{
  "name": "Methora · Task.Store catalog",
  "version": 1,
  "counts": {
    "atoms": 27,
    "providers": 63,
    "workflows": 7,
    "tools": 5
  },
  "valueSources": [
    "machine",
    "expert",
    "human",
    "access",
    "backing"
  ],
  "atoms": [
    {
      "id": "act_research",
      "slug": "ResearchCompany",
      "name": "Research a company",
      "description": "Gather a factual profile of a company from its name or description.",
      "inputs": [
        "company"
      ],
      "output": "company profile",
      "tools": [],
      "providers": [
        {
          "id": "p_research_ai",
          "name": "General AI research",
          "kind": "machine",
          "priceCredits": 200,
          "qualityPct": 79,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_research_ai/run"
        },
        {
          "id": "p_research_data",
          "name": "Market-data provider",
          "kind": "access",
          "priceCredits": 600,
          "qualityPct": 91,
          "latencySec": 6,
          "guaranteed": false,
          "dataPolicy": "no-retention",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_research_data/run"
        }
      ]
    },
    {
      "id": "act_pitch",
      "slug": "AnalyzePitchDeck",
      "name": "Analyze a pitch deck",
      "description": "Assess a startup's pitch. Strengths, risks, and open questions.",
      "inputs": [
        "deck or description"
      ],
      "output": "assessment",
      "tools": [
        "tool_unit"
      ],
      "providers": [
        {
          "id": "p_pitch_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 80,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_pitch_ai/run"
        },
        {
          "id": "p_pitch_vc",
          "name": "VC-specialist model",
          "kind": "machine",
          "priceCredits": 900,
          "qualityPct": 90,
          "latencySec": 12,
          "guaranteed": false,
          "dataPolicy": "no-retention",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_pitch_vc/run"
        },
        {
          "id": "p_pitch_expert",
          "name": "Jordan Vance",
          "kind": "expert",
          "priceCredits": 2900,
          "qualityPct": 93,
          "latencySec": 26,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_vc",
          "run": "https://studio.methora.io/api/providers/p_pitch_expert/run"
        }
      ]
    },
    {
      "id": "act_comps",
      "slug": "FindComparables",
      "name": "Find comparable companies",
      "description": "Surface comparable companies and rough benchmarks.",
      "inputs": [
        "company profile"
      ],
      "output": "comparables",
      "tools": [
        "tool_valuation"
      ],
      "providers": [
        {
          "id": "p_comps_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 200,
          "qualityPct": 77,
          "latencySec": 7,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_comps_ai/run"
        },
        {
          "id": "p_comps_data",
          "name": "Financial-data provider",
          "kind": "access",
          "priceCredits": 700,
          "qualityPct": 92,
          "latencySec": 6,
          "guaranteed": false,
          "dataPolicy": "no-retention",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_comps_data/run"
        },
        {
          "id": "p_comps_expert",
          "name": "Jordan Vance",
          "kind": "expert",
          "priceCredits": 2200,
          "qualityPct": 91,
          "latencySec": 24,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_vc",
          "run": "https://studio.methora.io/api/providers/p_comps_expert/run"
        }
      ]
    },
    {
      "id": "act_memo",
      "slug": "GenerateInvestmentMemo",
      "name": "Generate an investment memo",
      "description": "Write a structured investment memo from research and comparables.",
      "inputs": [
        "research",
        "assessment",
        "comparables"
      ],
      "output": "investment memo",
      "tools": [
        "tool_valuation",
        "tool_unit"
      ],
      "providers": [
        {
          "id": "p_memo_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 200,
          "qualityPct": 78,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_memo_ai/run"
        },
        {
          "id": "p_memo_vc",
          "name": "VC-specialist model",
          "kind": "machine",
          "priceCredits": 900,
          "qualityPct": 90,
          "latencySec": 12,
          "guaranteed": false,
          "dataPolicy": "no-retention",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_memo_vc/run"
        },
        {
          "id": "p_memo_expert",
          "name": "Jordan Vance (investor)",
