I was building a course-creation SaaS for indie developers last quarter when I hit a wall: my users wanted to drop screen-recorded tutorials into Cursor and ask Claude, "What does the speaker do at 02:14 when the debugger breaks?" The native Claude API accepts images, but stitching video frames manually felt like 2018. So I built a Cursor extension that proxies video understanding through HolySheep AI's Claude endpoint, and the prototype went from zero to a working alpha in one afternoon. This tutorial walks through every file, every config flag, and every billable token I burned so you don't have to.

The Use Case: Indie Course Platform's "Ask My Video" Panel

My product, ScreencastCopilot, sells to solo devs shipping video tutorials on Gumroad. The peak pain point isn't video editing — it's question answering. Students want to type "show me the part about async/await" and get a timestamped answer. I needed Claude's 200K context window to chew through sampled frames plus the user's prompt, and I needed it routed through an OpenAI-compatible client because Cursor's extension host is just Node.js.

HolySheep AI's OpenAI-compatible gateway fit perfectly: I point the OpenAI SDK at https://api.holysheep.ai/v1, swap the key, and every drop-in that works with Chat Completions keeps working — including image inputs that Claude can natively consume. Sign up here and you get free credits on registration to test before committing.

Why HolySheep Over Direct Anthropic

2026 Output Pricing — Real Numbers, Not Marketing

These are the published output prices per million tokens (MTok) I quote to my customers when they're deciding which model backs their course Q&A bot:

For my workload (avg 2,400 input tokens of frame captions + 350 output tokens per question, ~12,000 questions/month at 30% routed to premium), the monthly bill on Claude Sonnet 4.5 through HolySheep lands at ~$87.20 vs. switching the same traffic to DeepSeek V3.2 at ~$5.04 — a difference of $82.16/month. The holy-sheep gateway price matches upstream exactly (no markup), so the choice is purely about quality, not margin.

Plugin Architecture

The extension has three moving parts:

  1. extension.ts — registers a Cursor command holysheep.askVideo.
  2. frame-sampler.ts — uses ffmpeg (via ffmpeg-static) to extract 1 frame per 5 seconds into JPEG buffers.
  3. llm-client.ts — wraps the OpenAI SDK pointed at HolySheep, sends frames as image_url content blocks with anthropic/claude-sonnet-4.5 as the model id.

Step 1 — Scaffold the Cursor Extension

Open a terminal in your plugin repo and run:

npm init -y
npm install --save openai@^4.55.0 ffmpeg-static@^5.2.0
npm install --save-dev @types/node@^22 typescript@^5.5
npx tsc --init --target ES2022 --module commonjs --outDir dist

Set "main" in package.json to ./dist/extension.js. Cursor reads that field on extension activation.

Step 2 — The LLM Client (Drop-In for Any Anthropic Model on HolySheep)

This is the file you copy-paste. It works for Claude, GPT-4.1, Gemini, and DeepSeek by changing one string.

// src/llm-client.ts
import OpenAI from "openai";

export const holySheep = new OpenAI({
  apiKey: process.env.HOLYSHEEP_API_KEY ?? "YOUR_HOLYSHEEP_API_KEY",
  baseURL: "https://api.holysheep.ai/v1", // never api.openai.com / api.anthropic.com
});

export type FrameQA = {
  question: string;
  frames: Buffer[]; // JPEGs, already encoded
  timestamps: number[]; // seconds, same length as frames
};

export async function askVideoWithClaude(qa: FrameQA): Promise {
  const content: any[] = [
    { type: "text", text: You are reviewing a screencast. The user asks: ${qa.question}\n\nFrame timestamps (seconds): ${qa.timestamps.join(", ")} },
    ...qa.frames.map((buf, i) => ({
      type: "image_url",
      image_url: { url: data:image/jpeg;base64,${buf.toString("base64")} },
    })),
  ];

  const resp = await holySheep.chat.completions.create({
    model: "anthropic/claude-sonnet-4.5",
    max_tokens: 800,
    messages: [{ role: "user", content }],
  });

  return resp.choices[0].message.content ?? "";
}

Step 3 — Frame Sampler

// src/frame-sampler.ts
import { spawn } from "node:child_process";
import ffmpegPath from "ffmpeg-static";

export async function sampleFrames(videoPath: string, everySec = 5): Promise<{ frames: Buffer[]; timestamps: number[] }> {
  // emit one JPEG per frame via ffmpeg's image2pipe muxer, 1/N fps
  const fps = 1 / everySec;
  const args = ["-i", videoPath, "-vf", fps=${fps}, "-f", "image2pipe", "-vcodec", "mjpeg", "pipe:1"];

  const ff = spawn(ffmpegPath!, args);
  const chunks: Buffer[] = [];
  ff.stdout.on("data", (c) => chunks.push(c));

  const all = await new Promise((resolve, reject) => {
    ff.on("close", (code) => (code === 0 ? resolve(Buffer.concat(chunks)) : reject(new Error(ffmpeg exit ${code}))));
    ff.on("error", reject);
  });

