1.0]. Skillstore. https://skillstore.io/skills/caffeinelabs-extension-inference/audits/1BibTeX 引用
@techreport{caffeinelabs-caffeinelabs-extension-inference-2026,
author = {caffeinelabs},
title = {extension-inference security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/caffeinelabs-extension-inference/audits/1},
note = {Author version 0.1.0}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "extension-inference security audit report (audit version 1)"
version: "0.1.0"
type: report
authors:
- name: "caffeinelabs"
date-released: "2026-08-29"
url: "https://skillstore.io/skills/caffeinelabs-extension-inference/audits/1"
identifiers:
- type: other
value: "skillstore:caffeinelabs-extension-inference:audit:1"
description: "Skillstore immutable audit report identifier"
Skillstore 评分
评分依据 证据置信度: 中你能构建什么
Add a Canister Chat Endpoint
Create a Motoko backend method that sends prompts through Caffeine Inference and returns validated text.
Review Inference Architecture
Check that credentials remain backend-only and requests use supported configuration, model, and replication settings.
Plan an AI Product Feature
Confirm whether chat completions meet product requirements before designing frontend states and backend interfaces.
试试这些提示
Add a basic Caffeine Inference chat method to my Motoko canister. Use platform credentials and explain each changed file.
Integrate Caffeine Inference into this existing Motoko backend. Preserve current architecture and handle empty or missing response content.
Review this Caffeine Inference endpoint for credential exposure, replicated outcalls, unauthorized usage, cycle costs, and missing request limits.
Design a production Caffeine Inference workflow with authorization, per-principal quotas, prompt limits, usage accounting, retries, and user-facing failure states.
最佳实践
- Create configuration from the platform environment for every request and never persist its authorization data.
- Keep non-replicated outcalls enabled and use the router model for every chat completion.
- Protect paid inference methods with authorization, quotas, rate limits, and bounded prompt sizes.
避免
- Do not collect OpenAI keys or expose inference credentials through frontend settings, logs, queries, or actor state.
- Do not call provider endpoints directly or replace platform configuration with hand-built HTTP requests.
- Do not expose an unrestricted public inference method that allows anonymous callers to consume cycles.
常见问题
Do users need to provide an OpenAI API key?
Which model should requests use?
Can the frontend call the inference host directly?
Does this skill support streaming?
Which inference APIs are available?
What production controls should I add?
开发者详情
作者
caffeinelabs许可证
MIT
作者版本
v0.1.0
Skillstore 修订
r1
引用
2197f31c95ebfaf5ee9b50f95c8ecb53afcd7380
维护新鲜度
2026/8/29
使用量
0 次下载 · 0 次浏览
文件结构
📄 SKILL.md