Skills

export async function intent(cart: Cart) {
  const session = await stripe.checkout.sessions.create({
    line_items: cart.items.map(toLine),
    mode: "payment",
    success_url: `${BASE}/ok?id={CHECKOUT_SESSION_ID}`,

def clone(samples: list[Path]) -> str:
    files = [("files", open(p, "rb")) for p in samples]
    r = client.voices.add(name=meta.name, files=files,
        description=meta.desc, labels={"accent": meta.acc})

You are a precise table extractor. Given a PDF page,
return JSON matching the schema below. Preserve units,
never hallucinate cells, and emit null for empty.

Schema: { rows: Array<{ ...fields }> }

await rateLimiter.acquire(host);
const res = await fetch(url, {
  headers: { "user-agent": UA, "accept": "text/html" },
  signal: AbortSignal.timeout(8000),
});
if (res.status === 429) return backoff(url);

function pick(task: Task, budget: number) {
  const evals = MODELS.filter(m => m.cost <= budget);
  return evals.sort((a, b) =>
    score(b, task) - score(a, task))[0] ?? FALLBACK;
}

Transcribe the handwritten note in the image verbatim.
Preserve line breaks and indentation. Mark unreadable
spans as [???]. Do not normalize spelling. Output plain
text only — no commentary.

for chunk in batched(docs, 64):
    vecs = embed(chunk, model="text-embed-3")
    cellar.upsert(
        ns="kb",
        items=[(d.id, v, d.meta) for d, v in zip(chunk, vecs)],
    )

for await (const tok of textStream) {
  buf += tok;
  if (sentenceBoundary(buf)) {
    yield* synth.stream(buf);
    buf = "";
  }
}

const sig = req.headers.get("stripe-signature")!;
const body = await req.text();
const event = stripe.webhooks.constructEvent(
  body, sig, process.env.STRIPE_WEBHOOK_SECRET!
);

You wrote a draft. Now critique it as a senior reviewer.
List exactly 3 weaknesses with line refs. Then rewrite
the draft addressing every weakness. Output ONLY the
revised draft — no preamble.

// Routes a user message to the right specialist agent.
export async function route(msg: string) {
  const { intent, confidence } = await classify(msg);
  if (confidence < 0.6) return AGENTS.generalist;
  return AGENTS[intent] ?? AGENTS.generalist;
}

const cache = new LRU<string, Float32Array>({ max: 10_000 });
export async function embed(text: string) {
  const key = sha1(text);
  if (cache.has(key)) return cache.get(key)!;
  const v = await openai.embeddings.create({ model, input: text });
  cache.set(key, v); return v;
}