merge: feat/issue-42-classifier-bias (#42)

luminos.py

@@ -8,7 +8,11 @@ import shutil
 import sys
 
 from luminos_lib.tree import build_tree, render_tree
-from luminos_lib.filetypes import classify_files, summarize_categories
+from luminos_lib.filetypes import (
+    classify_files,
+    summarize_categories,
+    survey_signals,
+)
 from luminos_lib.code import detect_languages, find_large_files
 from luminos_lib.recency import find_recent_files
 from luminos_lib.disk import get_disk_usage, top_directories
@@ -56,6 +60,7 @@ def scan(target, depth=3, show_hidden=False, exclude=None):
     finish()
     report["file_categories"] = summarize_categories(classified)
     report["classified_files"] = classified
+    report["survey_signals"] = survey_signals(classified)
 
     on_file, finish = _progress("Counting lines")
     languages, loc = detect_languages(classified, on_file=on_file)

luminos_lib/ai.py

@@ -996,21 +996,44 @@ def _block_to_dict(block):
 # Synthesis pass
 # ---------------------------------------------------------------------------
 
+def _format_survey_signals(signals):
+    """Render the survey_signals dict as a labeled text block."""
+    if not signals or not signals.get("total_files"):
+        return "(no files classified)"
+
+    lines = [f"Total files: {signals.get('total_files', 0)}", ""]
+
+    ext_hist = signals.get("extension_histogram") or {}
+    if ext_hist:
+        lines.append("Extensions (top, by count):")
+        for ext, n in ext_hist.items():
+            lines.append(f"  {ext}: {n}")
+        lines.append("")
+
+    descs = signals.get("file_descriptions") or {}
+    if descs:
+        lines.append("file --brief output (top, by count):")
+        for desc, n in descs.items():
+            lines.append(f"  {desc}: {n}")
+        lines.append("")
+
+    samples = signals.get("filename_samples") or []
+    if samples:
+        lines.append("Filename samples (evenly drawn):")
+        for name in samples:
+            lines.append(f"  {name}")
+
+    return "\n".join(lines).rstrip()
+
+
 def _run_survey(client, target, report, tracker, max_turns=3, verbose=False):
     """Run the reconnaissance survey pass.
 
     Returns a survey dict on success, or None on failure / out-of-turns.
     Survey is advisory — callers must treat None as "no survey context".
     """
-    categories = report.get("file_categories", {}) or {}
+    signals = report.get("survey_signals") or {}
-    if categories:
+    survey_signals_text = _format_survey_signals(signals)
-        ftd_lines = [
-            f"  {cat}: {n}"
-            for cat, n in sorted(categories.items(), key=lambda kv: -kv[1])
-        ]
-        file_type_distribution = "\n".join(ftd_lines)
-    else:
-        file_type_distribution = "  (no files classified)"
 
     try:
         tree_node = build_tree(target, max_depth=2)
@@ -1023,7 +1046,7 @@ def _run_survey(client, target, report, tracker, max_turns=3, verbose=False):
 
     system = _SURVEY_SYSTEM_PROMPT.format(
         target=target,
-        file_type_distribution=file_type_distribution,
+        survey_signals=survey_signals_text,
         tree_preview=tree_preview,
         available_tools=available_tools,
     )

luminos_lib/filetypes.py

@@ -128,3 +128,64 @@ def summarize_categories(classified):
         cat = f["category"]
         summary[cat] = summary.get(cat, 0) + 1
     return summary
+
+
+_SURVEY_TOP_N = 20
+_SURVEY_DESC_TRUNCATE = 80
+
+
+def survey_signals(classified, max_samples=20):
+    """Return raw, unbucketed signals for the AI survey pass.
+
+    Unlike `summarize_categories`, which collapses files into a small
+    biased taxonomy, this exposes the primary signals so the survey
+    LLM can characterize the target without being misled by the
+    classifier's source-code bias.
+
+    See #42 for the rationale and #48 for the unit-of-analysis
+    limitation: the unit here is still "file" — containers like mbox,
+    SQLite, and zip will under-count, while dense file collections like
+    Maildir will over-count.
+
+    Returns a dict with:
+      total_files       — total count
+      extension_histogram — {ext: count}, top _SURVEY_TOP_N by count
+      file_descriptions — {description: count}, top _SURVEY_TOP_N by count
+      filename_samples  — up to max_samples filenames, evenly drawn
+    """
+    total = len(classified)
+
+    ext_counts = {}
+    desc_counts = {}
+    for f in classified:
+        ext = os.path.splitext(f.get("name", ""))[1].lower() or "(none)"
+        ext_counts[ext] = ext_counts.get(ext, 0) + 1
+
+        desc = (f.get("description") or "").strip()
+        if desc:
+            if len(desc) > _SURVEY_DESC_TRUNCATE:
+                desc = desc[:_SURVEY_DESC_TRUNCATE] + "..."
+            desc_counts[desc] = desc_counts.get(desc, 0) + 1
+
+    def _top(d):
+        items = sorted(d.items(), key=lambda kv: (-kv[1], kv[0]))
+        return dict(items[:_SURVEY_TOP_N])
+
+    if total > 0 and max_samples > 0:
+        if total <= max_samples:
+            samples = [f.get("name", "") for f in classified]
+        else:
+            stride = total / max_samples
+            samples = [
+                classified[int(i * stride)].get("name", "")
+                for i in range(max_samples)
+            ]
+    else:
+        samples = []
+
+    return {
+        "total_files": total,
+        "extension_histogram": _top(ext_counts),
+        "file_descriptions": _top(desc_counts),
+        "filename_samples": samples,
+    }

luminos_lib/prompts.py

@@ -131,17 +131,22 @@ Answer three questions about the target: {target}
 ## Inputs
 You have exactly two signals. Do not ask for more.
 
