Update to Bigdata.com

{
  "name": "bigdata-peer-comparables",
  "description": "Compare a public company against its peer set using Bigdata.com data — valuation multiples, growth, profitability, returns, leverage, and sentiment — to judge relative attractiveness. Builds the peer set with an explicit rationale for inclusion and exclusion, tabulates like-for-like metrics with peer median and quartile positioning, decomposes any premium or discount into what fundamentals justify versus what they do not, and closes with a relative verdict. Triggers: \"compare X to its peers\", \"peer comparables for X\", \"how does X screen vs competitors\", \"is X cheap relative to peers\", \"comps table for X\", \"relative valuation of X\", \"who are X's peers\".",
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    {
      "relative_path": "README.md",
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    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 330
    },
    {
      "relative_path": "assets/bigdata-icon-black.svg",
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    {
      "relative_path": "assets/report-template.md",
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    {
      "relative_path": "references/multiples-framework.md",
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    {
      "relative_path": "references/sector-routing.md",
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    {
      "relative_path": "scripts/peer_comparables.py",
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    }
  ],
  "skill_md_contents": "---\nname: bigdata-peer-comparables\ndescription: >\n  Compare a public company against its peer set using Bigdata.com data — valuation multiples,\n  growth, profitability, returns, leverage, and sentiment — to judge relative attractiveness.\n  Builds the peer set with an explicit rationale for inclusion and exclusion, tabulates\n  like-for-like metrics with peer median and quartile positioning, decomposes any premium or\n  discount into what fundamentals justify versus what they do not, and closes with a relative\n  verdict. Triggers: \"compare X to its peers\", \"peer comparables for X\", \"how does X screen vs\n  competitors\", \"is X cheap relative to peers\", \"comps table for X\", \"relative valuation of X\",\n  \"who are X's peers\".\n---\n\n# Bigdata Peer Comparables\n\nRelative screen against a defensible peer set. Use Bigdata.com plugin tools for every fact.\n\n**Use this skill when** the question is relative. Not this skill when:\n\n| Request | Use instead |\n|---------|-------------|\n| Absolute value — what is it worth | Valuation snapshot |\n| Sector-level performance and themes | Sector analysis |\n| Sectors ranked against each other | Cross-sector comparison |\n| Full thesis with recommendation | Investment memo |\n\n## Data foundation (plugin tools)\n\n| Tool | Purpose | Prerequisite |\n|------|---------|--------------|\n| `find_securities` | Resolve the subject and every peer → `entity_id` | None |\n| `bigdata_company_tearsheet` | Multiples, growth, margins, returns, leverage, sentiment per company | `find_securities` |\n| `bigdata_search` | Peer-set validation, competitive positioning, valuation debate | 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 subject company\n\nCall `find_securities`, then `bigdata_company_tearsheet` to establish the business model, segment mix, and size.\n\n### Step 2 — Construct the peer set (state the rationale)\n\nPick **5–8 peers** on business model and economics, not just sector label. Screen on: revenue model, end markets, size within an order of magnitude, growth profile, geographic mix, and capital intensity.\n\nSearch to validate: \"[Company] competitors peer group comparison\", \"[Company] closest comparable companies\".\n\n**Write down why each peer is in — and name the obvious candidates you excluded, with the reason.** A comps table is only as good as its peer set, and an unstated peer set is unfalsifiable.\n\n### Step 3 — Pull peer data\n\nRun `find_securities` then `bigdata_company_tearsheet` for each peer. Pull the **same** metrics for everyone, from the same period, so the table is like-for-like. Note any fiscal-year misalignment.\n\n### Step 4 — Build the comparables table\n\n| Category | Metrics |\n|----------|---------|\n| Valuation | EV/Sales, EV/EBITDA, P/E (NTM and TTM), FCF yield, plus the sector-standard multiple |\n| Growth | Revenue growth (TTM, NTM consensus), EPS growth |\n| Profitability | Gross margin, EBITDA margin, operating margin, FCF margin |\n| Returns | ROIC, ROE |\n| Leverage | Net debt/EBITDA, interest coverage |\n| Sentiment | Mean price target vs spot, rating distribution, quantified sentiment where available |\n\nUse the multiples that fit the business — P/TBV for banks, P/AFFO for REITs, EV/Sales for pre-profit growth. Framework: [references/multiples-framework.md](./references/multiples-framework.md). Sector-specific KPIs: [references/sector-routing.md](./references/sector-routing.md).\n\n### Step 5 — Position against the set\n\nFor each metric: the subject's value, the **peer median**, and its **quartile**. Percentile positioning shows what a raw table hides.\n\n### Step 6 — Decompose the premium or discount\n\nThe core analytical step. If the company trades at a premium or discount, ask **what fundamentals justify it** — faster growth, higher margins, better returns, lower leverage, cleaner accounting — and how much of the gap remains **unexplained**. An unexplained gap is where the opportunity or the warning sits.\n\n### Step 7 — Verdict\n\nState relative attractiveness with the specific drivers, plus what would close or widen the gap.\n\nRun [scripts/peer_comparables.py](./scripts/peer_comparables.py) only when the user explicitly wants a scripted comp table.\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 `[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 spreadsheet-style version if useful.\n\n## Quality bar\n\nNon-negotiables:\n\n- Peer set **justified**, with exclusions named — this is the credibility of the whole deliverable\n- Same metrics, same period, for every company; fiscal misalignment flagged\n- Multiples chosen for the business type, not generic P/E across a mixed set\n- Peer median **and** quartile positioning given, not just raw values\n- Premium/discount **decomposed** into justified and unexplained\n- A relative verdict stated, with what would close the gap\n"
}