agent: phase2-tokei-001 — first auto-accepted win from the unattended loop

Ledger row: class 5 (algorithmic) on tokei, first-byte gate table for
the per-byte token matchers in perform_multi_line_analysis.
Speedup median 1.107, 95% CI [1.084, 1.131] vs the banked-wins baseline,
workload: 221 MB / 4360 files, single rayon thread, pinned core.

Manual audit gates (pipeline covers tests+golden+bench only):
- Differential fuzz, 10k mutated inputs across 200 batched runs:
  0 divergences after canonicalizing tokei's benign parallel-walker
  output ordering (present in pristine baseline; counts identical).
  Fuzzer added as scripts/diff-fuzz-tokei.py.
- ASan run recorded in the case study (next commit).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2

scripts/diff-fuzz-tokei.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+"""Differential fuzz for phase2-tokei-001: pristine vs candidate tokei
+must produce byte-identical output on mutated source inputs targeting
+the patched code path (quote/comment token scanning).
+
+Seeds: files from the pinned corpus (many languages). Mutations focus on
+quote/comment structure: inserting/deleting/duplicating quote chars,
+comment openers/closers, backslashes, and random bytes; plus boundary
+slices. Deterministic RNG for reproducibility.
+"""
+
+import random
+import subprocess
+import sys
+from pathlib import Path
+
+ROOT = Path("/home/user/peltier.io")
+BASE = ROOT / "targets/tokei/baseline/release/tokei"
+CAND = ROOT / "targets/tokei/candidate-4e3da7e6bd1c/release/tokei"
+CORPUS = ROOT / "corpora/tokei/input/copy1"
+WORK = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("/tmp/difffuzz")
+ITERS = int(sys.argv[2]) if len(sys.argv) > 2 else 10000
+
+TOKENS = [
+    b'"', b"'", b'`', b'"""', b"'''", b"/*", b"*/", b"//", b"#", b"--",
+    b"<!--", b"-->", b"(*", b"*)", b"{-", b"-}", b"\\", b"\\\\", b'\\"',
+    b"r#\"", b"\"#", b"=begin", b"=end", b"'''", b'f"""', b"%{", b"}",
+]
+
+def mutate(data: bytes, rng: random.Random) -> bytes:
+    ops = rng.randrange(1, 6)
+    b = bytearray(data)
+    for _ in range(ops):
+        if not b:
+            b = bytearray(rng.choice(TOKENS))
+            continue
+        op = rng.randrange(5)
+        pos = rng.randrange(len(b) + 1)
+        if op == 0:      # insert a token
+            b[pos:pos] = rng.choice(TOKENS)
+        elif op == 1 and len(b) > 2:  # delete a slice
+            end = min(len(b), pos + rng.randrange(1, 8))
+            del b[pos:end]
+        elif op == 2:    # flip to a random byte
+            if pos < len(b):
+                b[pos] = rng.randrange(256)
+        elif op == 3:    # duplicate a slice
+            end = min(len(b), pos + rng.randrange(1, 32))
+            b[end:end] = b[pos:end]
+        else:            # newline injection (line-structure stress)
+            b[pos:pos] = b"\n"
+    return bytes(b)
+
+def canonicalize(payload: bytes) -> str:
+    """Parse tokei JSON and sort every list so benign output-order
+    nondeterminism (parallel walker arrival order) doesn't count as a
+    divergence; counts/stats still must match exactly."""
+    import json
+
+    def norm(o):
+        if isinstance(o, dict):
+            return {k: norm(v) for k, v in sorted(o.items())}
+        if isinstance(o, list):
+            return sorted((norm(v) for v in o), key=lambda x: json.dumps(x, sort_keys=True))
+        return o
+
+    return json.dumps(norm(json.loads(payload)), sort_keys=True)
+
+
+def run(binary: Path, d: Path) -> tuple[str, bytes, int]:
+    r = subprocess.run(
+        [str(binary), "--sort", "code", "--output", "json", str(d)],
+        capture_output=True, env={"RAYON_NUM_THREADS": "1", "PATH": "/usr/bin:/bin"},
+    )
+    try:
+        canon = canonicalize(r.stdout)
+    except Exception as e:  # unparseable output is always a divergence candidate
+        canon = f"UNPARSEABLE:{e}:{r.stdout[:200]!r}"
+    return canon + f"||rc={r.returncode}", r.stdout, r.returncode
+
+def main() -> int:
+    rng = random.Random(0xB16B00B5)
+    seeds = sorted(CORPUS.rglob("*"))
+    seeds = [p for p in seeds if p.is_file() and p.stat().st_size < 200_000][:400]
+    if not seeds:
+        print("no seeds found", file=sys.stderr)
+        return 2
+    WORK.mkdir(parents=True, exist_ok=True)
+    fdir = WORK / "case"
+    divergences = 0
+    done = 0
+    # Batch: mutate BATCH files per tokei invocation (tokei scans a dir),
+    # keeping each seed's extension so language detection stays realistic.
+    BATCH = 50
+    while done < ITERS:
+        if fdir.exists():
+            for f in fdir.iterdir():
+                f.unlink()
+        fdir.mkdir(exist_ok=True)
+        for j in range(min(BATCH, ITERS - done)):
+            seed = rng.choice(seeds)
+            name = f"m{done + j}{seed.suffix or '.txt'}"
+            (fdir / name).write_bytes(mutate(seed.read_bytes(), rng))
+        a_canon, a_raw, a_rc = run(BASE, fdir)
+        b_canon, b_raw, b_rc = run(CAND, fdir)
+        if a_canon != b_canon:
+            divergences += 1
+            keep = WORK / f"divergence-{done}"
+            fdir.rename(keep)
+            (keep / "_baseline.out").write_bytes(a_raw + f"\nrc={a_rc}".encode())
+            (keep / "_candidate.out").write_bytes(b_raw + f"\nrc={b_rc}".encode())
+            print(f"DIVERGENCE at batch starting {done}; inputs+outputs kept in {keep}")
+        done += BATCH
+        if done % 1000 == 0:
+            print(f"{done}/{ITERS} mutated files, {divergences} divergences", flush=True)
+    print(f"done: {done} mutated files across {done // BATCH} runs, {divergences} divergences")
+    return 1 if divergences else 0
+
+if __name__ == "__main__":
+    sys.exit(main())