-File type distribution (counts by category):
+File-level signals (raw, unbucketed):
-{file_type_distribution}
+{survey_signals}
 
-IMPORTANT: the file type distribution is produced by a classifier
+These signals are intentionally raw. The extension histogram and
-that is biased toward source code. Its categories are: source,
+the `file --brief` descriptions reflect what is actually on disk,
-config, data, document, media, archive, unknown. It has NO concept
+without any taxonomy collapsing distinct content into one bucket.
-of mail, notebooks, calendars, contacts, ledgers, photo libraries,
+Use them together: an extension alone can mislead (`.txt` could be
-or other personal-data domains — anything text-shaped tends to be
+notes, logs, or message bodies); the `file` command output and
-labeled `source` even when it is not code. If the tree preview
+filename samples disambiguate.
-suggests a non-code target, trust the tree over the histogram and
+
-say so in `domain_notes`.
+Note on units: each signal counts filesystem files. Some targets
+have a different natural unit — a Maildir is one logical mailbox
+with thousands of message files; an mbox is one file containing
+many messages; an archive is one file containing many entries. If
+the signals point at a container shape, name it in `description`
+and `domain_notes` even though the count is in files.
 
 Top-level tree (2 levels deep):
 {tree_preview}

tests/test_filetypes.py

@@ -10,6 +10,7 @@ from luminos_lib.filetypes import (
     _classify_one,
     classify_files,
     summarize_categories,
+    survey_signals,
 )
 
 
@@ -140,5 +141,69 @@ class TestClassifyFiles(unittest.TestCase):
         self.assertGreater(item["size"], 0)
 
 
+class TestSurveySignals(unittest.TestCase):
+    def _f(self, name, description="", category="source"):
+        return {"name": name, "path": f"/x/{name}", "category": category,
+                "size": 10, "description": description}
+
+    def test_empty_input(self):
+        s = survey_signals([])
+        self.assertEqual(s["total_files"], 0)
+        self.assertEqual(s["extension_histogram"], {})
+        self.assertEqual(s["file_descriptions"], {})
+        self.assertEqual(s["filename_samples"], [])
+
+    def test_extension_histogram_uses_lowercase_and_keeps_none(self):
+        files = [
+            self._f("a.PY"), self._f("b.py"), self._f("c.py"),
+            self._f("README"), self._f("Makefile"),
+        ]
+        s = survey_signals(files)
+        self.assertEqual(s["extension_histogram"][".py"], 3)
+        self.assertEqual(s["extension_histogram"]["(none)"], 2)
+
+    def test_file_descriptions_aggregated_and_truncated(self):
+        long_desc = "x" * 200
+        files = [
+            self._f("a.py", "Python script, ASCII text"),
+            self._f("b.py", "Python script, ASCII text"),
+            self._f("c.bin", long_desc),
+        ]
+        s = survey_signals(files)
+        self.assertEqual(s["file_descriptions"]["Python script, ASCII text"], 2)
+        # The long description was truncated and still counted once
+        truncated_keys = [k for k in s["file_descriptions"] if k.startswith("xxx") and k.endswith("...")]
+        self.assertEqual(len(truncated_keys), 1)
+        self.assertLessEqual(len(truncated_keys[0]), 84)  # 80 + "..."
+
+    def test_descriptions_skipped_when_empty(self):
+        files = [self._f("a.py", ""), self._f("b.py", None)]
+        s = survey_signals(files)
+        self.assertEqual(s["file_descriptions"], {})
+
+    def test_top_n_caps_at_20(self):
+        files = [self._f(f"f{i}.ext{i}") for i in range(50)]
+        s = survey_signals(files)
+        self.assertEqual(len(s["extension_histogram"]), 20)
+
+    def test_filename_samples_evenly_drawn(self):
+        files = [self._f(f"file_{i:04d}.txt") for i in range(100)]
+        s = survey_signals(files, max_samples=10)
+        self.assertEqual(len(s["filename_samples"]), 10)
+        # First sample is the first file (stride 10, index 0)
+        self.assertEqual(s["filename_samples"][0], "file_0000.txt")
+        # Last sample is around index 90, not 99
+        self.assertTrue(s["filename_samples"][-1].startswith("file_009"))
+
+    def test_filename_samples_returns_all_when_under_cap(self):
+        files = [self._f(f"f{i}.txt") for i in range(5)]
+        s = survey_signals(files, max_samples=20)
+        self.assertEqual(len(s["filename_samples"]), 5)
+
+    def test_total_files_matches_input(self):
+        files = [self._f(f"f{i}.py") for i in range(7)]
+        self.assertEqual(survey_signals(files)["total_files"], 7)
+
+
 if __name__ == "__main__":
     unittest.main()