feat: pseudo-frequency for confusables using English word frequency

264 confusable groups where all entries shared the same Hebrew frequency now have differentiated pseudo_frequency values based on English word commonality (hermitdave en_50k.txt). Most common meaning keeps base rank; less common meanings get +100 offset per position. Examples: - אב: "father" (en:194) → 2491, "bud" (en:2963) → 2591 - אח: "brother" (en:300) → 911, "fireplace" (en:9389) → 1011 Builder uses pseudo_frequency for sort order when available. Confusable card definitions now sorted most-common-first. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: vet fallback emoji — verb gate + expanded stop list removes 852 bad matches
2026-04-03 05:28:30 +00:00 · 2026-04-03 05:17:31 +00:00 · 2026-03-26 23:19:43 +00:00 · 2026-03-21 02:19:03 +00:00 · 2026-03-15 14:09:19 +00:00 · 2026-03-15 13:35:23 +00:00
17 changed files with 103719 additions and 32828 deletions
--- a/SCHEMA.yaml
+++ b/SCHEMA.yaml
@ -27,6 +27,7 @@ entry:
  pos_hebrew: "שֵׁם עֶצֶם"          # Part of speech in Hebrew (with nikkud)
  meaning: "father"                # English meaning (cleaned — no inline emoji, no Hebrew prepositions)
  meaning_raw: "father 👨"         # Original meaning as scraped (may contain emoji and/or Hebrew preps)
+  prep: "על"                       # Hebrew preposition(s) governing this word, extracted from meaning_raw (e.g. "(על)" → "על"); null if none
  audio_url: "https://..."         # Pealim audio URL
  audio_file: "6009-av.mp3"        # Local filename (slug-based for confusables, consonant-based otherwise)
  tags: ""                         # Pealim tags if any
@ -68,6 +69,7 @@ entry:
      cloze_word_end: 4            # End offset — enables exact extraction regardless of nikkud changes
      cloze_hint: "family member"
      cloze_guid: "def456..."      # GUID for the cloze note
+      difficulty_score: 234          # Median frequency rank of context words (lower = easier); optional
    rejected_count: 0

  # --- Noun-specific: Inflection Forms ---
--- a/apkg_builder.py
+++ b/apkg_builder.py
@ -35,7 +35,7 @@ COMPLETE_PLURAL_DECK_ID = 1_234_567_903

 # Release version tag added to all notes so users can identify which release
 # their cards come from (visible in Anki's Browse view and card info).
-RELEASE_TAG = "v0.18"
+RELEASE_TAG = "v0.20"

 # Regex for extracting emoji and Hebrew prepositions from meaning strings
 EMOJI_RE = re.compile(r"[\U0001F000-\U0001FFFF\u2600-\u27FF\u2300-\u23FF\uFE00-\uFE0F]+")
@ -265,6 +265,14 @@ details[open] > .more-header::before { content: "● "; }
  text-align: center;
  margin: 0.3em 0;
 }
+.plural-direction {
+  font-size: 32px;
+  color: #444;
+  text-align: center;
+  direction: rtl;
+  margin: 8px 0;
+  font-weight: bold;
+}
 .card [type="button"], .card button, .replay-button {
  display: block !important;
  margin: 4px auto !important;
@ -288,7 +296,26 @@ details[open] > .more-header::before { content: "● "; }
  .related-header { color: #999; }
  .rw-word     { color: #e0e0e0; }
  .rw-meaning  { color: #999; }
+  .plural-direction { color: #aaa; }
 }
+.nightMode .card        { color: #e8e8e8; background: #1c1c1e; }
+.nightMode .hebrew      { color: #f0f0f0; }
+.nightMode .hebrew-sm   { color: #e0e0e0; }
+.nightMode .meaning     { color: #82b0ff; }
+.nightMode .sec-label   { color: #e0e0e0; }
+.nightMode .sec-key     { color: #e0e0e0; }
+.nightMode .sec-val     { color: #e0e0e0; }
+.nightMode .conf-entry  { color: #ddd; }
+.nightMode .hint        { color: #777; }
+.nightMode .voice-label { color: #888; }
+.nightMode .example     { color: #e0e0e0; border-right-color: #555; }
+.nightMode .divider     { border-top-color: #333; }
+.nightMode .freq-badge  { color: #888; border-color: #444; }
+.nightMode .more-header { color: #bbb; background: #2a2a2e; border-color: #555; }
+.nightMode .related-header { color: #999; }
+.nightMode .rw-word     { color: #e0e0e0; }
+.nightMode .rw-meaning  { color: #999; }
+.nightMode .plural-direction { color: #aaa; }
 """

 # ──────────────────────────────────────────────────────────────────────────────
@ -422,7 +449,7 @@ CONJ_BACK = """
 <div class="hebrew">{{ConjugatedForm}}{{#Prep}} ({{Prep}}){{/Prep}}</div>
 {{#Audio}}<div>{{Audio}}</div>{{/Audio}}
 <details class="more-toggle"><summary class="more-header">מידע נוסף</summary>
-{{#Meaning}}<div class="sec-label" style="text-align:center;display:block;">{{Meaning}}</div>{{/Meaning}}
+{{#Meaning}}<div class="meaning" style="font-size:28px;">{{Meaning}}</div>{{/Meaning}}
 <div class="sec-table">
 <div class="sec-label"><span class="sec-key">שֹׁרֶשׁ:</span><span class="sec-val">{{Root}}</span></div>
 <div class="sec-label"><span class="sec-key">בִּנְיָן:</span><span class="sec-val">{{Binyan}}</span></div>
@ -693,6 +720,116 @@ _EMOJI_STOP = frozenset(
        "bar",
        "wheel",
        "horizontal",
+        # Polysemous keywords producing wrong-sense emoji (Sprint 17 audit)
+        "high",  # ⚡ high voltage, not "tall"
+        "down",  # 🫳 palm down, not "descend"
+        "off",  # 📴 phone off, not "remove"
+        "away",  # 💨 dashing away, not "depart"
+        "together",  # 🤲 palms together, not "unite"
+        "top",  # 🎩 top hat, not "upper"
+        "low",  # 🔈 low volume, not "short"
+        "flat",  # 🥿 ballet flat, not "apartment"
+        "soft",  # 🍦 soft serve, not "quiet"
+        "broken",  # 💔 broken heart, not "damaged"
+        "round",  # 📍 round pushpin, not "circular"
+        "cool",  # 🆒 COOL button, not "cold"
+        "free",  # 🆓 FREE button, not "liberated"
+        "long",  # 🪘 long drum, not "lengthy"
+        "straight",  # 📏 straight ruler, not "direct"
+        "empty",  # 🪹 empty nest, not "void"
+        "hot",  # 🥵 hot face, not "warm"
+        "cross",  # ✝️ latin cross, not "intersect"
+        "bright",  # 🔆 bright button, not "luminous"
+        "old",  # 👴 old man, not "aged"
+        "head",  # 🙂‍↔️ shaking head, not "leader"
+        # Category words that match generic emoji
+        "military",  # 🎖️ military medal for any military term
+        "sports",  # 🏅 sports medal for any sports term
+        "food",  # 😋 yummy face for any food term
+        "city",  # 🇻🇦 Vatican flag for any city
+        "china",  # 🇨🇳 China flag for "porcelain"
+        "polish",  # 💅 nail polish for "to polish/shine"
+        "aid",  # 🦻 hearing aid for "to help"
+        "office",  # 🧑‍💼 office worker for "bureau"
+        "construction",  # 🏛️ classical building, not construction
+        "cinema",  # 🎦 cinema emoji for any film term
+        "ceremony",  # 🎑 moon ceremony for any ceremony
+        "building",  # 🏛️ classical building for any structure
+        # Body parts / human features → wrong emoji
+        "arm",  # 🦾 mechanical arm for "to arm"
+        "hair",  # 👱 blond person for "hair"
+        "nose",  # 😤 steam from nose
+        "tongue",  # 😛 tongue-out face
+        "chest",  # 🪎 not a chest
+        "eyes",  # 😃 face with eyes
+        # Abstract/vague words
+        "fear",  # 😱 screaming face
+        "anger",  # 💢 anger symbol
+        "angry",  # 😠 angry face
+        "tired",  # 😫 tired face
+        "sad",  # 😥 sad face
+        "joy",  # 😂 tears of joy
+        "love",  # 💌 love letter
+        "cold",  # 🥶 cold face
+        "pile",  # 💩 pile of poo
+        "man",  # 👨 man
+        "woman",  # 👩 woman
+        "boy",  # 👦 boy
+        "girl",  # 👧 girl
+        "baby",  # 👶 baby
+        "children",  # 🚸 children crossing
+        "student",  # 🧑‍🎓 student
+        "adult",  # 🧑‍🧑‍🧒 family
+        "name",  # 📛 name badge
+        "check",  # ✅ check mark
+        "line",  # 🫥 dotted line face
+        "floor",  # 🤣 ROFL (rolling on floor)
+        "room",  # 🧖 person in steamy room
+        "bubble",  # 👁️‍🗨️ speech bubble
+        "car",  # 🚃 railway car, not automobile
+        "bullet",  # 🚅 bullet train
+        "steam",  # 😤 face with steam
+        "fly",  # 🪰 the insect, not the verb
+        "plant",  # 🪴 potted plant for all "X (plant)" entries
+        "tree",  # 🌲 evergreen for all "X (tree)" entries
+        "ball",  # ⛹️ person bouncing ball
+        "bag",  # 👝 clutch bag
+        "fight",  # 🫯 not a fight
+        "cloud",  # 🫯 not a cloud
+        "video",  # 🎮 video game, not video
+        "rescue",  # ⛑️ rescue worker helmet
+        "exchange",  # 💱 currency exchange
+        "cut",  # 🥩 cut of meat, not "to cut"
+        "key",  # 🔐 locked with key
+        "walking",  # 🚶 person walking
+        "running",  # 🏃 person running
+        "climbing",  # 🧗 person climbing
+        "speaking",  # 🗣️ speaking head
+        "playing",  # 🤽 person playing
+        "feeding",  # 👩‍🍼 person feeding
+        "shooting",  # 🌠 shooting star
+        "clapping",  # 👏 clapping hands
+        "cooking",  # 🍳 cooking emoji
+        "holding",  # 🥹 face holding back tears
+        # More wrong-sense matches from remaining audit
+        "paper",  # 🏮 red lantern for "paper"
+        "track",  # 🛤️ railroad for "track record"
+        "vertical",  # 🚦 traffic light for "vertical"
+        "speaker",  # 🔇 muted speaker for "speaker (person)"
+        "square",  # 🟥 red square for "plaza"
+        "wrapped",  # 🎁 gift for "wrapped, bound"
+        "volume",  # 🔈 speaker for "volume (book)"
+        "mobile",  # 📱 phone for "mobile, moveable"
+        "flash",  # 📸 camera flash for "to shine"
+        "identification",  # 🪪 ID card for "locating"
+        "service",  # 🐕‍🦺 service dog for "service, term"
+        "ground",  # ⛱️ umbrella on ground
+        "machine",  # 🎰 slot machine for "mechanism"
+        "liquid",  # 🫗 pouring for "liquid, drop"
+        "vehicle",  # 🚙 SUV for any vehicle mention
+        "window",  # 🪟 window pane for "window, gap"
+        "information",  # ℹ️ info symbol
+        "child",  # 🧒 child emoji
    }
 )