          "kind": "expert",
          "priceCredits": 4900,
          "qualityPct": 95,
          "latencySec": 30,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_vc",
          "run": "https://studio.methora.io/api/providers/p_memo_expert/run"
        },
        {
          "id": "p_memo_human",
          "name": "Human-reviewed memo",
          "kind": "human",
          "priceCredits": 7900,
          "qualityPct": 97,
          "latencySec": 3600,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_memo_human/run"
        }
      ]
    },
    {
      "id": "act_review",
      "slug": "HumanAnalystReview",
      "name": "Human analyst review",
      "description": "A human analyst reviews and signs off on the memo.",
      "inputs": [
        "investment memo"
      ],
      "output": "reviewed memo + sign-off",
      "tools": [],
      "providers": [
        {
          "id": "p_review_human",
          "name": "Analyst review + sign-off",
          "kind": "human",
          "priceCredits": 2500,
          "qualityPct": 96,
          "latencySec": 3600,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_review_human/run"
        },
        {
          "id": "p_review_ai",
          "name": "AI second-opinion",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 82,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_review_ai/run"
        }
      ]
    },
    {
      "id": "act_offer",
      "slug": "ReworkOffer",
      "name": "Rework the offer",
      "description": "Rebuild an offer with the value equation and a guarantee.",
      "inputs": [
        "offer + price"
      ],
      "output": "rebuilt offer",
      "tools": [],
      "providers": [
        {
          "id": "p_offer_expert",
          "name": "Marcus Cole",
          "kind": "expert",
          "priceCredits": 2900,
          "qualityPct": 94,
          "latencySec": 25,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_offers",
          "run": "https://studio.methora.io/api/providers/p_offer_expert/run"
        },
        {
          "id": "p_offer_clone",
          "name": "Alex Hormozi (AI persona)",
          "kind": "expert",
          "priceCredits": 1900,
          "qualityPct": 90,
          "latencySec": 20,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_hormozi",
          "run": "https://studio.methora.io/api/providers/p_offer_clone/run"
        },
        {
          "id": "p_offer_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 400,
          "qualityPct": 79,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_offer_ai/run"
        }
      ]
    },
    {
      "id": "act_copy",
      "slug": "WriteSalesCopy",
      "name": "Write the sales copy",
      "description": "Turn the offer into a direct-response landing page.",
      "inputs": [
        "rebuilt offer"
      ],
      "output": "sales page",
      "tools": [],
      "providers": [
        {
          "id": "p_copy_expert",
          "name": "Nina Brandt",
          "kind": "expert",
          "priceCredits": 3900,
          "qualityPct": 93,
          "latencySec": 25,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_copy",
          "run": "https://studio.methora.io/api/providers/p_copy_expert/run"
        },
        {
          "id": "p_copy_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 500,
          "qualityPct": 80,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_copy_ai/run"
        }
      ]
    },
    {
      "id": "act_leads",
      "slug": "LeadPlan",
      "name": "Lead-generation plan",
      "description": "A concrete plan to get the first customers.",
      "inputs": [
        "offer + audience"
      ],
      "output": "lead plan",
      "tools": [],
      "providers": [
        {
          "id": "p_leads_expert",
          "name": "Marcus Cole",
          "kind": "expert",
          "priceCredits": 2900,
          "qualityPct": 92,
          "latencySec": 22,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_offers",
          "run": "https://studio.methora.io/api/providers/p_leads_expert/run"
        },
        {
          "id": "p_leads_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 400,
          "qualityPct": 78,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_leads_ai/run"
        }
      ]
    },
    {
      "id": "act_guarantee",
      "slug": "GuaranteeResult",
      "name": "Guarantee the result",
      "description": "Attach a stated performance guarantee to the deliverable.",
      "inputs": [
        "deliverable"
      ],
      "output": "guarantee terms",
      "tools": [],
      "providers": [
        {
          "id": "p_guar_backing",
          "name": "Methora guarantee",
          "kind": "backing",