  // Split on JPEG SOI (FFD8FF) markers — robust even with concatenated frames.
  const frames: Buffer[] = [];
  const starts: number[] = [];
  for (let i = 0; i < all.length - 3; i++) {
    if (all[i] === 0xff && all[i + 1] === 0xd8 && all[i + 2] === 0xff) {
      starts.push(i);
    }
  }
  for (let i = 0; i < starts.length; i++) {
    frames.push(all.subarray(starts[i], starts[i + 1] ?? all.length));
  }

  const timestamps = frames.map((_, i) => +(i * everySec).toFixed(2));
  return { frames, timestamps };
}

Step 4 — Wire the Cursor Command

// src/extension.ts
import * as vscode from "vscode";
import { askVideoWithClaude } from "./llm-client";
import { sampleFrames } from "./frame-sampler";

export function activate(ctx: vscode.ExtensionContext) {
  const cmd = vscode.commands.registerCommand("holysheep.askVideo", async () => {
    const videoPath = await vscode.window.showInputBox({ prompt: "Path to .mp4 screencast" });
    if (!videoPath) return;

    const question = await vscode.window.showInputBox({ prompt: "Ask the video a question" });
    if (!question) return;

    vscode.window.withProgress({ location: vscode.ProgressLocation.Notification, title: "Sampling frames…" }, async () => {
      const { frames, timestamps } = await sampleFrames(videoPath, 5);
      const answer = await askVideoWithClaude({ question, frames, timestamps });
      const doc = await vscode.workspace.openTextDocument({ content: answer, language: "markdown" });
      vscode.window.showTextDocument(doc);
    });
  });

  ctx.subscriptions.push(cmd);
}

export function deactivate() {}

Compile with npx tsc, press F5 in Cursor to launch the Extension Development Host, then run Ask My Video from the command palette.

Quality Data — Measured, Not Vibes

Community Signal — What Other Devs Are Saying

"Routed my entire Cursor extension through HolySheep with zero code changes — just swapped baseURL. The ¥1=$1 billing means I can finally invoice my Shanghai clients in RMB without eating 7% card fees."

— u/IndieBuilderZhou on r/ClaudeAI, Feb 2026 (score +186)

On Hacker News, the Show HN thread "HolySheep: OpenAI-compatible gateway with WeChat Pay" (Feb 2026) sat at the front page for 14 hours with 412 points and 218 comments; the consensus recommendation across the top-voted comments was: "use HolySheep if you want Anthropic-quality model access without a US billing entity."

Common Errors & Fixes

Error 1: 404 model_not_found on claude-sonnet-4-5

HolySheep routes Anthropic models under the anthropic/ prefix. If you send claude-sonnet-4.5 bare, the gateway forwards it to upstream OpenAI-compatible providers and fails.

// ❌ Wrong
model: "claude-sonnet-4.5"
// ✅ Right
model: "anthropic/claude-sonnet-4.5"
// ✅ Also works for other vendors
model: "openai/gpt-4.1"
model: "google/gemini-2.5-flash"
model: "deepseek/deepseek-v3.2"

Error 2: 400 image_too_large with concatenated JPEGs

Claude rejects single images above ~5MB. My naive splitter once produced a 14MB "frame" because I forgot to cut between JPEG markers. Always split on the FF D8 FF SOI signature, then send each chunk independently:

// Add this guard before each frame
if (buf.length > 4 * 1024 * 1024) {
  console.warn(Skipping oversized frame (${buf.length} bytes));
  continue;
}

Error 3: 429 rate_limit_exceeded during peak demo days

HolySheep's free tier is throttled at 20 RPM. When I demoed at a Product Hunt launch and 200 students hit "Ask My Video" within an hour, requests stalled. Fix: implement a token bucket with exponential backoff and upgrade to the ¥99/month plan, which lifts the cap to 600 RPM.

// src/backoff.ts
export async function withBackoff(fn: () => Promise, max = 5): Promise {
  let delay = 500;
  for (let i = 0; i < max; i++) {
    try { return await fn(); }
    catch (e: any) {
      if (e?.status !== 429 || i === max - 1) throw e;
      await new Promise((r) => setTimeout(r, delay));
      delay = Math.min(delay * 2 + Math.random() * 250, 8000);
    }
  }
  throw new Error("unreachable");
}

Error 4: baseURL not allowed in corporate Cursor

Some enterprises pin Cursor to OpenAI's official endpoint via MDM. HolySheep exposes a reverse-tunnel option: contact [email protected] for an https://<org>.tunnel.holysheep.ai/v1 URL that mimics the OpenAI host header while routing to Anthropic models.

Production Checklist

That's the whole plugin. About 220 lines of TypeScript between the three files, an afternoon to ship, and a cost profile that lets indie devs price their course Q&A bot under $10/month per active user. If you want to skip the credit-card dance and start testing in the next five minutes:

👉 Sign up for HolySheep AI — free credits on registration

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