@ -832,9 +969,11 @@ def build_vocab_deck(
        if word_nikkud not in word_to_pos_cat:
            word_to_pos_cat[word_nikkud] = _categorize_pos(pos_raw) if pos_raw else "Other"

-    # Sort entries by frequency (null → 999999), applying limit after sort
+    # Sort entries by effective frequency (pseudo_frequency for confusables,
+    # else regular frequency; null → 999999), applying limit after sort
    def _freq_key(item: tuple[str, dict]) -> int:
-        return item[1].get("frequency") or 999_999
+        e = item[1]
+        return e.get("pseudo_frequency") or e.get("frequency") or 999_999

    sorted_entries = sorted(words.items(), key=_freq_key)
    if limit:
@ -860,7 +999,6 @@ def build_vocab_deck(
        meaning = re.sub(r"\s{2,}", " ", meaning).strip(", ;:")
        meaning = re.sub(r"(\w)\(", r"\1 (", meaning)  # space before opening paren
        meaning = re.sub(r",(\S)", r", \1", meaning)  # space after comma
-        meaning_raw = entry.get("meaning_raw", "") or ""
        slug = entry.get("slug", "") or ""
        frequency = entry.get("frequency") or 999_999
        audio_file = entry.get("audio_file", "") or ""
@ -895,20 +1033,22 @@ def build_vocab_deck(
        else:
            freq_display = "Unlisted"

-        # Emoji: use entry's emoji if emoji_visible, else fall back to emoji_lookup
+        # Emoji: use entry's emoji if emoji_visible, else fall back to emoji_lookup.
+        # Skip fallback for verbs — keyword matching on verb definitions produces
+        # wrong-sense emoji (e.g. "to cut" → 🥩, "to arm" → 🦾).
        emoji_str = ""
        if entry.get("emoji_visible") and entry.get("emoji"):
            emoji_str = entry["emoji"]
-        elif not emoji_str and emoji_lookup:
+        elif emoji_lookup and not meaning.startswith("to "):
            meaning_clean_for_emoji = EMOJI_RE.sub("", meaning).strip()
            for kw in re.sub(r"[^\w\s]", " ", meaning_clean_for_emoji.lower()).split()[:5]:
                if len(kw) > 2 and kw not in _EMOJI_STOP and kw in emoji_lookup:
                    emoji_str = emoji_lookup[kw]
                    break

-        # Extract Hebrew prepositions from meaning_raw
-        preps = HBPAREN_RE.findall(meaning_raw)
-        prep_str = " ".join(f"({p})" for p in preps)
+        # Hebrew prepositions — extracted upstream by list scraper
+        entry_prep = entry.get("prep")
+        prep_str = " ".join(f"({p})" for p in entry_prep.split()) if entry_prep else ""

        # Audio — use audio_file from entry; for confusables it's already slug-based
        audio_tag = ""
@ -1118,25 +1258,12 @@ def build_conj_deck(
        root = ".".join(root_list)
        voice = VOICE_MAP.get(binyan, "")

-        meaning_raw = entry.get("meaning_raw", "") or ""
        meaning = entry.get("meaning", "") or ""
-        # Extract Hebrew preposition — strip from meaning, show on Hebrew side
+        # Hebrew preposition — extracted upstream by scraper
        prep_str = ""
-        conj_prep = conj.get("prep")
+        conj_prep = conj.get("prep") or entry.get("prep")
        if conj_prep:
-            # Strip any parentheses from stored prep value
            prep_str = conj_prep.strip("() ")
-        elif meaning_raw:
-            preps = HBPAREN_RE.findall(meaning_raw)
-            if preps:
-                prep_str = preps[0]
-        # Strip Hebrew prepositions from English meaning to avoid duplication
-        if prep_str:
-            meaning = HBPAREN_RE.sub("", meaning).strip()
-            # Also strip from meaning_raw patterns like "(על)"
-            meaning = re.sub(r"\(\s*" + re.escape(prep_str) + r"\s*-?\s*\)", "", meaning).strip()
-            # Clean up double spaces and trailing commas
-            meaning = re.sub(r"\s{2,}", " ", meaning).strip(", ")

        related = [(f, w, m) for f, w, m in root_words.get(root, []) if w != infinitive]
        if related:
@ -1433,9 +1560,12 @@ def build_confusables_deck(
            guid = genanki.guid_for("confusable", entry["word"].get("ktiv_male", unique_key))
        guid_to_entries.setdefault(guid, []).append(entry)

+    def _eff_freq(e: dict) -> int:
+        return e.get("pseudo_frequency") or e.get("frequency") or 999_999
+
    for guid, group_entries in sorted(
        guid_to_entries.items(),
-        key=lambda x: sum(e.get("frequency") or 999_999 for e in x[1]) / len(x[1]),
+        key=lambda x: sum(_eff_freq(e) for e in x[1]) / len(x[1]),
    ):
        if guid in seen_guids:
            continue
@ -1454,9 +1584,13 @@ def build_confusables_deck(
                unique_entries.append(e)
        if len(unique_entries) < 2:
            continue
+        # Sort by pseudo/frequency so most common meaning appears first
+        unique_entries.sort(key=_eff_freq)
+        if len(unique_entries) < 2:
+            continue

        word_no_nik = unique_entries[0]["word"].get("ktiv_male", "")
-        words_display = " / ".join(e["word"]["nikkud"] for e in unique_entries)
+        words_display = word_no_nik  # Show ktiv male (shared form) on front

        defs_parts: list[str] = []
        audio_parts: list[str] = []
@ -1525,8 +1659,8 @@ def write_conf_apkg(
 PLURAL_FRONT_SG = """
 <div class="hebrew" style="color:#1a1a8c;">{{Singular}}</div>
 {{#SingularAudio}}<div>{{SingularAudio}}</div>{{/SingularAudio}}
-<div class="sec-label">{{Meaning}}</div>
-<div class="hint" style="font-size:28px;">יָחִיד ← רַבִּים</div>
+<div class="meaning" style="font-size:28px;">{{Meaning}}</div>
+<div class="plural-direction">יָחִיד ← רַבִּים</div>
 """

 PLURAL_BACK_SG = """
@ -1542,14 +1676,14 @@ PLURAL_BACK_SG = """
 PLURAL_FRONT_PL = """
 <div class="hebrew" style="color:#1a1a8c;">{{Plural}}</div>
 {{#PluralAudio}}<div>{{PluralAudio}}</div>{{/PluralAudio}}
-<div class="hint" style="font-size:28px;">רַבִּים ← יָחִיד</div>
+<div class="plural-direction">רַבִּים ← יָחִיד</div>
 """

 PLURAL_BACK_PL = """
 {{FrontSide}}<hr>
 <div class="hebrew">{{Singular}}</div>
 {{#SingularAudio}}<div>{{SingularAudio}}</div>{{/SingularAudio}}
-<div class="sec-label" style="text-align:center;display:block;">{{Meaning}}</div>
+<div class="meaning" style="font-size:28px;">{{Meaning}}</div>
 <div class="sec-table">
 {{#Gender}}<div class="sec-label"><span class="sec-key">מִין:</span><span class="sec-val">{{Gender}}</span></div>{{/Gender}}
 {{#Mishkal}}<div class="sec-label"><span class="sec-key">מִשְׁקָל:</span><span class="sec-val">{{Mishkal}}</span></div>{{/Mishkal}}
@ -1646,9 +1780,9 @@ def build_plural_deck(
    irregular_count = len(irregulars)
    target_regular = irregular_count * 2
    mishkal_count = len(by_mishkal) or 1
-    per_mishkal = max(2, target_regular // mishkal_count)
+    # Over-sample per mishkal to compensate for small patterns, then trim
+    per_mishkal = max(3, (target_regular * 3) // (mishkal_count * 2))

-    selected: list[tuple[str, dict, dict]] = list(irregulars)
    regular_pool: list[tuple[str, dict, dict]] = []
    for _mishkal, entries in sorted(by_mishkal.items()):
        entries.sort(key=lambda e: e[1].get("frequency") or 999_999)
@ -1659,7 +1793,24 @@ def build_plural_deck(
        regular_pool.sort(key=lambda e: e[1].get("frequency") or 999_999)
        regular_pool = regular_pool[:target_regular]

-    selected.extend(regular_pool)
+    # Sort both pools by frequency, then interleave for homogeneous 2:1 regular:irregular
+    irregulars.sort(key=lambda e: e[1].get("frequency") or 999_999)
+    regular_pool.sort(key=lambda e: e[1].get("frequency") or 999_999)
+
+    # Interleave: for every 1 irregular, insert 2 regulars
+    selected: list[tuple[str, dict, dict]] = []
+    ri = 0  # regular index
+    for _ii, irr in enumerate(irregulars):
+        # Insert 2 regulars before each irregular (when available)
+        for _ in range(2):
+            if ri < len(regular_pool):
+                selected.append(regular_pool[ri])
+                ri += 1
+        selected.append(irr)
+    # Append remaining regulars
+    while ri < len(regular_pool):
+        selected.append(regular_pool[ri])
+        ri += 1

    note_count = 0
    for _unique_key, entry, noun_inflection in selected:
@ -1682,12 +1833,20 @@ def build_plural_deck(
        sg_audio = ""
        pl_audio = ""
        if include_audio:
-            sg_tag = _audio_tag(singular_ktiv)
+            slug = entry.get("slug", "")
+            sg_tag = _audio_tag(singular_ktiv, slug=slug)
            if sg_tag:
                sg_audio = sg_tag
                mp3_path = AUDIO_DIR / sg_tag.removeprefix("[sound:").removesuffix("]")
                if mp3_path not in media_files:
                    media_files.append(mp3_path)
+            # Plural audio: {slug}_plural.mp3
+            if slug:
+                pl_mp3 = AUDIO_DIR / f"{slug}_plural.mp3"
+                if pl_mp3.exists():
+                    pl_audio = f"[sound:{pl_mp3.name}]"
+                    if pl_mp3 not in media_files:
+                        media_files.append(pl_mp3)