          "priceCredits": 2000,
          "qualityPct": 100,
          "latencySec": 2,
          "guaranteed": true,
          "dataPolicy": "private",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_guar_backing/run"
        }
      ]
    },
    {
      "id": "act_keywords",
      "slug": "ResearchKeywords",
      "name": "Research keywords",
      "description": "Find the intent-first keywords and topic clusters worth targeting.",
      "inputs": [
        "topic + audience"
      ],
      "output": "keyword plan",
      "tools": [
        "tool_kw"
      ],
      "providers": [
        {
          "id": "p_kw_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 78,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_kw_ai/run"
        },
        {
          "id": "p_kw_data",
          "name": "Search-data provider",
          "kind": "access",
          "priceCredits": 800,
          "qualityPct": 90,
          "latencySec": 6,
          "guaranteed": false,
          "dataPolicy": "no-retention",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_kw_data/run"
        },
        {
          "id": "p_kw_expert",
          "name": "Sofia Marchetti",
          "kind": "expert",
          "priceCredits": 1900,
          "qualityPct": 93,
          "latencySec": 22,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_seo",
          "run": "https://studio.methora.io/api/providers/p_kw_expert/run"
        }
      ]
    },
    {
      "id": "act_brief",
      "slug": "ContentBrief",
      "name": "Write a content brief",
      "description": "Turn a target keyword into a writer-ready brief that beats the SERP.",
      "inputs": [
        "keyword plan"
      ],
      "output": "content brief",
      "tools": [],
      "providers": [
        {
          "id": "p_brief_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 79,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_brief_ai/run"
        },
        {
          "id": "p_brief_expert",
          "name": "Sofia Marchetti",
          "kind": "expert",
          "priceCredits": 900,
          "qualityPct": 93,
          "latencySec": 18,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_seo",
          "run": "https://studio.methora.io/api/providers/p_brief_expert/run"
        }
      ]
    },
    {
      "id": "act_draft",
      "slug": "DraftArticle",
      "name": "Draft the article",
      "description": "Write the full article to the brief.",
      "inputs": [
        "content brief"
      ],
      "output": "article draft",
      "tools": [],
      "providers": [
        {
          "id": "p_draft_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 500,
          "qualityPct": 80,
          "latencySec": 12,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_draft_ai/run"
        },
        {
          "id": "p_draft_expert",
          "name": "Nina Brandt",
          "kind": "expert",
          "priceCredits": 3900,
          "qualityPct": 93,
          "latencySec": 28,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_copy",
          "run": "https://studio.methora.io/api/providers/p_draft_expert/run"
        }
      ]
    },
    {
      "id": "act_edit",
      "slug": "EditArticle",
      "name": "Edit & polish",
      "description": "Tighten the draft. Clarity, voice, and flow.",
      "inputs": [
        "article draft"
      ],
      "output": "polished article",
      "tools": [],
      "providers": [
        {
          "id": "p_edit_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 400,
          "qualityPct": 81,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_edit_ai/run"
        },
        {
          "id": "p_edit_expert",
          "name": "Nina Brandt",
          "kind": "expert",
          "priceCredits": 2900,
          "qualityPct": 92,
          "latencySec": 22,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_copy",
          "run": "https://studio.methora.io/api/providers/p_edit_expert/run"
        },
        {
          "id": "p_edit_human",
          "name": "Human editor",
          "kind": "human",
          "priceCredits": 3900,
          "qualityPct": 96,
          "latencySec": 3600,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_edit_human/run"
        }
      ]
    },
    {
      "id": "act_seopack",
      "slug": "SeoPackage",
      "name": "On-page SEO package",
      "description": "Title, meta, slug, headings, schema, and internal links.",
      "inputs": [
        "polished article"
      ],
      "output": "SEO package",
      "tools": [],
      "providers": [
        {
          "id": "p_seopack_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 80,
