diff --git a/scripts/smoke.py b/scripts/smoke.py
new file mode 100644
index 0000000..8bb9043
--- /dev/null
+++ b/scripts/smoke.py
@@ -0,0 +1,92 @@
+"""Smoke test: fit a tiny Jacobian on Qwen3-0.6B, steer on the word "happy/joy".
+
+(authored by Claude)
+
+Touches the whole jsteer path end-to-end on a real model, cheaply:
+  fit -> save -> load round-trip -> word_vector -> generate at C in {-8, 0, 8}.
+
+Run:
+    uv run python scripts/smoke.py 2>&1 | tee /tmp/claude-1000/jsteer_smoke.log
+
+Read the prints: the FULL first fit prompt (with special tokens), the FULL
+generation prompt as the model sees it, and each generation verbatim, with a
+SHOULD line so a deviation is legible.
+"""
+from __future__ import annotations
+
+import torch
+from loguru import logger
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from jsteer import Jacobian
+
+MODEL = "Qwen/Qwen3-0.6B"
+CACHE = "artifacts/qwen3-0.6b-smoke.jac"
+DEVICE = "cuda"
+DTYPE = torch.bfloat16
+
+# Claude: 8 english web-text-ish prompts, each padded past 17 tokens so jlens
+# (skip_first=16, drop final) has >=1 valid source position per prompt.
+PROMPTS = [
+    "The weather this morning was cold and grey, so I made a large pot of coffee and sat by the window watching the rain fall.",
+    "Scientists have long argued about whether the early universe expanded smoothly or in sudden bursts that left traces we can still measure today.",
+    "My grandmother used to tell stories about growing up on a small farm, where every season brought a different kind of hard and honest work.",
+    "The city council voted last night to repair the old stone bridge downtown, a project residents have been requesting for well over a decade.",
+    "After months of training, the runners lined up at dawn, breath fogging in the cold air, waiting nervously for the starting gun to fire.",
+    "Learning to cook well takes patience more than talent, a willingness to taste often, to fail a few times, and to pay attention to detail.",
+    "The library on the corner smells of old paper and dust, and its quiet reading room has been my favourite place to think for many years.",
+    "Software written in a hurry tends to accumulate small mistakes that hide quietly until, one ordinary afternoon, they surface all at once together.",
+]
+
+GEN_PROMPT = "I went to the park today and"
+
+
+def _show_tokens(tok, text: str, label: str) -> None:
+    ids = tok(text, add_special_tokens=True).input_ids
+    logger.info(f"{label}: {len(ids)} tokens (add_special_tokens=True)")
+    logger.info(f"{label} decoded-with-special:\n{tok.decode(ids)!r}")
+
+
+def main() -> None:
+    logger.info(f"loading {MODEL} ({DTYPE}) on {DEVICE}")
+    tok = AutoTokenizer.from_pretrained(MODEL)
+    model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=DTYPE).to(DEVICE).eval()
+
+    # SHOULD: fit prompt shows a BOS-like sink token then english web text.
+    # ELSE tokenizer/template drift (jlens force_bos should add an attention sink).
+    _show_tokens(tok, PROMPTS[0], "FIT PROMPT[0]")
+
+    logger.info("fitting Jacobian (layers=0.3..0.9 band, dim_batch=8)")
+    jac = Jacobian.fit(model, tok, PROMPTS, layers=(0.3, 0.9),
+                       dim_batch=8, max_seq_len=128)
+    logger.info(f"fitted: {jac!r}  layers={jac.layers}")
+
+    jac.save(CACHE)
+    jac2 = Jacobian.load(CACHE)
+    logger.info(f"load round-trip: {jac2!r}  layers={jac2.layers}")
+    # SHOULD: reloaded layers identical to fitted. ELSE save/load wiring bug.
+    assert jac2.layers == jac.layers, (jac2.layers, jac.layers)
+
+    v = jac2.word_vector(model, tok, ["happy", "joy"])
+    logger.info(f"word_vector layers={sorted(v.stacked)}")
+
+    # SHOULD: generation prompt shows a sink token then "I went to the park today and".
+    _show_tokens(tok, GEN_PROMPT, "GEN PROMPT")
+    enc = tok(GEN_PROMPT, return_tensors="pt").to(DEVICE)
+
+    for C in (-8, 0, 8):
+        with v(model, C=C):
+            out = model.generate(**enc, max_new_tokens=40, do_sample=False,
+                                 pad_token_id=tok.eos_token_id)
+        text = tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True)
+        logger.info(f"=== C={C:+d} generation ===\n{text!r}")
+
+    logger.info(
+        "SHOULD: C=+8 mentions happiness/joy more than C=0; C=-8 less or "
+        "negative tone. ELSE steering wiring or sign issue. All three SHOULD "
+        "stay coherent english; gibberish means the coeff is too large or the "
+        "vector is malformed.")
+
+
+if __name__ == "__main__":
+    main()