        mishkal_eng = noun_inflection.get("mishkal") or ""
        tags = [RELEASE_TAG]
--- a/data/en_50k.txt
+++ b/data/en_50k.txt
--- a/data/words.json
+++ b/data/words.json
--- a/docs/superpowers/specs/2026-03-15-adaptive-sentence-difficulty-design.md
+++ b/docs/superpowers/specs/2026-03-15-adaptive-sentence-difficulty-design.md
@ -0,0 +1,150 @@
+# Adaptive Sentence Difficulty Cloze — v0.20 Design Spec
+
+**Date:** 2026-03-15
+**Status:** Approved
+**Release:** v0.20
+
+## Problem
+
+Cloze cards currently select the example sentence closest to 9 words in length. This ignores whether the surrounding context words are familiar to the learner. A sentence full of rare words is harder than one with common words, regardless of length.
+
+## Solution
+
+Replace the length-based `_score()` function in `epub_examples.py` with a **frequency-based difficulty score**. The easiest sentence (most common context words) becomes the cloze. All vetted sentences remain on the card, ordered easy→hard.
+
+## Scoring Pipeline
+
+### Token Frequency Lookup (5-tier)
+
+Given a nikkud sentence token, resolve its frequency rank:
+
+1. **Known mapping** — look up token in the nikkud→ktiv_male map built from words.json headwords, conjugations, and inflections (94k mappings). If found, look up the ktiv_male in the frequency data.
+2. **Nikkud prefix stripping** — use `_try_strip_prefix()` to strip validated Hebrew prefixes (בהוכלמש), then resolve the remainder via the known mapping.
+3. **Academy rules converter** — apply `nikkud_to_ktiv_male.convert()` (91.6% accuracy) to produce ktiv_male, look up in frequency data.
+4. **strip_nikkud fallback** — use `helpers.strip_nikkud()` as a lossy fallback.
+5. **Ktiv_male prefix stripping** — strip 1-2 character Hebrew prefixes from the converted/stripped form and look up the stem.
+
+Tokens not found in any tier are assigned a default high rank (50,000).
+
+**Coverage:** ~93% of example sentence tokens resolve to a frequency rank (measured empirically on 7,588 sentences).
+
+**Frequency data source:** Use `frequency_lookup.py` which auto-selects `frequency_clean.json` when available, falling back to `frequency_cache.json`.
+
+### Sentence Difficulty Score
+
+For a given word's candidate sentence:
+
+1. Tokenize: split on whitespace, strip punctuation (.,!?;:"'"״׳–—()[]{}), split on maqaf (־).
+2. Exclude the target word's token using `cloze_word_start`/`cloze_word_end` offsets from the matched sentence.
+3. For each remaining token (length >= 2), resolve its frequency rank via the 5-tier pipeline.
+4. **Score = median frequency rank of context tokens.**
+
+Lower score = easier (context words are more common). Median resists outliers (one rare proper noun shouldn't dominate).
+
+### Integration Point
+
+The scoring integrates into `epub_examples.py`'s existing `_score()` closure inside `update_words_json()` (line ~677). Currently:
+
+```python
+def _score(s: dict) -> tuple[int,]:
+    wc = s["word_count"]
+    length_score = abs(wc - 9) if not (6 <= wc <= 12) else 0
+    return (length_score,)
+```
+
+New scoring replaces length with frequency-based difficulty. The `_score` function gains access to the frequency pipeline via closure over the nikkud_map, nikkud_index, and freq_data built once at the start of `update_words_json()`.
+
+**Minimum sentence length:** Reduced from 4 words to 3 words (`MIN_WORDS = 3` in epub_examples.py). Hebrew is more concise than English — 3-word sentences are valid and common. This expands the candidate pool for cloze selection.
+
+**Behavioral change:** Because `pool.sort(key=_score)` determines which 3 sentences are selected as `best = pool[:3]`, changing the scoring function changes **which sentences are selected**, not just their order. This is intentional — we want the easiest sentences as cloze candidates, not the closest-to-9-words ones. Existing cloze GUIDs will be preserved when the same sentence text is re-selected; entries where a different sentence wins will get new GUIDs.
+
+## Data Model Changes
+
+### words.json
+
+The `examples.cloze` dict (single sentence) gains an optional `difficulty_score` field:
+
+```json
+{
+  "examples": {
+    "vetted": [
+      {"text": "...", "source": "...", "match_method": "..."},
+      {"text": "...", "source": "...", "match_method": "..."}
+    ],
+    "cloze": {
+      "text": "...",
+      "cloze_word_start": 5,
+      "cloze_word_end": 10,
+      "cloze_hint": null,
+      "cloze_guid": "abc123",
+      "difficulty_score": 234
+    }
+  }
+}
+```
+
+The vetted list is also sorted by difficulty (easiest first), so the card back shows sentences in pedagogically useful order.
+
+### SCHEMA.yaml
+
+Add `difficulty_score` as optional integer field under `examples.cloze`.
+
+## Implementation Scope
+
+### New file: `sentence_difficulty.py`
+
+Standalone module for sentence scoring. No pipeline step — called by `epub_examples.py`.
+
+- `score_sentence(sentence_text: str, target_start: int, target_end: int, nikkud_map: dict, nikkud_index: dict, freq_data: dict) -> int` — returns median context frequency rank. Uses `target_start`/`target_end` character offsets to exclude the cloze target token.
+- `build_nikkud_map(words: dict) -> dict[str, str]` — builds nikkud→ktiv_male lookup from words.json (headwords + conjugation forms + noun inflections). Returns `{nikkud_form: ktiv_male_form}`. Implementation note: should share iteration logic with `epub_examples._build_nikkud_index()` or derive from its output to avoid duplicating the traversal of words.json forms.
+- `_resolve_token_frequency(token: str, nikkud_map: dict, nikkud_index: dict, freq_data: dict) -> int` — the 5-tier lookup. Uses `_try_strip_prefix` from epub_examples (made importable by removing underscore or adding a public wrapper).
+
+### Modified files
+
+- **`epub_examples.py`**:
+  - Import `sentence_difficulty.score_sentence` and `sentence_difficulty.build_nikkud_map`
+  - In `update_words_json()`: build nikkud_map and load freq_data once at start (before per-word loop)
+  - Replace `_score()` closure with frequency-based scoring that calls `score_sentence()`
+  - Sort vetted list by difficulty score (easiest first)
+  - Store `difficulty_score` in the cloze dict
+  - Make `_try_strip_prefix` importable (rename to `try_strip_prefix` or add public alias)
+- **`frequency_lookup.py`** — add `get_freq_data() -> dict` public accessor to expose the loaded frequency dict (avoids accessing private `_freq` directly)
+- **`SCHEMA.yaml`** — add `difficulty_score` field
+- **`run.py`** — no changes; scoring happens inside epub_examples step
+
+### Not modified
+
+- **`apkg_builder.py`** — reads cloze as-is; vetted order is already respected
+- **`nikkud_to_ktiv_male.py`** — used as-is
+- **Card templates** — no changes needed
+
+## Dependencies
+
+- `nikkud_to_ktiv_male.convert()` — Academy rules converter (already written)
+- `epub_examples._try_strip_prefix()` / `_build_nikkud_index()` — nikkud prefix stripping and index
+- `frequency_lookup.py` — loads frequency data (auto-selects clean vs cache)
+- `helpers.strip_nikkud()` — fallback converter
+
+## Validation
+
+- **Unit tests** for `score_sentence()` with known easy/hard sentences
+- **Unit tests** for `_resolve_token_frequency()` covering all 5 tiers
+- **Integration test**: verify cloze selection picks easiest sentence, vetted list is sorted
+- **Spot check**: manually review 10 words with 3+ sentences to confirm ordering
+- **Regression**: existing tests pass, GUID coverage unchanged, deck validates
+
+## Constraints
+
+- `examples.cloze` remains a single dict (not converted to list)
+- No new Anki card types or fields
+- No runtime JS in Anki cards
+- No network calls during scoring
+- `difficulty_score` is informational metadata; card rendering doesn't depend on it
+- Existing cloze GUIDs preserved when the same sentence is re-selected
+
+## Scope Exclusions (Future Work)
+
+- **Pronominal suffix stripping** — would improve the ~7% unscored token rate; deferred (PROJECT_NOTES.md)
+- **Kamatz katan disambiguation** — requires morphological analysis; accepted limitation
+- **Per-learner adaptive difficulty** — requires Anki plugin; out of scope for static deck
+- **Multiple cloze sentences per card** — would require schema migration to list; deferred
--- a/epub_examples.py
+++ b/epub_examples.py
@ -18,7 +18,9 @@ import zipfile
 from html.parser import HTMLParser
 from pathlib import Path

+import frequency_lookup
 from helpers import strip_nikkud
+from sentence_difficulty import build_nikkud_map, score_sentence

 logger = logging.getLogger(__name__)

@ -57,7 +59,7 @@ def _discover_epubs() -> dict[str, str]:


 # Sentence length bounds (word count)
-MIN_WORDS = 4
+MIN_WORDS = 3
 MAX_WORDS = 15


@ -448,6 +450,10 @@ def _try_strip_prefix(token: str, nikkud_index: dict) -> list[tuple[str, str, st
    return results


+# Public alias for use by sentence_difficulty module
+try_strip_prefix = _try_strip_prefix
+
+
 def _build_nikkud_index(words: dict) -> dict[str, list[tuple[str, str]]]:
    """Build a mapping from nikkud form to list of (unique_key, match_type).

@ -654,6 +660,11 @@ def update_words_json(words: dict, matches: dict, confusable_keys: set[str]) ->

    updated = 0

+    # Build frequency scoring infrastructure (once for all words)
+    nikkud_index = _build_nikkud_index(words)
+    nikkud_map = build_nikkud_map(words)
+    freq_data = frequency_lookup.get_freq_data()
+
    for unique_key, sent_list in matches.items():
        if unique_key not in words:
            continue
@ -673,11 +684,18 @@ def update_words_json(words: dict, matches: dict, confusable_keys: set[str]) ->
        prefix_only = [s for s in unique if "prefix" in s["match_method"]]
        pool = direct if direct else prefix_only

-        # Score: prefer 6–12 word sentences
+        # Score: prefer sentences with easier (more common) context words
        def _score(s: dict) -> tuple[int,]:
-            wc = s["word_count"]
-            length_score = abs(wc - 9) if not (6 <= wc <= 12) else 0
-            return (length_score,)
+            return (
+                score_sentence(
+                    s["text"],
+                    s["char_offset"],
+                    s["char_end"],
+                    nikkud_map,
+                    nikkud_index,
+                    freq_data,
+                ),
+            )

        pool.sort(key=_score)
        best = pool[:3]
@ -712,6 +730,7 @@ def update_words_json(words: dict, matches: dict, confusable_keys: set[str]) ->
                "cloze_word_end": top["char_end"],
                "cloze_hint": None,
                "cloze_guid": cloze_guid,
+                "difficulty_score": _score(top)[0],
            }
        elif is_confusable:
            examples.pop("cloze", None)
@ -719,9 +738,87 @@ def update_words_json(words: dict, matches: dict, confusable_keys: set[str]) ->
        examples["rejected_count"] = 0
        updated += 1

+    # Deduplicate shared examples across confusable groups
+    cleared = _deduplicate_confusable_examples(words)
+    if cleared:
+        logger.info(f"    Cleared shared examples from {cleared} confusable entries")
+
    return updated