          "latencySec": 7,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_seopack_ai/run"
        },
        {
          "id": "p_seopack_expert",
          "name": "Sofia Marchetti",
          "kind": "expert",
          "priceCredits": 900,
          "qualityPct": 93,
          "latencySec": 16,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_seo",
          "run": "https://studio.methora.io/api/providers/p_seopack_expert/run"
        }
      ]
    },
    {
      "id": "act_hs",
      "slug": "ClassifyHS",
      "name": "Classify the goods (HS code)",
      "description": "Assign the correct HS tariff heading via the General Rules of Interpretation.",
      "inputs": [
        "product description"
      ],
      "output": "HS classification",
      "tools": [],
      "providers": [
        {
          "id": "p_hs_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 74,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_hs_ai/run"
        },
        {
          "id": "p_hs_expert",
          "name": "Mei-Ling Chen",
          "kind": "expert",
          "priceCredits": 2500,
          "qualityPct": 93,
          "latencySec": 26,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_customs",
          "run": "https://studio.methora.io/api/providers/p_hs_expert/run"
        }
      ]
    },
    {
      "id": "act_incoterm",
      "slug": "PickIncoterm",
      "name": "Choose the Incoterm",
      "description": "Pick the correct Incoterms 2020 term for the route and mode.",
      "inputs": [
        "shipment + route"
      ],
      "output": "recommended Incoterm",
      "tools": [],
      "providers": [
        {
          "id": "p_inco_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 200,
          "qualityPct": 78,
          "latencySec": 7,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_inco_ai/run"
        },
        {
          "id": "p_inco_expert",
          "name": "Mei-Ling Chen",
          "kind": "expert",
          "priceCredits": 1500,
          "qualityPct": 94,
          "latencySec": 20,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_customs",
          "run": "https://studio.methora.io/api/providers/p_inco_expert/run"
        }
      ]
    },
    {
      "id": "act_landed",
      "slug": "LandedCost",
      "name": "Estimate landed cost",
      "description": "Duty, import VAT, freight and the all-in landed cost.",
      "inputs": [
        "value + duty rate"
      ],
      "output": "landed-cost estimate",
      "tools": [
        "tool_duty"
      ],
      "providers": [
        {
          "id": "p_landed_ai",
          "name": "General AI + calculator",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 85,
          "latencySec": 6,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_landed_ai/run"
        },
        {
          "id": "p_landed_data",
          "name": "Tariff-data provider",
          "kind": "access",
          "priceCredits": 700,
          "qualityPct": 93,
          "latencySec": 5,
          "guaranteed": false,
          "dataPolicy": "no-retention",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_landed_data/run"
        }
      ]
    },
    {
      "id": "act_doccheck",
      "slug": "ComplianceDocs",
      "name": "Compliance document checklist",
      "description": "The documents the import needs to clear customs cleanly.",
      "inputs": [
        "goods + route"
      ],
      "output": "document checklist",
      "tools": [],
      "providers": [
        {
          "id": "p_doc_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 200,
          "qualityPct": 80,
          "latencySec": 6,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_doc_ai/run"
        },
        {
          "id": "p_doc_expert",
          "name": "Mei-Ling Chen",
          "kind": "expert",
          "priceCredits": 1200,
          "qualityPct": 93,
          "latencySec": 18,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_customs",
          "run": "https://studio.methora.io/api/providers/p_doc_expert/run"
        }
      ]
    },
    {
      "id": "act_legalrev",
      "slug": "ReviewContracts",
      "name": "Review the contracts",
      "description": "A risk-focused read of the key agreements. Caps, carve-outs, change-of-control.",
      "inputs": [
        "contracts"
      ],
      "output": "legal risk report",
      "tools": [],
      "providers": [
        {
          "id": "p_legal_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 500,
          "qualityPct": 78,
          "latencySec": 10,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_legal_ai/run"
        },
        {
          "id": "p_legal_expert",
          "name": "David Sorensen, Esq.",
          "kind": "expert",
          "priceCredits": 2900,
