Update to Bigdata.com

{
  "name": "bigdata-cross-sector",
  "description": "Compare two or more sectors using Bigdata.com data — relative valuations, earnings growth, analyst sentiment, and where each sits in the economic cycle — and turn that into a rotation call with overweight and underweight recommendations. Includes bellwether-level fundamentals per sector and a profitability/ROIC-versus-history read that says whether current valuations sit on peak, mid-cycle, or trough earnings power. Triggers: \"compare X vs Y sectors\", \"which sectors look attractive\", \"sector rotation\", \"cyclicals vs defensives\", \"relative value across sectors\", \"should I rotate out of X into Y\".",
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      "relative_path": "README.md",
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      "relative_path": "agents/openai.yaml",
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      "relative_path": "references/porter-five-forces.md",
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  "skill_md_contents": "---\nname: bigdata-cross-sector\ndescription: >\n  Compare two or more sectors using Bigdata.com data — relative valuations, earnings growth,\n  analyst sentiment, and where each sits in the economic cycle — and turn that into a rotation\n  call with overweight and underweight recommendations. Includes bellwether-level fundamentals\n  per sector and a profitability/ROIC-versus-history read that says whether current valuations\n  sit on peak, mid-cycle, or trough earnings power. Triggers: \"compare X vs Y sectors\",\n  \"which sectors look attractive\", \"sector rotation\", \"cyclicals vs defensives\",\n  \"relative value across sectors\", \"should I rotate out of X into Y\".\n---\n\n# Bigdata Cross-Sector Comparison\n\nRelative value and rotation across sectors. Use Bigdata.com plugin tools for every fact.\n\n**Use this skill when** two or more sectors are being weighed against each other. Not this skill when:\n\n| Request | Use instead |\n|---------|-------------|\n| One sector in depth | Sector analysis |\n| A sector inside one country | Country-sector analysis |\n| An actionable playbook for investing one sector | Sector playbook |\n| Regions rather than sectors | Regional comparison |\n| Individual companies within a sector | Peer comparables |\n\n## Data foundation (plugin tools)\n\n| Tool | Purpose | Prerequisite |\n|------|---------|--------------|\n| `bigdata_search` | Sector performance, valuation, growth, cycle context | None |\n| `find_securities` | Entity ids for 3–5 bellwethers per sector | None |\n| `bigdata_company_tearsheet` | Bellwether fundamentals and estimates | `find_securities` |\n\n## Workflow\n\n### Step 1 — Define the sectors in scope\n\nGICS sectors: Information Technology, Health Care, Financials, Consumer Discretionary, Consumer Staples, Industrials, Energy, Materials, Real Estate, Communication Services, Utilities. If the user named sectors, use theirs; otherwise confirm which to compare rather than sweeping all eleven.\n\n### Step 2 — Gather sector data\n\nFor **each** sector in scope:\n\n- \"[Sector] sector performance valuation\"\n- \"[Sector] sector earnings growth estimates\"\n- \"[Sector] sector analyst recommendations\"\n\n### Step 3 — Select bellwethers\n\nUse `find_securities` for 3–5 companies per sector, then `bigdata_company_tearsheet` for each. These anchor the sector-level numbers in something checkable.\n\n### Step 4 — Economic cycle analysis\n\n- \"sector rotation economic cycle\"\n- \"cyclical vs defensive outlook\"\n- \"interest rate sensitive sectors\"\n\n### Step 5 — Profitability and ROIC spread context\n\nFor **each** sector, add a short read on profitability versus history (or versus cost of capital), using bellwether tearsheets and search:\n\n- \"[Sector] sector ROIC margin cycle vs historical average\"\n- \"sector profitability peak trough\"\n\nState whether current valuations sit on **peak**, **mid-cycle**, or **trough-like** earnings power — where the evidence allows. This is the difference between a comparison that misleads and one that informs: a low P/E on peak earnings is not cheap. Deeper framework: [references/porter-five-forces.md](./references/porter-five-forces.md).\n\n### Step 6 — Rotation call\n\nRank the sectors and state the rotation explicitly: what to overweight, what to underweight, and the specific reason for each. Tie the call to cycle positioning and the earnings-power read, not to trailing multiples alone.\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:\n\n- Every sector in scope covered on the **same** metrics, so the comparison is like-for-like\n- Cycle positioning stated per sector, not just aggregate market commentary\n- Peak / mid / trough earnings-power read attempted, or its data limits flagged\n- A rotation call actually made — overweight and underweight, with reasons\n- Every claim from a source carries an inline citation and appears in the Sources table\n"
}