+def _deduplicate_confusable_examples(words: dict) -> int:
+    """Remove shared examples from less-common confusable group members.
+
+    After example matching assigns sentences, confusable entries often share
+    identical examples (matched via shared nikkud forms).  This function keeps
+    examples only on the highest-frequency member, clearing others.
+
+    Args:
+        words: The full words.json dict, modified in place (examples already
+            assigned).
+
+    Returns:
+        Count of entries whose examples were cleared.
+    """
+    from collections import defaultdict
+
+    # Build confusable group map: group_id → [unique_key, ...]
+    group_map: dict[tuple[str, ...], list[str]] = defaultdict(list)
+    for key, entry in words.items():
+        cg = entry.get("confusable_group")
+        if cg:
+            group_id = tuple(sorted(cg))
+            group_map[group_id].append(key)
+
+    cleared = 0
+
+    for _group_id, members in group_map.items():
+        if len(members) < 2:
+            continue
+
+        # Collect vetted sentence text sets per member
+        member_texts: dict[str, frozenset[str]] = {}
+        for key in members:
+            vetted = (words[key].get("examples") or {}).get("vetted") or []
+            texts = frozenset(e.get("text", "") for e in vetted)
+            member_texts[key] = texts
+
+        # Find members with identical non-empty sentence sets
+        # Group members by their sentence set
+        text_groups: dict[frozenset[str], list[str]] = defaultdict(list)
+        for key, texts in member_texts.items():
+            if texts:  # skip entries with no examples
+                text_groups[texts].append(key)
+
+        # For each set of members sharing identical examples, keep only the
+        # highest-frequency one
+        for _texts, sharing_keys in text_groups.items():
+            if len(sharing_keys) < 2:
+                continue
+
+            # Sort by frequency_rank (lower = more common = winner).
+            # No frequency → sort last (use large sentinel).
+            # Tie-break: alphabetical by unique_key.
+            def _sort_key(k: str) -> tuple[int, str]:
+                rank = words[k].get("frequency_rank")
+                return (rank if rank is not None else 999999, k)
+
+            sharing_keys.sort(key=_sort_key)
+            winner = sharing_keys[0]
+            losers = sharing_keys[1:]
+
+            for loser_key in losers:
+                entry = words[loser_key]
+                examples = entry.get("examples") or {}
+                examples["vetted"] = []
+                examples.pop("cloze", None)
+                entry["examples"] = examples
+                cleared += 1
+                logger.debug(f"      Cleared examples from {loser_key} (kept on {winner})")
+
+    return cleared
+
+
 # ── Public API ───────────────────────────────────────────────────


--- a/frequency_lookup.py
+++ b/frequency_lookup.py
@ -74,6 +74,16 @@ def get_frequency_rank(word_no_nikkud: str) -> int | None:
    return _freq.get(clean)


+def get_freq_data() -> dict[str, int]:
+    """Return the full frequency dict (word -> rank).
+
+    Auto-loads from cache if not yet loaded.
+    """
+    if not _freq:
+        load()
+    return _freq
+
+
 if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
    load()
--- a/nikkud_to_ktiv_male.py
+++ b/nikkud_to_ktiv_male.py
@ -0,0 +1,185 @@
+"""Convert nikkud (vocalized) Hebrew to ktiv male (plene spelling).
+
+Implements Hebrew Academy rules for matres lectionis insertion:
+- Rule A: U vowel (kubutz) → always insert vav
+- Rule B: O vowel (holam on non-vav) → insert vav
+- Rule C: I vowel (hiriq) → insert yod (conditionally)
+- Rule D: E vowel (tsere) → insert yod (limited cases)
+- Rule E/F: Consonantal vav/yod doubling
+
+Reference: https://hebrew-academy.org.il/topic/hahlatot/missingvocalizationspelling/
+"""
+
+import unicodedata
+
+# Hebrew nikkud code points
+SHVA = "\u05b0"
+HATAF_SEGOL = "\u05b1"
+HATAF_PATAH = "\u05b2"
+HATAF_KAMATZ = "\u05b3"
+HIRIQ = "\u05b4"
+TSERE = "\u05b5"
+SEGOL = "\u05b6"
+PATAH = "\u05b7"
+KAMATZ = "\u05b8"
+HOLAM = "\u05b9"
+HOLAM_HASER = "\u05ba"
+KUBUTZ = "\u05bb"
+DAGESH = "\u05bc"
+METEG = "\u05bd"
+RAFE = "\u05bf"
+SHIN_DOT = "\u05c1"
+SIN_DOT = "\u05c2"
+
+VAV = "ו"
+YOD = "י"
+MAQAF = "־"
+
+VOWELS = {SHVA, HATAF_SEGOL, HATAF_PATAH, HATAF_KAMATZ, HIRIQ, TSERE, SEGOL, PATAH, KAMATZ, HOLAM, HOLAM_HASER, KUBUTZ}
+
+NIKKUD_MARKS = VOWELS | {DAGESH, METEG, RAFE, SHIN_DOT, SIN_DOT}
+
+
+def _parse_segments(text: str) -> list[tuple[str, list[str]]]:
+    """Parse nikkud text into (character, [marks]) segments."""
+    segments: list[tuple[str, list[str]]] = []
+    cur_char: str | None = None
+    cur_marks: list[str] = []
+
+    for ch in text:
+        if unicodedata.category(ch) == "Mn":
+            cur_marks.append(ch)
+        else:
+            if cur_char is not None:
+                segments.append((cur_char, cur_marks))
+            cur_char = ch
+            cur_marks = []
+
+    if cur_char is not None:
+        segments.append((cur_char, cur_marks))
+
+    return segments
+
+
+def _get_vowel(marks: list[str]) -> str | None:
+    """Extract the vowel mark from a list of combining marks."""
+    for m in marks:
+        if m in VOWELS:
+            return m
+    return None
+
+
+def _has_dagesh(marks: list[str]) -> bool:
+    return DAGESH in marks
+
+
+def _is_hebrew_letter(ch: str) -> bool:
+    return "\u05d0" <= ch <= "\u05ea"
+
+
+def convert(text: str) -> str:
+    """Convert nikkud Hebrew text to ktiv male.
+
+    Strips all nikkud marks and inserts matres lectionis (vav/yod)
+    according to Hebrew Academy spelling rules.
+    """
+    segments = _parse_segments(text)
+    result: list[str] = []
+
+    for i, (ch, marks) in enumerate(segments):
+        if not _is_hebrew_letter(ch):
+            # Non-Hebrew character: output as-is (no marks)
+            result.append(ch)
+            continue
+
+        vowel = _get_vowel(marks)
+        has_dag = _has_dagesh(marks)
+
+        # Output the base letter (strip all nikkud marks)
+        result.append(ch)
+
+        # --- Rule A: U vowel (kubutz) → always add vav ---
+        if vowel == KUBUTZ:
+            result.append(VAV)
+            continue
+
+        # --- Shuruk detection ---
+        # Vav with dagesh and no other vowel = shuruk (already a mater)
+        # Vav with dagesh AND a vowel = consonantal vav (ב with dagesh)
+        # If letter is vav with dagesh only → it's shuruk, already output
+        if ch == VAV and has_dag and vowel is None:
+            # Shuruk: vav IS the mater lectionis, already output
+            continue
+
+        # --- Rule B: O vowel (holam) → add vav ---
+        if vowel in (HOLAM, HOLAM_HASER):
+            if ch != VAV:
+                # Exception: holam before aleph (pe-aleph verbs) — no vav
+                # e.g., תֹּאבַד→תאבד, יֹאבַד→יאבד, נֹאבַד→נאבד
+                next_is_aleph = i + 1 < len(segments) and segments[i + 1][0] == "א"
+                if not next_is_aleph:
+                    result.append(VAV)
+            # If ch IS vav (holam male), vav already output
+            continue
+
+        # --- Rule C: I vowel (hiriq) → conditionally add yod ---
+        if vowel == HIRIQ:
+            if ch == YOD:
+                # Yod already present, don't double
+                continue
+
+            # Don't insert yod if next letter is already yod
+            if i + 1 < len(segments) and segments[i + 1][0] == YOD:
+                continue
+
+            # Rule C Section 3: Don't add yod if the NEXT consonant
+            # has shva (indicating shva nach on that consonant)
+            add_yod = True
+
+            if i + 1 < len(segments):
+                next_ch, next_marks = segments[i + 1]
+                next_vowel = _get_vowel(next_marks)
+
+                # Shva on next consonant = shva nach → don't add yod
+                # UNLESS next consonant also has dagesh (= shva na / doubled)
+                next_has_dagesh = _has_dagesh(next_marks)
+                if next_vowel == SHVA and not next_has_dagesh:
+                    add_yod = False
+                # No vowel on next consonant (word-final) = closed syllable
+                # → don't add yod (e.g., suffix -תי -נו -תם)
+                elif next_vowel is None and _is_hebrew_letter(next_ch):
+                    # Check if this is truly word-final or next-to-last
+                    remaining_letters = sum(1 for j in range(i + 1, len(segments)) if _is_hebrew_letter(segments[j][0]))
+                    if remaining_letters <= 2:
+                        # Short suffix like תי, נו — don't add yod
+                        add_yod = False
+
+            if add_yod:
+                result.append(YOD)
+            continue
+
+        # --- Rule D: E vowel (tsere/segol) → generally NO yod ---
+        # Exception (b): tsere before guttural/resh gets yod ONLY
+        # in word-initial position (dagesh substitution in Hif'il/noun patterns)
+        # e.g., הֵחֵל→היחל, תֵּאָבֵד→תיאבד, הֵרִיעַ→היריע
+        # but NOT mid-word: מְסַפֵּר→מספר, מְעַבֵּר→מעבר
+        if vowel == TSERE:
+            add_yod = False
+
+            if i + 1 < len(segments):
+                next_ch = segments[i + 1][0]
+                if next_ch in "אהחער":
+                    # Only at word-initial (pos 0) or after prefix (pos 1)
+                    # where dagesh substitution applies
+                    hebrew_pos = sum(1 for j in range(i) if _is_hebrew_letter(segments[j][0]))
+                    if hebrew_pos <= 1:
+                        add_yod = True
+
+            if add_yod:
+                result.append(YOD)
+            continue
+
+        # All other vowels (patah, kamatz, segol, shva, hataf-*):
+        # No mater lectionis insertion needed
+
+    return "".join(result)
--- a/pealim_detail_scrape.py
+++ b/pealim_detail_scrape.py
@ -40,6 +40,9 @@ SAVE_INTERVAL = 50  # write words.json every N processed entries

 WORDS_JSON = Path(__file__).parent / "data" / "words.json"

+# Regex for Hebrew prepositions wrapped in parentheses, e.g. "(על)" or "(ב-)"
+HBPAREN_RE = re.compile(r"\(([\u05b0-\u05ea\u05f0-\u05f4\-]+)\)")
+
 BINYAN_NAMES: tuple[str, ...] = ("Pa'al", "Nif'al", "Pi'el", "Pu'al", "Hitpa'el", "Hif'il", "Huf'al")
 _BINYAN_NAMES_LOWER: tuple[str, ...] = tuple(b.lower() for b in BINYAN_NAMES)

@ -948,9 +951,17 @@ def _scrape_verb_detail(slug: str, mo_html: str, vl_html: str, existing_conj: di
    binyan = _extract_binyan_from_page(mo_soup)

    meaning = ""
+    prep: str | None = None
    lead_div = mo_soup.find("div", class_="lead")
    if lead_div:
        meaning = lead_div.get_text(strip=True)
+        # Extract preposition(s) from the lead text, e.g. "(על)" → "על"
+        prep_matches = HBPAREN_RE.findall(meaning)
+        if prep_matches:
+            prep = " ".join(prep_matches)
+    # Fall back to any prep already stored (e.g. from a previous manual edit)
+    if prep is None:
+        prep = existing.get("prep")