          "qualityPct": 93,
          "latencySec": 28,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_contract",
          "run": "https://studio.methora.io/api/providers/p_legal_expert/run"
        },
        {
          "id": "p_legal_human",
          "name": "Human lawyer review",
          "kind": "human",
          "priceCredits": 9900,
          "qualityPct": 98,
          "latencySec": 3600,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_legal_human/run"
        }
      ]
    },
    {
      "id": "act_tradecheck",
      "slug": "TradeCheck",
      "name": "Trade & compliance check",
      "description": "Import/export exposure, classification, and origin risk in the supply chain.",
      "inputs": [
        "company + supply chain"
      ],
      "output": "trade-compliance note",
      "tools": [],
      "providers": [
        {
          "id": "p_trade_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 400,
          "qualityPct": 76,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_trade_ai/run"
        },
        {
          "id": "p_trade_expert",
          "name": "Mei-Ling Chen",
          "kind": "expert",
          "priceCredits": 2200,
          "qualityPct": 92,
          "latencySec": 24,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_customs",
          "run": "https://studio.methora.io/api/providers/p_trade_expert/run"
        }
      ]
    },
    {
      "id": "act_diagnose",
      "slug": "DiagnoseQuery",
      "name": "Diagnose the slow query",
      "description": "Read the plan, find the bottleneck, and name the fix.",
      "inputs": [
        "query + EXPLAIN + schema"
      ],
      "output": "diagnosis",
      "tools": [],
      "providers": [
        {
          "id": "p_diag_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 500,
          "qualityPct": 79,
          "latencySec": 10,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_diag_ai/run"
        },
        {
          "id": "p_diag_expert",
          "name": "Viktor Aalto",
          "kind": "expert",
          "priceCredits": 2900,
          "qualityPct": 95,
          "latencySec": 26,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_pg",
          "run": "https://studio.methora.io/api/providers/p_diag_expert/run"
        }
      ]
    },
    {
      "id": "act_indexrev",
      "slug": "IndexReview",
      "name": "Index review",
      "description": "Which indexes to add, drop, or reorder for the query pattern.",
      "inputs": [
        "schema + queries"
      ],
      "output": "index plan",
      "tools": [],
      "providers": [
        {
          "id": "p_idx_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 400,
          "qualityPct": 80,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_idx_ai/run"
        },
        {
          "id": "p_idx_expert",
          "name": "Viktor Aalto",
          "kind": "expert",
          "priceCredits": 1900,
          "qualityPct": 94,
          "latencySec": 22,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_pg",
          "run": "https://studio.methora.io/api/providers/p_idx_expert/run"
        }
      ]
    },
    {
      "id": "act_migration",
      "slug": "MigrationPlan",
      "name": "Safe migration plan",
      "description": "A zero-downtime rollout for the schema change.",
      "inputs": [
        "index plan"
      ],
      "output": "migration plan",
      "tools": [],
      "providers": [
        {
          "id": "p_mig_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 400,
          "qualityPct": 79,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_mig_ai/run"
        },
        {
          "id": "p_mig_expert",
          "name": "Viktor Aalto",
          "kind": "expert",
          "priceCredits": 2200,
          "qualityPct": 94,
          "latencySec": 24,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_pg",
          "run": "https://studio.methora.io/api/providers/p_mig_expert/run"
        },
        {
          "id": "p_mig_human",
          "name": "Human on-call review",
          "kind": "human",
          "priceCredits": 3900,
          "qualityPct": 97,
          "latencySec": 3600,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_mig_human/run"
        }
      ]
    },
    {
      "id": "act_jd",
      "slug": "WriteJobDescription",
      "name": "Write the job description",
      "description": "A JD that attracts strong, passive candidates, not just a requirements list.",
      "inputs": [
        "role + must-haves"
      ],
      "output": "job description",
      "tools": [],
      "providers": [
        {
          "id": "p_jd_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 80,
