Skills extension-inference

📦

extension-inference

Name: extension-inference
Author: caffeinelabs

v0.1.0 Content revision r1 High Risk ⚙️ External commands🌐 Network access📁 Filesystem access

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.

Supports: Claude Codex Code(CC)

⚠️ 38 Poor

Stop for confirmation before installing.

Review the plan and obtain explicit user consent before changing files.

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

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.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/caffeinelabs-extension-inference.md Signed manifest GET /api/skills/caffeinelabs-extension-inference/manifest Signed lockfile GET /api/skills/caffeinelabs-extension-inference/lockfile

Test it

Using "extension-inference". Add a summarization feature to my Caffeine application without asking users for an API key.

Expected outcome:

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

Using "extension-inference". Check whether my canister inference method is ready for production.

Expected outcome:

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.

Using "extension-inference". Can this integration generate images and stream partial responses?

Expected outcome:

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

Security Audit

High Risk

v1 • 8/29/2026 Open versioned report

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.

Files scanned

191

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

High

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.

Risk Factors

⚙️ External commands (50)

🌐 Network access (2)

SKILL.md:23 SKILL.md:138

📁 Filesystem access (2)

SKILL.md:36 SKILL.md:155

Audited by: codex

Share & cite this report

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Open versioned report

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

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Academic citations (APA · BibTeX · CFF)

APA citation

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 citation

@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

Why this score Evidence Confidence: Medium

Architecture

100

Maintainability

Content

Community

100

Spec Compliance

What You Can Build

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.

Try These Prompts

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.

Best Practices

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.

Avoid

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.

Frequently Asked Questions

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.

Developer Details

Author

caffeinelabs

License

MIT

Author version

v0.1.0

Skillstore revision

Repository

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

Ref

2197f31c95ebfaf5ee9b50f95c8ecb53afcd7380

Maintenance freshness

8/29/2026

Usage

0 downloads · 0 views

File structure

📄 SKILL.md