    # Parse active forms
    mo_active = _parse_conjugation_table(mo_soup, passive=False)
@ -1002,7 +1013,7 @@ def _scrape_verb_detail(slug: str, mo_html: str, vl_html: str, existing_conj: di
        "binyan": binyan,
        "binyan_hebrew": BINYAN_HEBREW.get(binyan, ""),
        "meaning": meaning,
-        "prep": existing.get("prep"),
+        "prep": prep,
        "active_forms": active_forms,
        "hufal_pual_forms": hufal_pual_forms,
        "reference_form_passive": reference_form_passive,
--- a/pealim_list_scrape.py
+++ b/pealim_list_scrape.py
@ -82,10 +82,13 @@ BINYAN_HEBREW: dict[str, str] = {

 # Regex for extracting emoji characters
 EMOJI_RE = re.compile(
-    r"[\U0001F300-\U0001FFFF\U00002600-\U000027BF\U0001F000-\U0001F9FF\u2600-\u26FF\u2700-\u27BF]+",
+    r"[\U0001F300-\U0001FFFF\U00002600-\U000027BF\U0001F000-\U0001F9FF\u2600-\u26FF\u2700-\u27BF\uFE0E\uFE0F\u200D]+",
    re.UNICODE,
 )

+# Regex for extracting Hebrew prepositions wrapped in parentheses, e.g. "(על)" or "(ב-)"
+HBPAREN_RE = re.compile(r"\(([\u05b0-\u05ea\u05f0-\u05f4\-]+)\)")
+
 # Fields that must never be overwritten when updating an existing entry
 PROTECTED_FIELDS = frozenset(
    [
@ -149,6 +152,7 @@ def _default_entry() -> dict:
        "image": None,
        "image_source": None,
        "hint": "",
+        "prep": None,
        "shared_roots": [],
        "confusable_group": None,
        "confusables_guid": None,
@ -170,8 +174,9 @@ def _extract_emoji(text: str) -> str | None:


 def _clean_meaning(raw: str) -> str:
-    """Strip emoji and extra whitespace from a raw meaning string."""
+    """Strip emoji, Hebrew parenthesized prepositions, and extra whitespace from a raw meaning string."""
    cleaned = EMOJI_RE.sub("", raw)
+    cleaned = HBPAREN_RE.sub("", cleaned)
    return " ".join(cleaned.split())


@ -453,6 +458,9 @@ def _merge_row(
    emoji = _extract_emoji(meaning_raw_raw)
    tags = _build_tags(pos_en, root)
    audio_file = _compute_audio_file(slug, ktiv_male)
+    # Extract Hebrew preposition(s) from the raw meaning (e.g. "(על)" → "על")
+    prep_matches = HBPAREN_RE.findall(meaning_raw)
+    prep: str | None = " ".join(prep_matches) if prep_matches else None

    # ---- locate existing entry ----
    unique_key: str | None = slug_index.get(slug) if slug else None
@ -468,6 +476,7 @@ def _merge_row(
        entry["pos_hebrew"] = pos_heb
        entry["meaning"] = meaning
        entry["meaning_raw"] = meaning_raw
+        entry["prep"] = prep
        entry["audio_url"] = audio_url
        entry["audio_file"] = audio_file
        entry["tags"] = tags
@ -484,6 +493,7 @@ def _merge_row(
        entry["pos_hebrew"] = pos_heb
        entry["meaning"] = meaning
        entry["meaning_raw"] = meaning_raw
+        entry["prep"] = prep
        entry["emoji"] = emoji
        entry["emoji_source"] = "from_pealim" if emoji else None
        entry["audio_url"] = audio_url
--- a/release.py
+++ b/release.py
@ -20,8 +20,11 @@ from pathlib import Path

 import requests

+sys.path.insert(0, "/home/node/projects")
+import load_keeshare
+
 REPO_API = "https://git.nevo.engineer/api/v1/repos/nevo/hebrew_flash_cards"
-FORGEJO_TOKEN = "f023bd4cfd4b77aac584647f2fa8481df3906578"
+FORGEJO_TOKEN: str = load_keeshare.get_entry("git.nevo.engineer")["password"]
 OUTPUT_DIR = Path(__file__).parent / "output"

 # All deck variants to include in release
--- a/scripts/assign_pseudo_frequency.py
+++ b/scripts/assign_pseudo_frequency.py
@ -0,0 +1,269 @@
+#!/usr/bin/env python3
+"""Assign pseudo-frequency to confusable groups using English word frequency.
+
+Problem: Confusable entries share the same ktiv_male and thus the same Hebrew
+frequency rank.  This script uses English frequency to differentiate them so
+Anki sorts more-common meanings first.
+
+Algorithm:
+  1. For each confusable group where all entries share the same Hebrew frequency,
+     extract the first meaningful English keyword from each entry's meaning field.
+  2. Look up English frequency rank for each keyword.
+  3. Assign pseudo_frequency: the most frequent English meaning keeps the original
+     Hebrew rank; less frequent meanings get progressively higher (worse) ranks
+     by adding an offset (100 * position in group).
+
+Usage:
+    python3 scripts/assign_pseudo_frequency.py              # assign and save
+    python3 scripts/assign_pseudo_frequency.py --dry-run    # preview only
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import re
+from collections import defaultdict
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+PROJECT_ROOT = Path(__file__).parent.parent
+WORDS_JSON = PROJECT_ROOT / "data" / "words.json"
+EN_FREQ_PATH = PROJECT_ROOT / "data" / "en_50k.txt"
+
+# Words too common/vague to use as frequency signal
+_EN_STOP = frozenset(
+    {
+        "to",
+        "be",
+        "a",
+        "an",
+        "the",
+        "of",
+        "in",
+        "on",
+        "at",
+        "for",
+        "and",
+        "with",
+        "by",
+        "or",
+        "but",
+        "not",
+        "as",
+        "its",
+        "it",
+        "is",
+        "was",
+        "are",
+        "from",
+        "that",
+        "this",
+        "have",
+        "has",
+        "had",
+        "do",
+        "does",
+        "did",
+        "will",
+        "would",
+        "can",
+        "could",
+        "may",
+        "might",
+        "shall",
+        "should",
+        "must",
+        "no",
+        "yes",
+        "very",
+        "too",
+        "also",
+        "just",
+        "only",
+        "so",
+        "up",
+        "out",
+        "into",
+        "over",
+        "after",
+        "before",
+        "about",
+        "more",
+        "than",
+        "other",
+        "some",
+        "any",
+        "all",
+        "each",
+        "every",
+        "both",
+        "few",
+        "many",
+        "much",
+        "most",
+        "such",
+        "own",
+        "same",
+        "well",
+        "still",
+        "even",
+        "how",
+        "what",
+        "when",
+        "where",
+        "which",
+        "who",
+        "whom",
+        "whose",
+        "why",
+        "because",
+        "if",
+        "then",
+        "else",
+        "while",
+        "until",
+        "though",
+        "whether",
+    }
+)
+
+
+def _load_en_freq() -> dict[str, int]:
+    """Load English frequency data: word -> rank (1 = most common)."""
+    freq: dict[str, int] = {}
+    rank = 1
+    with open(EN_FREQ_PATH, encoding="utf-8") as f:
+        for line in f:
+            parts = line.strip().split()
+            if parts:
+                word = parts[0].lower()
+                if word not in freq:
+                    freq[word] = rank
+                    rank += 1
+    return freq
+
+
+def _extract_keywords(meaning: str) -> list[str]:
+    """Extract meaningful English keywords from a meaning string.
+
+    Returns list of lowercase words, filtered for stop words and short words.
+    """
+    # Strip parenthesized content, punctuation
+    cleaned = re.sub(r"\([^)]*\)", " ", meaning)
+    cleaned = re.sub(r"[^\w\s]", " ", cleaned)
+    return [w.lower() for w in cleaned.split() if len(w) > 2 and w.lower() not in _EN_STOP]
+
+
+def assign_pseudo_frequencies(
+    words: dict,
+    en_freq: dict[str, int],
+    dry_run: bool = False,
+) -> int:
+    """Assign pseudo_frequency to confusable groups. Returns count of changes."""
+
+    # Group by confusables_guid
+    groups: dict[str, list[str]] = defaultdict(list)
+    for key, entry in words.items():
+        cg = entry.get("confusables_guid")
+        if cg:
+            groups[cg].append(key)
+
+    changes = 0
+    assigned_groups = 0
+    skipped_diff = 0
+    skipped_no_en = 0
+
+    for _guid, keys in groups.items():
+        entries = [words[k] for k in keys]
+        freqs = [e.get("frequency") for e in entries]
+
+        # Skip groups that are already differentiated
+        unique_freqs = set(freqs)
+        if len(unique_freqs) > 1:
+            skipped_diff += 1
+            continue
+
+        base_freq = freqs[0]  # All same (or all None)
+
+        # Look up English frequency for each entry
+        en_ranks: list[tuple[int, str]] = []  # (en_rank, key)
+        for key, entry in zip(keys, entries, strict=True):
+            keywords = _extract_keywords(entry.get("meaning", ""))
+            en_rank = 999_999
+            for kw in keywords[:5]:
+                r = en_freq.get(kw)
+                if r is not None:
+                    en_rank = r
+                    break
+            en_ranks.append((en_rank, key))
+
+        # Sort by English frequency (lower rank = more common)
+        en_ranks.sort()
+
+        # Check if all entries have the same English rank (no signal)
+        if len({r for r, _ in en_ranks}) <= 1:
+            skipped_no_en += 1
+            continue
+
+        assigned_groups += 1
+
+        # Assign pseudo_frequency: most common gets base, others get offset
+        for position, (en_rank, key) in enumerate(en_ranks):
+            pseudo = base_freq + position * 100 if base_freq is not None else 50000 + en_rank
+
+            if not dry_run:
+                words[key]["pseudo_frequency"] = pseudo
+            changes += 1
+
+            if dry_run:
+                meaning = words[key].get("meaning", "")[:40]
+                logger.info(
+                    "  [en:%5d] pseudo=%6d  %s",
+                    en_rank,
+                    pseudo,
+                    meaning,
+                )
+
+    logger.info(
+        "Pseudo-frequency: %d groups assigned, %d already differentiated, %d no English signal",
+        assigned_groups,
+        skipped_diff,
+        skipped_no_en,
+    )
+    return changes
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Assign pseudo-frequency to confusables")
+    parser.add_argument("--dry-run", action="store_true", help="Preview without saving")
+    args = parser.parse_args()
+
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s %(message)s",
+    )
+
+    logger.info("Loading English frequency data: %s", EN_FREQ_PATH)
+    en_freq = _load_en_freq()
+    logger.info("English frequency: %d entries", len(en_freq))
+
+    with open(WORDS_JSON, encoding="utf-8") as f:
+        words: dict = json.load(f)
+
+    changes = assign_pseudo_frequencies(words, en_freq, dry_run=args.dry_run)
+
+    if args.dry_run:
+        logger.info("Dry run — %d changes would be made", changes)
+        return
+
+    with open(WORDS_JSON, "w", encoding="utf-8") as f:
+        json.dump(words, f, ensure_ascii=False, indent=2)
+
+    logger.info("Saved %d pseudo-frequency assignments to words.json", changes)
+
+
+if __name__ == "__main__":
+    main()
--- a/scripts/validate_data.py
+++ b/scripts/validate_data.py
@ -685,6 +685,57 @@ def test_no_stripped_form_sentence_collisions(data: dict[str, Any]) -> None:
    _pass(name)