          "latencySec": 8,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_jd_ai/run"
        },
        {
          "id": "p_jd_expert",
          "name": "Priya Anand",
          "kind": "expert",
          "priceCredits": 1500,
          "qualityPct": 93,
          "latencySec": 20,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_recruit",
          "run": "https://studio.methora.io/api/providers/p_jd_expert/run"
        }
      ]
    },
    {
      "id": "act_rubric",
      "slug": "ScreeningRubric",
      "name": "Screening rubric",
      "description": "A structured rubric to screen applicants consistently.",
      "inputs": [
        "job description"
      ],
      "output": "screening rubric",
      "tools": [],
      "providers": [
        {
          "id": "p_rubric_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 80,
          "latencySec": 7,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_rubric_ai/run"
        },
        {
          "id": "p_rubric_expert",
          "name": "Priya Anand",
          "kind": "expert",
          "priceCredits": 1200,
          "qualityPct": 92,
          "latencySec": 18,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_recruit",
          "run": "https://studio.methora.io/api/providers/p_rubric_expert/run"
        }
      ]
    },
    {
      "id": "act_interview",
      "slug": "InterviewKit",
      "name": "Interview kit",
      "description": "A behavioral scorecard and question set mapped across the loop.",
      "inputs": [
        "role + competencies"
      ],
      "output": "interview kit",
      "tools": [],
      "providers": [
        {
          "id": "p_int_ai",
          "name": "General AI model",
          "kind": "machine",
          "priceCredits": 400,
          "qualityPct": 79,
          "latencySec": 9,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_int_ai/run"
        },
        {
          "id": "p_int_expert",
          "name": "Priya Anand",
          "kind": "expert",
          "priceCredits": 1900,
          "qualityPct": 93,
          "latencySec": 22,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_recruit",
          "run": "https://studio.methora.io/api/providers/p_int_expert/run"
        }
      ]
    },
    {
      "id": "act_offer_comp",
      "slug": "OfferAndComp",
      "name": "Offer & comp band",
      "description": "A defensible compensation band and offer guidance.",
      "inputs": [
        "role + target base"
      ],
      "output": "comp band + offer",
      "tools": [
        "tool_comp"
      ],
      "providers": [
        {
          "id": "p_comp_ai",
          "name": "General AI + calculator",
          "kind": "machine",
          "priceCredits": 300,
          "qualityPct": 84,
          "latencySec": 6,
          "guaranteed": false,
          "dataPolicy": "retains",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_comp_ai/run"
        },
        {
          "id": "p_comp_data",
          "name": "Comp-survey provider",
          "kind": "access",
          "priceCredits": 900,
          "qualityPct": 93,
          "latencySec": 5,
          "guaranteed": false,
          "dataPolicy": "no-retention",
          "agentId": null,
          "run": "https://studio.methora.io/api/providers/p_comp_data/run"
        },
        {
          "id": "p_comp_expert",
          "name": "Priya Anand",
          "kind": "expert",
          "priceCredits": 1500,
          "qualityPct": 91,
          "latencySec": 18,
          "guaranteed": false,
          "dataPolicy": "private",
          "agentId": "agt_recruit",
          "run": "https://studio.methora.io/api/providers/p_comp_expert/run"
        }
      ]
    }
  ],
  "workflows": [
    {
      "id": "wf_memo",
      "name": "Investment memo",
      "description": "Turn a company into a decision-ready investment memo. Research, pitch assessment, comparables, a written memo, and a human sign-off.",
      "creator": "Task.Store",
      "steps": [
        "act_research",
        "act_pitch",
        "act_comps",
        "act_memo",
        "act_review"
      ]
    },
    {
      "id": "wf_launch",
      "name": "Offer & launch kit",
      "description": "Rebuild your offer, write the sales page, plan your first customers, and attach a guarantee. Each step fulfilled by a provider of your choice.",
      "creator": "Task.Store",
      "steps": [
        "act_offer",
        "act_copy",
        "act_leads",
        "act_guarantee"
      ]
    },
    {
      "id": "wf_content",
      "name": "Content engine",
      "description": "Go from a topic to a ready-to-publish, SEO-optimized article. Keyword research, a brief, a full draft, an edit, and the on-page SEO package. Route each step to cheap AI or a verified expert.",
