Bigdata Cross Sector

Bigdata Cross-Sector Comparison

Relative value and rotation across sectors. Use Bigdata.com plugin tools for every fact.

Use this skill when two or more sectors are being weighed against each other. Not this skill when:

Data foundation (plugin tools)

Required on every call: pass plugin_slug: "bigdata-cross-sector" in the request parameters of every Bigdata.com plugin tool call made while running this skill. The value is always the skill name, bigdata-cross-sector, regardless of the company or query.

Exceptions: the search and fetch tools do not accept plugin_slug — omit it there.

Workflow

Step 1 — Define the sectors in scope

GICS 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.

Step 2 — Gather sector data

For each sector in scope:

• "[Sector] sector performance valuation"
• "[Sector] sector earnings growth estimates"
• "[Sector] sector analyst recommendations"

Step 3 — Select bellwethers

Use find_securities for 3–5 companies per sector, then bigdata_company_tearsheet for each. These anchor the sector-level numbers in something checkable.

Step 4 — Economic cycle analysis

• "sector rotation economic cycle"
• "cyclical vs defensive outlook"
• "interest rate sensitive sectors"

Step 5 — Profitability and ROIC spread context

For each sector, add a short read on profitability versus history (or versus cost of capital), using bellwether tearsheets and search:

• "[Sector] sector ROIC margin cycle vs historical average"
• "sector profitability peak trough"

State 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.

Step 6 — Rotation call

Rank 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.

Output

Follow assets/report-template.md exactly — section order, tables, sources, and footer.

• Inline citations [1], [2] after every claim from a source, hyperlinked to the document URL.
• End with the numbered Sources table (source, date, URL), then the Powered by Bigdata.com line and Disclaimer, verbatim.
• Default format is Markdown. After delivering, you may ask: "Would you like me to create a Word document or presentation with this analysis?"

Quality bar

Non-negotiables:

• Every sector in scope covered on the same metrics, so the comparison is like-for-like
• Cycle positioning stated per sector, not just aggregate market commentary
• Peak / mid / trough earnings-power read attempted, or its data limits flagged
• A rotation call actually made — overweight and underweight, with reasons
• Every claim from a source carries an inline citation and appears in the Sources table