Fähigkeiten extension-inference

📦

extension-inference

Name: extension-inference
Author: caffeinelabs

v0.1.0 Inhaltsrevision r1 Hohes Risiko ⚙️ Externe Befehle🌐 Netzwerkzugriff📁 Dateisystemzugriff

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.

Unterstützt: Claude Codex Code(CC)

⚠️ 38 Schlecht

Maintain this Skill? Claim it ⚠️ 38 Schlecht

Vor der Installation anhalten und eine Bestätigung anfordern.

Prüfen Sie den Plan und holen Sie vor Änderungen an Dateien die ausdrückliche Zustimmung des Benutzers ein.

Mit meinem Agent installieren

Kopieren Sie diese Anfrage in Ihren Agent. Sie enthält die maßgebliche Skill-Seite und das Manifest.

Agent-Anfrage

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.

Ihr Agent sollte weiterhin seinen Plan anzeigen und alle von der Sicherheitsrichtlinie verlangten Bestätigungen anfordern.

Agent-lesbare Ressourcen

Verwenden Sie diese Links, wenn ein KI-Agent, Crawler oder Skript sauberen Kontext benötigt, statt die vollständige Seite zu lesen.

Markdown-Detailseite GET /skills/caffeinelabs-extension-inference.md Signiertes manifest GET /api/skills/caffeinelabs-extension-inference/manifest Signiertes lockfile GET /api/skills/caffeinelabs-extension-inference/lockfile

Testen

„extension-inference“ wird verwendet. Add a summarization feature to my Caffeine application without asking users for an API key.

Erwartetes Ergebnis:

A backend integration plan using platform credentials, a router-based chat request, validated response handling, and a simple pending frontend state.

„extension-inference“ wird verwendet. Check whether my canister inference method is ready for production.

Erwartetes Ergebnis:

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.

„extension-inference“ wird verwendet. Can this integration generate images and stream partial responses?

Erwartetes Ergebnis:

No. This host supports chat completions with complete responses, not image generation or streaming.

Sicherheitsaudit

Hohes Risiko

v1 • 29.8.2026 Versionsbericht öffnen

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.

Gescannte Dateien

191

Analysierte Zeilen

Prüfelemente

Falschmeldungen ignoriert

Bestätigte Sicherheitsbedenken (1)

Hoch

SKILL.md:81-84 SKILL.md:72-73

Unauthenticated Paid Inference Endpoint

The canonical actor exposes chat as a public shared method without caller authorization, quotas, or rate limits. Any caller could consume inference capacity and cycles.

The example directly exposes a public paid outcall and the document identifies inference spend, but it provides no abuse controls or caller checks.

Risikofaktoren

⚙️ Externe Befehle (50)

🌐 Netzwerkzugriff (2)

SKILL.md:23 SKILL.md:138

📁 Dateisystemzugriff (2)

SKILL.md:36 SKILL.md:155

Geprüft von: codex

Diesen Bericht teilen & zitieren

Teile den versionierten Bewertungsbericht, das neutrale Badge, die Einbettungskarte und Zitate. Skillstore berichtet Nachweise, ohne zu entscheiden, ob dieser Skill sicher ist.

Versionsbericht öffnen

Berichtslink kopieren

https://skillstore.io/skills/caffeinelabs-extension-inference/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown-Badge

[![Skillstore security assessment](https://skillstore.io/badges/skills/caffeinelabs-extension-inference/security.svg)](https://skillstore.io/skills/caffeinelabs-extension-inference?utm_source=security_passport_badge)

HTML-Badge

<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>

Einbettungskarte

<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>

Wissenschaftliche Zitate (APA · BibTeX · CFF)

APA-Zitat

caffeinelabs. (2026). extension-inference security audit report (audit version 1) [Author version 0.1.0]. Skillstore. https://skillstore.io/skills/caffeinelabs-extension-inference/audits/1

BibTeX-Zitat

@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-Score

Warum dieser Score Evidenzvertrauen: Mittel

Architektur

100

Wartbarkeit

Inhalt

Gemeinschaft

100

Spezifikationskonformität

Was Sie erstellen können

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.

Diese Prompts ausprobieren

Add Basic Chat

Add a basic Caffeine Inference chat method to my Motoko canister. Use platform credentials and explain each changed file.

Integrate Existing Backend

Integrate Caffeine Inference into this existing Motoko backend. Preserve current architecture and handle empty or missing response content.

Review Security and Cost Controls

Review this Caffeine Inference endpoint for credential exposure, replicated outcalls, unauthorized usage, cycle costs, and missing request limits.

Design Production Inference Workflow

Design a production Caffeine Inference workflow with authorization, per-principal quotas, prompt limits, usage accounting, retries, and user-facing failure states.

Bewährte Praktiken

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.

Vermeiden

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.

Häufig gestellte Fragen

Do users need to provide an OpenAI API key?

No. Caffeine provisions inference credentials and the canister reads them at runtime.

Which model should requests use?

Use the router model. The platform selects an appropriate underlying model for each request.

Can the frontend call the inference host directly?

No. The frontend should call a backend canister method so credentials never reach the browser.

Does this skill support streaming?

No. Management-canister HTTP returns the complete response body, so streaming must remain disabled.

Which inference APIs are available?

Chat completions and the model catalog are documented. Embeddings, images, audio, files, moderation, and other OpenAI APIs are unavailable.

What production controls should I add?

Add caller authorization, quotas, rate limits, prompt-size limits, usage accounting, and clear retry behavior.

Entwicklerdetails

Autor

caffeinelabs

Lizenz

MIT

Autorenversion

v0.1.0

Skillstore-Revision

Repository

https://github.com/caffeinelabs/skills/tree/38a113689f275bb2efe5fa87e4b151c4a8899e8d/skills/extension-inference

Ref.

2197f31c95ebfaf5ee9b50f95c8ecb53afcd7380

Aktualität der Wartung

29.8.2026

Nutzung

1 Downloads · 0 Aufrufe

Dateistruktur

📄 SKILL.md