Update to Bigdata.com

{
  "description": "Analyze a market sector using Bigdata.com data — performance, valuations, themes, sub-industries, and upcoming catalysts. Maps the sector to its own operating and valuation KPIs rather than generic P/E, reads cycle and profitability positioning as early, mid, or late versus history, aggregates bellwether tearsheet metrics, and closes with a positioning call plus top picks and areas to avoid. Triggers: \"analyze the X sector\", \"what's happening in X sector\", \"X sector outlook\", \"how is the X industry doing\", \"X sector valuations\", \"is the X sector attractive\", \"semiconductor/energy/healthcare sector view\".",
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  "name": "bigdata-sector-analysis",
  "skill_md_contents": "---\nname: bigdata-sector-analysis\ndescription: >\n  Analyze a market sector using Bigdata.com data — performance, valuations, themes,\n  sub-industries, and upcoming catalysts. Maps the sector to its own operating and valuation\n  KPIs rather than generic P/E, reads cycle and profitability positioning as early, mid, or late\n  versus history, aggregates bellwether tearsheet metrics, and closes with a positioning call\n  plus top picks and areas to avoid. Triggers: \"analyze the X sector\", \"what's happening in X\n  sector\", \"X sector outlook\", \"how is the X industry doing\", \"X sector valuations\",\n  \"is the X sector attractive\", \"semiconductor/energy/healthcare sector view\".\n---\n\n# Bigdata Sector Analysis\n\nFull read on one sector: where it trades, what drives it, and what is coming. Use Bigdata.com plugin tools for every fact.\n\n**Use this skill when** the subject is one sector. Not this skill when:\n\n| Request | Use instead |\n|---------|-------------|\n| Two or more sectors compared, or rotation | Cross-sector comparison |\n| A sector inside a specific country or region | Country-sector analysis |\n| An actionable KPI-and-debates playbook for investing the sector | Sector playbook |\n| A macro theme that cuts across sectors | Thematic research |\n| One company in the sector | Company brief / investment memo |\n\n## Data foundation (plugin tools)\n\n| Tool | Purpose | Prerequisite |\n|------|---------|--------------|\n| `bigdata_search` | Sector trends, valuations, policy, catalysts, cycle context | None |\n| `find_securities` | Entity ids for 5–10 sector bellwethers | None |\n| `bigdata_company_tearsheet` | Per-company metrics, estimates, sentiment, segments | `find_securities` |\n| `bigdata_events_calendar` | Upcoming earnings and conferences | `find_securities` |\n\nRun **5–10 targeted searches** across the workflow. Include temporal context (\"last 30 days\", \"2026 outlook\").\n\n## Workflow\n\n### Step 1 — Sector context\n\nSearch:\n\n- \"[Sector] sector outlook trends analysis\"\n- \"[Sector] sector earnings performance\"\n- \"[Sector] sector headwinds tailwinds\"\n- \"[Sector] sector valuations multiples\"\n- \"[Sector] sector regulatory policy\"\n\n### Step 2 — Sector-specific KPI lens (GICS)\n\nDo **not** rely only on generic P/E, P/S, and EV/EBITDA. Map the sector to its primary operating and valuation KPIs:\n\n| GICS sector | Emphasize these KPIs |\n|-------------|----------------------|\n| Information Technology / Software-SaaS | ARR growth, NRR, Rule of 40, FCF margin, payback |\n| Financials | NIM, CET1 / capital, credit costs, ROTCE, efficiency |\n| Health Care (incl. Pharma) | Growth drivers, pipeline / patent, R&D, payer mix, regulatory |\n| Real Estate (REITs) | AFFO, NAV, cap rates vs bonds, same-store NOI |\n| Industrials | Backlog, book-to-bill, margin mix, OEM / capex cycle |\n| Consumer Discretionary / Staples | Same-store sales, promo, input costs, private label |\n| Energy | Commodity linkage, breakeven, FCF at forward curve, capital discipline |\n| Materials | Price/volume, capacity, inventory, China / construction linkage |\n| Communication Services | Subscribers, ARPU, churn, ad market / streaming economics |\n| Utilities | Allowed ROE, rate case risk, weather / load growth |\n| (Other) | Default to margin trajectory, ROIC vs peers, and segment growth |\n\nDeeper playbooks: [references/sector-routing.md](./references/sector-routing.md).\n\n### Step 3 — Key companies\n\nUse `find_securities` for 5–10 major sector companies, then `bigdata_company_tearsheet` for each: financial metrics and performance, analyst estimates and sentiment, revenue segmentation, ESG scores.\n\n### Step 4 — Aggregate sector metrics\n\nFrom the tearsheets, compile sector-relevant multiples (per Step 2, not only P/E), the Step 2 KPIs where visible, revenue and earnings growth trends, the analyst rating distribution, and sentiment indicators.\n\n### Step 5 — Cycle and profitability positioning\n\nAdd brief, evidence-based cycle context:\n\n- Search \"[Sector] sector ROIC profitability cycle outlook\" and \"[Sector] margin cycle vs history\"\n- State whether ROIC (or a sector proxy) and margins look **early / mid / late** versus a normal cycle — or flag the data limits\n- Industry-economics mental model: [references/porter-five-forces.md](./references/porter-five-forces.md)\n\n### Step 6 — Catalysts\n\nSearch:\n\n- \"[Sector] regulatory changes policy\"\n- \"[Sector] technology disruption\"\n- \"[Sector] M&A consolidation\"\n- \"[Sector] earnings expectations\"\n- \"[Sector] supply chain tariffs\"\n\n### Step 7 — Events calendar\n\nUse `bigdata_events_calendar` for upcoming earnings and conferences across the bellwethers.\n\n## Output\n\nFollow [assets/report-template.md](./assets/report-template.md) exactly — section order, tables, sources, and footer.\n\n- **Inline citations** `[1]`, `[2]` after every claim from a source, hyperlinked to the document URL.\n- End with the numbered **Sources** table (source, date, URL), then the **Powered by Bigdata.com** line and **Disclaimer**, verbatim.\n- Default format is Markdown. After delivering, you may ask: \"Would you like me to create a Word document or presentation with this analysis?\"\n\n## Quality bar\n\nNon-negotiables in every sector analysis:\n\n- Sector-specific KPIs present — a report built only on P/E has not done the job\n- Cycle positioning stated (early / mid / late) or its data limits flagged\n- Tailwinds and headwinds name **which companies** are exposed\n- Positioning call given: overweight / neutral / underweight, with top picks and areas to avoid\n- Every claim from a source carries an inline citation and appears in the Sources table\n\n## GICS sectors reference\n\nInformation Technology, Health Care, Financials, Consumer Discretionary, Consumer Staples, Industrials, Energy, Materials, Real Estate, Communication Services, Utilities.\n"
}