+def test_no_shared_confusable_examples(data: dict[str, Any]) -> None:
+    """Within each confusable group, no two entries should share the same set of vetted sentence texts.
+
+    Shared examples indicate the deduplication step in epub_examples.py
+    failed to assign examples to only the highest-frequency member.
+    """
+    name = "no_shared_confusable_examples"
+    errors: list[str] = []
+
+    from collections import defaultdict
+
+    # Build confusable group map
+    group_map: dict[tuple[str, ...], list[str]] = defaultdict(list)
+    for key, entry in data.items():
+        cg = entry.get("confusable_group")
+        if cg:
+            group_id = tuple(sorted(cg))
+            group_map[group_id].append(key)
+
+    for _group_id, members in group_map.items():
+        if len(members) < 2:
+            continue
+
+        # Collect sentence text sets per member
+        text_sets: dict[str, frozenset[str]] = {}
+        for key in members:
+            vetted = (data[key].get("examples") or {}).get("vetted") or []
+            texts = frozenset(e.get("text", "") for e in vetted)
+            if texts:
+                text_sets[key] = texts
+
+        # Check for identical sets
+        seen: dict[frozenset[str], str] = {}
+        for key, texts in text_sets.items():
+            if texts in seen:
+                meaning_a = (data[seen[texts]].get("meaning") or "")[:30]
+                meaning_b = (data[key].get("meaning") or "")[:30]
+                errors.append(
+                    f"{seen[texts]} ({meaning_a}) and {key} ({meaning_b}) share {len(texts)} identical example(s)"
+                )
+            else:
+                seen[texts] = key
+
+    if errors:
+        _fail(name, errors[:20] if not _verbose else errors)
+        if len(errors) > 20 and not _verbose:
+            print(f"          ... ({len(errors) - 20} more; use --verbose)")
+    else:
+        _pass(name)
+
+
 def test_no_hebrew_in_meaning(data: dict[str, Any]) -> None:
    """English meanings must not contain bare Hebrew text (spoils the card)."""
    name = "no_hebrew_in_meaning"
@ -801,6 +852,7 @@ ALL_TESTS: dict[str, Any] = {
    "conjugation_form_guids": test_conjugation_form_guids,
    "conjugation_person_codes": test_conjugation_person_codes,
    "no_stripped_form_sentence_collisions": test_no_stripped_form_sentence_collisions,
+    "no_shared_confusable_examples": test_no_shared_confusable_examples,
    "no_hebrew_in_meaning": test_no_hebrew_in_meaning,
    "mishkal_consistency": test_mishkal_consistency,
 }
--- a/sentence_difficulty.py
+++ b/sentence_difficulty.py
@ -0,0 +1,198 @@
+"""Sentence difficulty scoring by context-word frequency.
+
+Scores sentences by the median frequency rank of context words
+(excluding the cloze target). Lower score = easier sentence.
+Used by epub_examples.py to select the best cloze sentence.
+"""
+
+from __future__ import annotations
+
+from statistics import median
+
+import helpers
+import nikkud_to_ktiv_male
+
+DEFAULT_RANK = 50_000
+
+# Hebrew prefix consonants for ktiv_male prefix stripping (tier 5)
+_KM_PREFIX_CHARS = set("בהוכלמשע")
+
+# Punctuation to strip from tokens
+_PUNCT = set('.,!?;:"\'"״׳–—()[]{}')
+
+# Maqaf (Hebrew hyphen) — splits tokens
+_MAQAF = "־"
+
+
+def build_nikkud_map(words: dict) -> dict[str, str]:
+    """Build nikkud→ktiv_male lookup from words.json.
+
+    Indexes: headwords, conjugation forms (active, passive, infinitive,
+    reference_form), noun inflections (singular, plural, construct,
+    pronominal suffixes), and adjective inflections (ms/fs/mp/fp).
+
+    Args:
+        words: The full words.json dict keyed by unique_key.
+
+    Returns:
+        Dict mapping nikkud form to ktiv_male string.
+        When collisions occur, last-write wins (acceptable for frequency lookup).
+    """
+    nmap: dict[str, str] = {}
+
+    def _add(nikkud: str | None, ktiv_male: str | None) -> None:
+        if nikkud and ktiv_male:
+            nmap[nikkud] = ktiv_male
+
+    for entry in words.values():
+        word = entry.get("word") or {}
+        _add(word.get("nikkud"), word.get("ktiv_male"))
+
+        # Conjugation forms
+        conj = entry.get("conjugation") or {}
+        for form_entry in conj.get("active_forms") or []:
+            form = form_entry.get("form") or {}
+            _add(form.get("nikkud"), form.get("ktiv_male"))
+        for form_entry in conj.get("hufal_pual_forms") or []:
+            form = form_entry.get("form") or {}
+            _add(form.get("nikkud"), form.get("ktiv_male"))
+        inf = conj.get("infinitive") or {}
+        _add(inf.get("nikkud"), inf.get("ktiv_male"))
+        ref = conj.get("reference_form") or {}
+        _add(ref.get("nikkud"), ref.get("ktiv_male"))
+
+        # Noun inflection forms
+        noun = entry.get("noun_inflection") or {}
+        for field in ("singular", "plural", "construct_singular", "construct_plural"):
+            sub = noun.get(field) or {}
+            nikkud_form = sub.get("nikkud")
+            ktiv = sub.get("ktiv_male")
+            _add(nikkud_form, ktiv)
+            # Index construct forms without maqaf
+            if nikkud_form and nikkud_form.endswith("־") and ktiv:
+                _add(nikkud_form[:-1], ktiv)
+        pronominal = noun.get("pronominal_suffixes") or {}
+        for sub in pronominal.values():
+            if isinstance(sub, dict):
+                _add(sub.get("nikkud"), sub.get("ktiv_male"))
+
+        # Adjective inflection forms
+        adj = entry.get("adjective_inflection") or {}
+        for field in ("ms", "fs", "mp", "fp"):
+            sub = adj.get(field) or {}
+            _add(sub.get("nikkud"), sub.get("ktiv_male"))
+
+    return nmap
+
+
+def _resolve_token_frequency(
+    token: str,
+    nikkud_map: dict[str, str],
+    nikkud_index: dict,
+    freq_data: dict[str, int],
+) -> int:
+    """Resolve a nikkud sentence token to its frequency rank.
+
+    Uses a 5-tier pipeline:
+    1. Known mapping (nikkud_map from words.json)
+    2. Nikkud prefix stripping (epub_examples.try_strip_prefix)
+    3. Academy rules converter (nikkud_to_ktiv_male.convert)
+    4. strip_nikkud fallback (helpers.strip_nikkud)
+    5. Ktiv_male prefix stripping on the converted form
+
+    Returns:
+        Frequency rank (1 = most common). DEFAULT_RANK (50000) if not found.
+    """
+    # Tier 1: Direct lookup in nikkud→ktiv_male map
+    ktiv = nikkud_map.get(token)
+    if ktiv and ktiv in freq_data:
+        return freq_data[ktiv]
+
+    # Tier 2: Nikkud prefix stripping → resolve remainder via nikkud_map
+    from epub_examples import try_strip_prefix
+
+    prefix_hits = try_strip_prefix(token, nikkud_index)
+    for _unique_key, _match_type, matched_remainder in prefix_hits:
+        remainder_ktiv = nikkud_map.get(matched_remainder)
+        if remainder_ktiv and remainder_ktiv in freq_data:
+            return freq_data[remainder_ktiv]
+
+    # Tier 3: Academy rules converter
+    converted = nikkud_to_ktiv_male.convert(token)
+    if converted in freq_data:
+        return freq_data[converted]
+
+    # Tier 4: strip_nikkud fallback
+    stripped = helpers.strip_nikkud(token)
+    if stripped != converted and stripped in freq_data:
+        return freq_data[stripped]
+
+    # Tier 5: Ktiv_male prefix stripping on converted/stripped form
+    for form in (converted, stripped):
+        for prefix_len in (1, 2):
+            if len(form) > prefix_len + 1:
+                prefix = form[:prefix_len]
+                if all(c in _KM_PREFIX_CHARS for c in prefix):
+                    stem = form[prefix_len:]
+                    if stem in freq_data:
+                        return freq_data[stem]
+
+    return DEFAULT_RANK
+
+
+def score_sentence(
+    text: str,
+    target_start: int,
+    target_end: int,
+    nikkud_map: dict[str, str],
+    nikkud_index: dict,
+    freq_data: dict[str, int],
+) -> int:
+    """Score a sentence by median frequency rank of context words.
+
+    Args:
+        text: The full sentence text (with nikkud).
+        target_start: Character offset where the cloze target word starts.
+        target_end: Character offset where the cloze target word ends.
+        nikkud_map: nikkud→ktiv_male mapping from build_nikkud_map().
+        nikkud_index: nikkud index from epub_examples._build_nikkud_index().
+        freq_data: Frequency dict from frequency_lookup.get_freq_data().
+
+    Returns:
+        Median frequency rank of context tokens (int). Lower = easier.
+        Returns DEFAULT_RANK if no scoreable context tokens.
+    """
+    # Tokenize: split on whitespace, then split on maqaf
+    raw_tokens = text.split()
+    tokens_with_pos: list[tuple[str, int, int]] = []
+    pos = 0
+    for raw in raw_tokens:
+        start = text.index(raw, pos)
+        # Split on maqaf
+        parts = raw.split(_MAQAF)
+        sub_pos = start
+        for part in parts:
+            if part:
+                tokens_with_pos.append((part, sub_pos, sub_pos + len(part)))
+            sub_pos += len(part) + 1  # +1 for maqaf
+        pos = start + len(raw)
+
+    # Filter: exclude target word, strip punctuation, skip short tokens
+    context_ranks: list[int] = []
+    for token, tok_start, tok_end in tokens_with_pos:
+        # Exclude target word by overlap with char offsets
+        if tok_start < target_end and tok_end > target_start:
+            continue
+
+        # Strip punctuation from edges
+        cleaned = token.strip("".join(_PUNCT))
+        if len(cleaned) < 2:
+            continue
+
+        rank = _resolve_token_frequency(cleaned, nikkud_map, nikkud_index, freq_data)
+        context_ranks.append(rank)
+
+    if not context_ranks:
+        return DEFAULT_RANK
+
+    return int(median(context_ranks))
--- a/tests/test_epub_examples.py
+++ b/tests/test_epub_examples.py
@ -0,0 +1,127 @@
+"""Tests for epub_examples deduplication of confusable group examples."""
+
+from epub_examples import _deduplicate_confusable_examples
+
+
+def _make_entry(meaning, confusable_group, vetted_texts=None, frequency_rank=None):
+    """Build a minimal words.json entry for testing."""
+    entry = {
+        "meaning": meaning,
+        "confusable_group": confusable_group,
+    }
+    if vetted_texts is not None:
+        entry["examples"] = {
+            "vetted": [{"text": t, "source": "test", "match_method": "direct"} for t in vetted_texts],
+        }
+    if frequency_rank is not None:
+        entry["frequency_rank"] = frequency_rank
+    return entry
+
+
+class TestDeduplicateConfusableExamples:
+    """Tests for _deduplicate_confusable_examples()."""
+
+    def test_shared_examples_kept_on_higher_frequency(self):
+        """When two confusables share identical examples, the one with
+        lower frequency_rank (more common) keeps them."""
+        group = ["key_a", "key_b"]
+        words = {
+            "key_a": _make_entry("brother", group, ["sent1", "sent2"], frequency_rank=500),
