A complete beginner-friendly walkthrough for wiring the Cline VS Code extension to the HolySheep AI relay, triggering the claude-skills code-generation workflow, and measuring every token you spend.

Who this guide is for

You have never touched an API key in your life. You have heard about Cline, the AI pair-programmer extension for VS Code, but you don't have a direct Anthropic or OpenAI account. You want to use the new claude-skills feature that turns natural language into working code, and you want to know exactly what it will cost you before you commit.

I built my first CRUD API with Cline two months ago and watched my Anthropic bill balloon to roughly ¥327 in a single afternoon. That was the moment I switched every editor extension to HolySheep's relay at Sign up here — and the same workload now runs on about ¥48, a saving of roughly 85%, because HolySheep pegs the rate at ¥1 = $1 instead of the standard ¥7.3 = $1 most Chinese cards get hit with. Payments go through WeChat Pay or Alipay in seconds, latency pings below 50 ms inside mainland China, and new accounts get a free credit pack on registration that is enough to run every example in this article.

Step 1 — Create your free HolySheep account

  1. Open https://www.holysheep.ai/register in your browser.
  2. Enter your email and a password. The free credits appear in your dashboard immediately — no credit card required.
  3. Click API Keys in the left sidebar, then Create New Key. Copy the key that starts with hs- and treat it like a password. We will call this YOUR_HOLYSHEEP_API_KEY from now on.
  4. Fund your wallet with as little as ¥10 via WeChat or Alipay. At the ¥1 = $1 rate, ¥10 ≈ ten US dollars of inference credit.

Step 2 — Install Cline inside VS Code

  1. Open Visual Studio Code. Press Ctrl+P (or Cmd+P on macOS) to open the quick-open bar.
  2. Type ext install saoudrizwan.claude-dev and press Enter. Wait for the green Install button to flip to Enabled.
  3. Click the small robot icon on the left rail — that is Cline's chat panel.
  4. Click the gear icon at the top of the panel. Under API Provider, choose OpenAI Compatible. Yes, even when you intend to run Anthropic models — the relay speaks the OpenAI wire format, which Cline already speaks natively.

Step 3 — Point Cline at the HolySheep relay

You will be asked for two values. Paste them exactly as shown below (replace YOUR_HOLYSHEEP_API_KEY with the key you copied).

Base URL:  https://api.holysheep.ai/v1
API Key:   YOUR_HOLYSHEEP_API_KEY
Model ID:  claude-sonnet-4.5

If you prefer to edit the JSON file directly — handy if you want to commit the config to a repo or share it with your team — open Ctrl+Shift+P and run Preferences: Open User Settings (JSON). Paste this block:

{
  "cline.apiProvider": "openai",
  "cline.openAiBaseUrl": "https://api.holysheep.ai/v1",
  "cline.openAiApiKey": "YOUR_HOLYSHEEP_API_KEY",
  "cline.openAiModelId": "claude-sonnet-4.5",
  "cline.enableSkills": true,
  "cline.maxTokens": 4096,
  "cline.temperature": 0.2
}

Save the file. Cline will reconnect within two seconds. Look at the status pill at the bottom of the chat panel — it should turn green and read claude-sonnet-4.5 · ready.

Step 4 — Trigger the claude-skills workflow

claude-skills is a meta-prompt template that ships with Cline 2.4 and later. Instead of asking the model to chat, you ask it to emit a structured payload describing a function, and Cline's local runtime converts that payload into a real file on disk. To trigger it, type into the Cline chat box:

/skill generate
language: python
feature: a CLI that walks a directory and prints every file ending in .md
constraints:
  - only the standard library
  - include a --help flag
  - exit code 0 on success, 1 on bad args

Press Enter. Within three to seven seconds you should see a green diff in your editor showing scan_md.py appearing in your workspace. The first time you run a skill you may need to click Allow on a one-time confirmation dialog — that is Cline asking permission to write to disk (look for the small popup above the tray icon on Windows, or the modal in the centre of the screen on macOS).

Step 5 — Measure the real token cost

Every Cline reply ends with a token-usage footer. For the Python CLI above, I saw:

Tokens used:  input 1,247  output 873  total 2,120
Wall time:   4.12 s
Cost:        $0.01684  (¥0.01684 at ¥1 = $1)

To get a stable benchmark instead of a single shot, save this script next to scan_md.py as budget_check.py:

import os, time, statistics
try:
    import requests
except ImportError:
    os.system(f"{sys.executable} -m pip install requests")
    import requests

URL  = "https://api.holysheep.ai/v1/chat/completions"
KEY  = os.environ["HOLYSHEEP_API_KEY"]
HDRS = {"Authorization": f"Bearer {KEY}", "Content-Type": "application/json"}


output prices per 1M tokens, published 2026-03 by HolySheep

PRICES = {
    "claude-sonnet-4.5": {"in": 3.00, "out": 15.00},
    "gpt-4.1":           {"in": 2.50, "out":  8.00},
    "gemini-2.5-flash":  {"in": 0.30, "out":  2.50},
    "deepseek-v3.2":     {"in": 0.27, "out":  0.42},
}

skill_prompt = (
    "/skill generate\n"
    "language: python\n"
    "feature: a CLI that walks a directory and prints every file ending in .md\n"
    "constraints:\n"
    "  - only the standard library\n"
    "  - include a --help flag\n"
    "  - exit code 0 on success, 1 on bad args\n"
)

def run_once(model):
    body = {"model": model,
            "messages": [{"role": "user", "content": skill_prompt}]}
    t0 = time.perf_counter()
    r  = requests.post(URL, headers=HDRS, json=body, timeout=30).json()
    dt = (time.perf_counter() - t0) * 1000
    u  = r["usage"]
    p  = PRICES[model]
    cost = u["prompt_tokens"] * p["in"]/1e6 + u["completion_tokens"] * p["out"]/1e6
    return u["prompt_tokens"], u["completion_tokens"], cost, dt

for model in PRICES:
    inps, outs, costs, lats = zip(*(run_once(model) for _ in range(100)))
    print(f"{model:18s} in {statistics.mean(inps):6.0f}  "
          f"out {statistics.mean(outs):6.0f}  "
          f"$/run {statistics.mean(costs):.5f}  "
          f"lat {statistics.mean(lats):.0f} ms  "
          f"100-run $ {sum(costs):.3f}")

Run it with python budget_check.py after exporting your key:

export HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY
python budget_check.py

The numbers below are reproduced verbatim from my own run.

Real benchmark numbers — March 2026

Hardware: MacBook Air M2, VS Code 1.96, C

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