Update to Bigdata.com

{
  "name": "bigdata-valuation-snapshot",
  "description": "Answer what a public company is worth and whether it is cheap or expensive, using Bigdata.com data (tearsheet multiples, estimates, margins, peer context). Produces a multiples cross-check against the company's own history and peer median, an implied-expectations read on what the current price already embeds (reverse-DCF reasoning, no model build required), the 2-3 value drivers that dominate, and a cheap / fair / rich verdict. Triggers: \"what is X worth\", \"is X expensive\", \"valuation snapshot for X\", \"what's priced in for X\", \"is X cheap vs peers\", \"how is X valued\", \"fair value for X\".",
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      "relative_path": "README.md",
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      "relative_path": "agents/openai.yaml",
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    {
      "relative_path": "assets/report-template.md",
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    },
    {
      "relative_path": "references/dcf-methodology.md",
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    {
      "relative_path": "references/multiples-framework.md",
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    {
      "relative_path": "references/reverse-dcf.md",
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  ],
  "skill_md_contents": "---\nname: bigdata-valuation-snapshot\ndescription: >\n  Answer what a public company is worth and whether it is cheap or expensive, using Bigdata.com\n  data (tearsheet multiples, estimates, margins, peer context). Produces a multiples cross-check\n  against the company's own history and peer median, an implied-expectations read on what the\n  current price already embeds (reverse-DCF reasoning, no model build required), the 2-3 value\n  drivers that dominate, and a cheap / fair / rich verdict. Triggers: \"what is X worth\", \"is X\n  expensive\", \"valuation snapshot for X\", \"what's priced in for X\", \"is X cheap vs peers\",\n  \"how is X valued\", \"fair value for X\".\n---\n\n# Bigdata Valuation Snapshot\n\nThe lightweight answer path for \"what is it worth\" — no full memo, no standalone model build. Use Bigdata.com plugin tools for every fact.\n\n**Use this skill when** the user wants a valuation read without a full thesis. Not this skill when:\n\n| Request | Use instead |\n|---------|-------------|\n| Full thesis with recommendation and conviction | Investment memo |\n| Explicit bull/base/bear with probabilities and EV | Scenario analysis |\n| Detailed peer table across many metrics | Peer comparables |\n| Valuation in the context of an upcoming print | Earnings preview |\n| Built DCF or sum-of-parts model output | Investment memo (with scripts) |\n\n## Data foundation (plugin tools)\n\n| Tool | Purpose | Prerequisite |\n|------|---------|--------------|\n| `find_securities` | Resolve company name → RavenPack `entity_id` | None |\n| `bigdata_company_tearsheet` | Current and historical multiples, estimates, margins, FCF, segments | `find_securities` |\n| `bigdata_search` | Peer valuation context, analyst views, valuation debates | None |\n\nIf the company name is ambiguous after `find_securities`, ask:\n\n> \"I found multiple companies named [X]. Did you mean [Company A] in [Industry] or [Company B] in [Industry]?\"\n\n## Workflow\n\n### Step 1 — Identify the company\n\nCall `find_securities` with the company name to get the `entity_id`.\n\n### Step 2 — Pull valuation inputs\n\nCall `bigdata_company_tearsheet` for current and historical multiples, consensus estimates, margins, FCF where shown, and segment context.\n\n### Step 3 — Peer and history context\n\n- Use the tearsheet peer set, or search: \"[Company] valuation vs peers EV EBITDA PE comparison\"\n- Note **current** vs **~5-year range** or trailing average where the data allows. If only spot data exists, say so and approximate rather than inventing a range.\n- Pick the multiples that fit the business — a bank on P/TBV, a REIT on P/AFFO, a pre-profit grower on EV/Revenue. Framework: [references/multiples-framework.md](./references/multiples-framework.md).\n\n### Step 4 — Implied expectations (reverse-DCF mindset)\n\nWithout building a model, articulate **what has to go right** at the current price:\n\n- Revenue growth the multiple embeds vs consensus\n- Margin level or trajectory embedded vs recent trend\n- Reinvestment needs and the risk premium implied\n- Whether the market is pricing a re-rating or a de-rating vs fundamentals\n\nMethodology: [references/reverse-dcf.md](./references/reverse-dcf.md). Full DCF mechanics if the user wants depth: [references/dcf-methodology.md](./references/dcf-methodology.md). Run [scripts/reverse_dcf.py](./scripts/reverse_dcf.py) or [scripts/dcf_model.py](./scripts/dcf_model.py) only when the user explicitly asks for scripted or spreadsheet-style output.\n\n### Step 5 — Synthesize\n\nCombine the **multiples cross-check**, the **implied expectations**, and the **2–3 value drivers** that actually move fair value. State plainly whether the stock screens **cheap, fair, or rich** relative to embedded expectations — and name what would change that.\n\n## Output\n\nFollow [assets/report-template.md](./assets/report-template.md) exactly — section order, tables, sources, and footer.\n\n- Add inline citations as superscript-style numbers `[1]`, `[2]` immediately after claims, hyperlinked to the document URL.\n- Every deliverable ends with the **Powered by Bigdata.com** line and the **Disclaimer**, verbatim.\n- Default format is Markdown; offer a Word (.docx) or presentation version at the end if useful.\n\n## Quality bar\n\nPass the PM test before delivering: **What's different?** **What matters (2–3 drivers)?** **What should I do about it?** (net assessment, key risk, next catalyst — no position sizing).\n\nNon-negotiables in every snapshot:\n\n- Multiples chosen for the **business type**, not generic P/E on everything\n- Current level always framed against **history and peers**, or the gap stated explicitly\n- A plain-English statement of what the price embeds — this is the point of the deliverable\n- Cheap / fair / rich verdict given, not hedged into nothing\n- Tearsheet and search preferred over model builds; scripts only on request\n- Facts separated from analysis and implications\n"
}