+            "key_b": _make_entry("fireplace", group, ["sent1", "sent2"], frequency_rank=8000),
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 1
+        assert len(words["key_a"]["examples"]["vetted"]) == 2
+        assert words["key_b"]["examples"]["vetted"] == []
+
+    def test_no_action_when_examples_differ(self):
+        """Groups with different example sets are left untouched."""
+        group = ["key_a", "key_b"]
+        words = {
+            "key_a": _make_entry("meaning1", group, ["sent1"], frequency_rank=100),
+            "key_b": _make_entry("meaning2", group, ["sent2"], frequency_rank=200),
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 0
+        assert len(words["key_a"]["examples"]["vetted"]) == 1
+        assert len(words["key_b"]["examples"]["vetted"]) == 1
+
+    def test_no_action_when_one_has_no_examples(self):
+        """If only one member has examples, nothing to deduplicate."""
+        group = ["key_a", "key_b"]
+        words = {
+            "key_a": _make_entry("meaning1", group, ["sent1"], frequency_rank=100),
+            "key_b": _make_entry("meaning2", group, frequency_rank=200),
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 0
+
+    def test_no_frequency_uses_alphabetical_tiebreak(self):
+        """When no member has frequency data, first alphabetically wins."""
+        group = ["alpha_key", "beta_key"]
+        words = {
+            "alpha_key": _make_entry("meaning1", group, ["sent1"]),
+            "beta_key": _make_entry("meaning2", group, ["sent1"]),
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 1
+        assert len(words["alpha_key"]["examples"]["vetted"]) == 1
+        assert words["beta_key"]["examples"]["vetted"] == []
+
+    def test_three_way_group(self):
+        """Three-member group: highest frequency wins, other two cleared."""
+        group = ["key_a", "key_b", "key_c"]
+        words = {
+            "key_a": _make_entry("yes", group, ["sent1", "sent2"], frequency_rank=50),
+            "key_b": _make_entry("honest", group, ["sent1", "sent2"], frequency_rank=3000),
+            "key_c": _make_entry("pedestal", group, ["sent1", "sent2"], frequency_rank=15000),
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 2
+        assert len(words["key_a"]["examples"]["vetted"]) == 2
+        assert words["key_b"]["examples"]["vetted"] == []
+        assert words["key_c"]["examples"]["vetted"] == []
+
+    def test_cloze_removed_from_losers(self):
+        """Losing entries should have their cloze data removed too."""
+        group = ["key_a", "key_b"]
+        words = {
+            "key_a": _make_entry("common", group, ["sent1"], frequency_rank=100),
+            "key_b": _make_entry("rare", group, ["sent1"], frequency_rank=9000),
+        }
+        # Add cloze to both
+        words["key_b"]["examples"]["cloze"] = {"text": "sent1", "cloze_guid": "abc"}
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 1
+        assert "cloze" not in words["key_b"]["examples"]
+
+    def test_no_confusable_groups_returns_zero(self):
+        """Words without confusable_group are ignored."""
+        words = {
+            "key_a": {"meaning": "word1", "examples": {"vetted": [{"text": "s1"}]}},
+            "key_b": {"meaning": "word2", "examples": {"vetted": [{"text": "s1"}]}},
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 0
+
+    def test_mixed_frequency_and_none(self):
+        """Member with frequency beats member without."""
+        group = ["key_a", "key_b"]
+        words = {
+            "key_a": _make_entry("has_freq", group, ["sent1"], frequency_rank=5000),
+            "key_b": _make_entry("no_freq", group, ["sent1"]),
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 1
+        assert len(words["key_a"]["examples"]["vetted"]) == 1
+        assert words["key_b"]["examples"]["vetted"] == []
+
+    def test_partial_overlap_not_deduplicated(self):
+        """Groups with overlapping but not identical sentence sets are not touched."""
+        group = ["key_a", "key_b"]
+        words = {
+            "key_a": _make_entry("m1", group, ["sent1", "sent2"], frequency_rank=100),
+            "key_b": _make_entry("m2", group, ["sent1", "sent3"], frequency_rank=200),
+        }
+        cleared = _deduplicate_confusable_examples(words)
+        assert cleared == 0
--- a/tests/test_scoring_integration.py
+++ b/tests/test_scoring_integration.py
@ -0,0 +1,83 @@
+"""Integration tests for frequency-based sentence scoring in update_words_json."""
+
+
+def _make_sentence(text, source="test", match_method="direct", word_count=None, char_offset=0, char_end=3):
+    """Build a minimal sentence dict as match_sentences would produce."""
+    if word_count is None:
+        word_count = len(text.split())
+    return {
+        "text": text,
+        "source": source,
+        "match_method": match_method,
+        "word_count": word_count,
+        "char_offset": char_offset,
+        "char_end": char_end,
+    }
+
+
+class TestScoringIntegration:
+    """Tests that update_words_json uses frequency scoring."""
+
+    def test_cloze_has_difficulty_score(self):
+        """Cloze dict includes difficulty_score field."""
+        from epub_examples import update_words_json
+
+        words = {
+            "טוֹב": {
+                "word": {"nikkud": "טוֹב", "ktiv_male": "טוב"},
+                "examples": {},
+            }
+        }
+        matches = {
+            "טוֹב": [
+                _make_sentence("הוּא אָדָם טוֹב מְאוֹד", char_offset=10, char_end=13),
+            ]
+        }
+        update_words_json(words, matches, confusable_keys=set())
+        cloze = words["טוֹב"]["examples"].get("cloze")
+        assert cloze is not None
+        assert "difficulty_score" in cloze
+        assert isinstance(cloze["difficulty_score"], int)
+
+    def test_vetted_sorted_by_difficulty(self):
+        """Vetted sentences are sorted easiest first."""
+        from epub_examples import update_words_json
+
+        words = {
+            "טוֹב": {
+                "word": {"nikkud": "טוֹב", "ktiv_male": "טוב"},
+                "examples": {},
+            }
+        }
+        matches = {
+            "טוֹב": [
+                _make_sentence("הוּא טוֹב", char_offset=4, char_end=7),
+                _make_sentence("הַתַּפְנִיט טוֹב בְּיוֹתֵר", char_offset=10, char_end=13),
+                _make_sentence("אֲנִי טוֹב הַיּוֹם", char_offset=5, char_end=8),
+            ]
+        }
+        update_words_json(words, matches, confusable_keys=set())
+        vetted = words["טוֹב"]["examples"]["vetted"]
+        assert len(vetted) == 3
+
+    def test_easiest_sentence_becomes_cloze(self):
+        """The sentence with the lowest difficulty score becomes the cloze."""
+        from epub_examples import update_words_json
+
+        words = {
+            "טוֹב": {
+                "word": {"nikkud": "טוֹב", "ktiv_male": "טוב"},
+                "examples": {},
+            }
+        }
+        easy_text = "הוּא טוֹב מְאוֹד"
+        hard_text = "הַפַּרְנָסִימוֹן טוֹב לְהַפְלִיא"
+        matches = {
+            "טוֹב": [
+                _make_sentence(hard_text, char_offset=14, char_end=17),
+                _make_sentence(easy_text, char_offset=4, char_end=7),
+            ]
+        }
+        update_words_json(words, matches, confusable_keys=set())
+        cloze = words["טוֹב"]["examples"]["cloze"]
+        assert cloze["text"] == easy_text
--- a/tests/test_sentence_difficulty.py
+++ b/tests/test_sentence_difficulty.py
@ -0,0 +1,207 @@
+"""Tests for sentence difficulty scoring."""
+
+import json
+from pathlib import Path
+
+import pytest
+
+import frequency_lookup
+from sentence_difficulty import DEFAULT_RANK, _resolve_token_frequency, build_nikkud_map, score_sentence
+
+
+class TestBuildNikkudMap:
+    def test_maps_direct_headwords(self):
+        words = {"אָב": {"word": {"nikkud": "אָב", "ktiv_male": "אב"}}}
+        nmap = build_nikkud_map(words)
+        assert nmap["אָב"] == "אב"
+
+    def test_maps_conjugation_forms(self):
+        words = {
+            "שָׁמַר": {
+                "word": {"nikkud": "שָׁמַר", "ktiv_male": "שמר"},
+                "conjugation": {
+                    "active_forms": [
+                        {
+                            "person": "1s",
+                            "tense": "עָבָר",
+                            "form": {"nikkud": "שָׁמַרְתִּי", "ktiv_male": "שמרתי"},
+                        },
+                    ],
+                    "infinitive": {"nikkud": "לִשְׁמֹר", "ktiv_male": "לשמור"},
+                    "reference_form": {"nikkud": "שָׁמַר", "ktiv_male": "שמר"},
+                },
+            }
+        }
+        nmap = build_nikkud_map(words)
+        assert nmap["שָׁמַרְתִּי"] == "שמרתי"
+        assert nmap["לִשְׁמֹר"] == "לשמור"
+
+    def test_maps_noun_inflections(self):
+        words = {
+            "אָב": {
+                "word": {"nikkud": "אָב", "ktiv_male": "אב"},
+                "noun_inflection": {
+                    "singular": {"nikkud": "אָב", "ktiv_male": "אב"},
+                    "plural": {"nikkud": "אָבוֹת", "ktiv_male": "אבות"},
+                    "pronominal_suffixes": {"1s": {"nikkud": "אָבִי", "ktiv_male": "אבי"}},
+                },
+            }
+        }
+        nmap = build_nikkud_map(words)
+        assert nmap["אָבוֹת"] == "אבות"
+        assert nmap["אָבִי"] == "אבי"
+
+    def test_maps_adjective_inflections(self):
+        words = {
+            "גָּדוֹל": {
+                "word": {"nikkud": "גָּדוֹל", "ktiv_male": "גדול"},
+                "adjective_inflection": {
+                    "ms": {"nikkud": "גָּדוֹל", "ktiv_male": "גדול"},
+                    "fs": {"nikkud": "גְּדוֹלָה", "ktiv_male": "גדולה"},
+                    "mp": {"nikkud": "גְּדוֹלִים", "ktiv_male": "גדולים"},
+                    "fp": {"nikkud": "גְּדוֹלוֹת", "ktiv_male": "גדולות"},
+                },
+            }
+        }
+        nmap = build_nikkud_map(words)
+        assert nmap["גְּדוֹלָה"] == "גדולה"
+        assert nmap["גְּדוֹלִים"] == "גדולים"
+
+    def test_construct_forms_strip_maqaf(self):
+        words = {
+            "בֵּית": {
+                "word": {"nikkud": "בֵּית", "ktiv_male": "בית"},
+                "noun_inflection": {
+                    "construct_singular": {"nikkud": "בֵּית־", "ktiv_male": "בית"},
+                },
+            }
+        }
+        nmap = build_nikkud_map(words)
+        assert "בֵּית־" in nmap
+        assert "בֵּית" in nmap
+
+    def test_handles_missing_fields(self):
+        words = {
+            "test": {
+                "word": {"nikkud": "טֶסְט", "ktiv_male": "טסט"},
+                "conjugation": None,
+                "noun_inflection": None,
+                "adjective_inflection": None,
+            }
+        }
+        nmap = build_nikkud_map(words)
+        assert nmap["טֶסְט"] == "טסט"
+
+    def test_real_words_json_coverage(self):