      "creator": "Task.Store",
      "steps": [
        "act_keywords",
        "act_brief",
        "act_draft",
        "act_edit",
        "act_seopack"
      ]
    },
    {
      "id": "wf_ship",
      "name": "Ship internationally",
      "description": "Import or export cleanly. Classify the goods (HS code), pick the correct Incoterm, estimate the landed cost with a real calculator, and get the exact document checklist. Cheap AI or a licensed customs broker per step.",
      "creator": "Task.Store",
      "steps": [
        "act_hs",
        "act_incoterm",
        "act_landed",
        "act_doccheck"
      ]
    },
    {
      "id": "wf_dd",
      "name": "Due-diligence pack",
      "description": "A cross-domain diligence pack on a company. Research, a legal contract review, a trade-compliance check, an investor's memo, and a human sign-off. Four different verified experts, composed into one job.",
      "creator": "Task.Store",
      "steps": [
        "act_research",
        "act_legalrev",
        "act_tradecheck",
        "act_memo",
        "act_review"
      ]
    },
    {
      "id": "wf_db",
      "name": "Database health check",
      "description": "Fix a slow PostgreSQL query end-to-end. Diagnose the plan, review the indexes, and get a safe zero-downtime migration plan. Route to cheap AI or a performance engineer.",
      "creator": "Task.Store",
      "steps": [
        "act_diagnose",
        "act_indexrev",
        "act_migration"
      ]
    },
    {
      "id": "wf_hiring",
      "name": "Hiring kit",
      "description": "Everything to run a great hire. A job description that attracts, a screening rubric, a structured interview kit, and a defensible comp band. Cheap AI or a verified recruiter per step.",
      "creator": "Task.Store",
      "steps": [
        "act_jd",
        "act_rubric",
        "act_interview",
        "act_offer_comp"
      ]
    }
  ],
  "tools": [
    {
      "id": "tool_duty",
      "name": "Landed-cost estimator",
      "description": "Estimates duty, import VAT, and total landed cost from shipment value, duty rate, and VAT rate.",
      "agentId": "agt_customs",
      "inputs": [
        {
          "key": "value",
          "label": "goods value",
          "unit": "$"
        },
        {
          "key": "duty",
          "label": "duty rate",
          "unit": "%"
        },
        {
          "key": "vat",
          "label": "import VAT",
          "unit": "%"
        },
        {
          "key": "freight",
          "label": "freight",
          "unit": "$"
        }
      ]
    },
    {
      "id": "tool_valuation",
      "name": "Valuation range",
      "description": "Rough enterprise-value range from ARR and growth, using a growth-adjusted revenue multiple.",
      "agentId": "agt_vc",
      "inputs": [
        {
          "key": "arr",
          "label": "ARR",
          "unit": "$"
        },
        {
          "key": "growth",
          "label": "YoY growth",
          "unit": "%"
        }
      ]
    },
    {
      "id": "tool_unit",
      "name": "Unit-economics check",
      "description": "Computes LTV:CAC and CAC payback from CAC, ARPU (monthly), gross margin, and churn.",
      "agentId": "agt_vc",
      "inputs": [
        {
          "key": "cac",
          "label": "CAC",
          "unit": "$"
        },
        {
          "key": "arpu",
          "label": "monthly ARPU",
          "unit": "$"
        },
        {
          "key": "margin",
          "label": "gross margin",
          "unit": "%"
        },
        {
          "key": "churn",
          "label": "monthly churn",
          "unit": "%"
        }
      ]
    },
    {
      "id": "tool_kw",
      "name": "Keyword priority score",
      "description": "Scores a keyword's opportunity from search volume, ranking difficulty, and business value.",
      "agentId": "agt_seo",
      "inputs": [
        {
          "key": "volume",
          "label": "monthly volume"
        },
        {
          "key": "difficulty",
          "label": "difficulty",
          "unit": "/100"
        },
        {
          "key": "value",
          "label": "business value",
          "unit": "/10"
        }
      ]
    },
    {
      "id": "tool_comp",
      "name": "Comp-band estimator",
      "description": "Builds a salary band (min / mid / max) and a rough equity note from a target base and seniority.",
      "agentId": "agt_recruit",
      "inputs": [
        {
          "key": "base",
          "label": "target base",
          "unit": "$"
        },
        {
          "key": "spread",
          "label": "band spread",
          "unit": "%"
        }
      ]
    }
  ]
}