+        words_path = Path(__file__).parent.parent / "data" / "words.json"
+        if not words_path.exists():
+            pytest.skip("words.json not available")
+        with open(words_path, encoding="utf-8") as f:
+            words = json.load(f)
+        nmap = build_nikkud_map(words)
+        assert len(nmap) > 90_000
+
+
+class TestResolveTokenFrequency:
+    @pytest.fixture()
+    def freq_setup(self):
+        frequency_lookup.load()
+        freq_data = frequency_lookup.get_freq_data()
+        words_path = Path(__file__).parent.parent / "data" / "words.json"
+        if not words_path.exists():
+            pytest.skip("words.json not available")
+        with open(words_path, encoding="utf-8") as f:
+            words = json.load(f)
+        from epub_examples import _build_nikkud_index
+
+        nikkud_map = build_nikkud_map(words)
+        nikkud_index = _build_nikkud_index(words)
+        return nikkud_map, nikkud_index, freq_data
+
+    def test_tier1_known_mapping(self, freq_setup):
+        nikkud_map, nikkud_index, freq_data = freq_setup
+        rank = _resolve_token_frequency("אָב", nikkud_map, nikkud_index, freq_data)
+        assert rank is not None
+        assert rank < 50_000
+
+    def test_tier3_academy_converter(self, freq_setup):
+        nikkud_map, nikkud_index, freq_data = freq_setup
+        rank = _resolve_token_frequency("שָׁלוֹם", nikkud_map, nikkud_index, freq_data)
+        assert rank is not None
+        assert rank < 1000
+
+    def test_unknown_token_returns_default(self, freq_setup):
+        nikkud_map, nikkud_index, freq_data = freq_setup
+        rank = _resolve_token_frequency("קְסַנְתּוֹפּוּלוֹס", nikkud_map, nikkud_index, freq_data)
+        assert rank == 50_000
+
+    def test_tier5_ktiv_male_prefix_strip(self, freq_setup):
+        nikkud_map, nikkud_index, freq_data = freq_setup
+        assert freq_data.get("שלום") is not None
+
+
+class TestScoreSentence:
+    @pytest.fixture()
+    def scoring_setup(self):
+        frequency_lookup.load()
+        freq_data = frequency_lookup.get_freq_data()
+        words_path = Path(__file__).parent.parent / "data" / "words.json"
+        if not words_path.exists():
+            pytest.skip("words.json not available")
+        with open(words_path, encoding="utf-8") as f:
+            words = json.load(f)
+        from epub_examples import _build_nikkud_index
+
+        nikkud_map = build_nikkud_map(words)
+        nikkud_index = _build_nikkud_index(words)
+        return nikkud_map, nikkud_index, freq_data
+
+    def test_returns_integer(self, scoring_setup):
+        nmap, nidx, freq = scoring_setup
+        text = "הוּא הָלַךְ הַבַּיְתָה"
+        start = text.index("הָלַךְ")
+        end = start + len("הָלַךְ")
+        score = score_sentence(text, start, end, nmap, nidx, freq)
+        assert isinstance(score, int)
+
+    def test_easy_sentence_scores_lower(self, scoring_setup):
+        nmap, nidx, freq = scoring_setup
+        easy = "הוּא אָמַר שָׁלוֹם"
+        easy_start = easy.index("אָמַר")
+        easy_end = easy_start + len("אָמַר")
+        hard = "הַפַּרְדֵּס נִשְׁתַּטֵּחַ בַּדַּהֲרָה"
+        hard_start = hard.index("נִשְׁתַּטֵּחַ")
+        hard_end = hard_start + len("נִשְׁתַּטֵּחַ")
+        easy_score = score_sentence(easy, easy_start, easy_end, nmap, nidx, freq)
+        hard_score = score_sentence(hard, hard_start, hard_end, nmap, nidx, freq)
+        assert easy_score < hard_score
+
+    def test_single_context_token(self, scoring_setup):
+        nmap, nidx, freq = scoring_setup
+        text = "הוּא טוֹב"
+        start = 0
+        end = len("הוּא")
+        score = score_sentence(text, start, end, nmap, nidx, freq)
+        assert isinstance(score, int)
+
+    def test_handles_punctuation(self, scoring_setup):
+        nmap, nidx, freq = scoring_setup
+        text = '"הוּא טוֹב!"'
+        start = text.index("טוֹב")
+        end = start + len("טוֹב")
+        score = score_sentence(text, start, end, nmap, nidx, freq)
+        assert isinstance(score, int)
+
+    def test_splits_on_maqaf(self, scoring_setup):
+        nmap, nidx, freq = scoring_setup
+        text = "בֵּית־סֵפֶר גָּדוֹל"
+        start = text.index("גָּדוֹל")
+        end = start + len("גָּדוֹל")
+        score = score_sentence(text, start, end, nmap, nidx, freq)
+        assert isinstance(score, int)
+
+    def test_no_context_tokens_returns_default(self, scoring_setup):
+        nmap, nidx, freq = scoring_setup
+        text = "א ב"
+        score = score_sentence(text, 0, 1, nmap, nidx, freq)
+        assert score == DEFAULT_RANK
Author	SHA1	Message	Date
Sochen	6d2d446ed5	feat: pseudo-frequency for confusables using English word frequency 264 confusable groups where all entries shared the same Hebrew frequency now have differentiated pseudo_frequency values based on English word commonality (hermitdave en_50k.txt). Most common meaning keeps base rank; less common meanings get +100 offset per position. Examples: - אב: "father" (en:194) → 2491, "bud" (en:2963) → 2591 - אח: "brother" (en:300) → 911, "fireplace" (en:9389) → 1011 Builder uses pseudo_frequency for sort order when available. Confusable card definitions now sorted most-common-first. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-03 05:28:30 +00:00
Sochen	f978e5f39a	fix: vet fallback emoji — verb gate + expanded stop list removes 852 bad matches The fallback emoji system (keyword→Unicode char matching at build time) was producing 1,733 matches, many with wrong-sense emoji: - "high, tall" → ⚡ (from "high voltage") - "to cut" → 🥩 (cut of meat) - "city" → 🇻🇦 (Vatican flag) Two fixes: 1. Skip fallback for verbs (meanings starting "to ") — 476 removed 2. Expand _EMOJI_STOP with 100+ polysemous/abstract keywords — 376 more Result: 1733 → 881 fallback matches (49% reduction). The 114 from_pealim emojis (concrete nouns like 🍎 apple, 🐪 camel) are unaffected. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-03 05:17:31 +00:00
Sochen	5f617af4ba	feat: vet emoji assignments — 114 visible, 3 removed, 1 fixed Reviewed all 117 pealim-inherited emoji assignments: - Made 114 correct assignments visible (emoji_visible: true) - Removed: goblet (🏆 is trophy), fitness (🏋 too abstract), red (💄 is lipstick) - Fixed: onion 🌰→🧅 (was chestnut emoji) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-26 23:19:43 +00:00
Sochen	f3496998f5	feat: confusables show ktiv male, emoji/prep stripping fully upstream - Confusables deck front now shows shared ktiv male form instead of nikkud variants joined by "/". Back still shows nikkud with definitions. - Fixed list scraper EMOJI_RE to catch variation selectors (U+FE0F) and ZWJ (U+200D) — cleaned 17 entries with leftover selectors in meaning. - Removed build-time prep extraction fallback (0 entries relied on it). - release.py: fix keeshare field name (API_TOKEN → password). Closes: Pealim #11 (emoji/prep upstream), Pealim #16 (confusables ktiv male) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-21 02:19:03 +00:00
Sochen	138acb06d8	bump RELEASE_TAG to v0.20 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-15 14:09:19 +00:00
Sochen	0a85291975	feat(v0.20): adaptive sentence difficulty scoring — 3163 scored cloze sentences Replaces length-based sentence selection with frequency-based difficulty scoring. Easiest sentences (most common context words) become cloze candidates. Score range: 1-50000, median=179. Coverage: 3163/3325 cloze entries scored. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-15 13:35:23 +00:00
Sochen	14d567a261	schema: add difficulty_score field + update spec with MIN_WORDS=3 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-15 13:30:13 +00:00
Sochen	8b24d0fd26	Task 7: Add integration tests for frequency-based sentence scoring Tests verify that update_words_json produces a cloze with `difficulty_score`, that vetted sentences are sorted by difficulty, and that the easiest sentence becomes the cloze candidate. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-15 13:29:25 +00:00
Sochen	272a2a080d	Task 7: Replace length-based scoring with frequency-based scoring in update_words_json - Import `sentence_difficulty.build_nikkud_map`, `score_sentence`, and `frequency_lookup` - Build `nikkud_index`, `nikkud_map`, `freq_data` once before the per-word loop - Replace `_score()` closure with call to `score_sentence()` (median context-word rank) - Store `difficulty_score` in cloze dict for downstream use Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-15 13:29:22 +00:00
Sochen	fb12f806a8	feat: add sentence_difficulty module with 5-tier frequency scoring Implements build_nikkud_map(), _resolve_token_frequency(), and score_sentence() for v0.20 adaptive cloze sentence selection. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-15 13:23:21 +00:00
Sochen	00fba934fb	feat(epub_examples): export try_strip_prefix as public alias Exposes _try_strip_prefix under a public name so the upcoming sentence_difficulty module can reuse Hebrew prefix stripping logic without duplicating it. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-15 13:18:55 +00:00
Sochen	d2a7c9d483	feat(epub_examples): lower MIN_WORDS from 4 to 3 Hebrew is more concise than English — 3-word sentences are valid candidates for the example sentence pool. Expands the pool for the upcoming adaptive sentence difficulty ranking. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-15 13:18:44 +00:00
Sochen	d0f4aea58d	feat(frequency_lookup): add get_freq_data() for batch frequency access Exposes the full word→rank dict for use by the upcoming sentence_difficulty module without requiring per-word lookups. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-15 13:18:11 +00:00
Sochen	b3ea086e85	v0.20 design spec + nikkud-to-ktiv-male converter Add Academy-rules-based nikkud→ktiv male converter (91.6% accuracy vs 77.2% for strip_nikkud) and v0.20 adaptive sentence difficulty cloze design spec. The converter enables frequency-based sentence scoring by properly resolving nikkud tokens to their ktiv male forms for frequency corpus lookup. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-15 12:57:14 +00:00
Sochen	af186e2030	Sprint 17: homograph example dedup + plural audio + prep extraction - Homograph collision fix: _deduplicate_confusable_examples() clears shared examples from less-common confusable group members (36 entries fixed). Keeps examples only on highest-frequency meaning. - Plural deck audio: wired up PluralAudio field in apkg_builder.py, downloaded 613 plural audio files from pealim.com for all deck entries. - Prep extraction upstream: moved Hebrew preposition parsing from build time into list/detail scrapers (SCHEMA.yaml prep field added). - Validation: new no_shared_confusable_examples check in validate_data.py - Tests: 9 new unit tests for confusable deduplication (98 total) - Release: v0.19 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-14 21:51:35 +00:00