bigdata-com

Plugin bigdata-com from marketplace claude-plugins-official in CATHLEEN-ANN-TICO/CATHLEEN-TICO. Commands read from the repo's manifest — 1 month ago.

Skills (27)

• bigdata-catalyst-monitor

  Map the dated events that could move a public company over the next few quarters, using Bigdata.com data (events calendar, filings, news, tearsheet). Covers scheduled catalysts — earnings, investor days, index reviews, lock-up and patent expiries, regulatory decision dates — and foreseeable unschedu

• bigdata-company-brief

  Generate a company brief — a cited 30-day summary of what happened at a public company and why it matters — using Bigdata.com data (news, filings, transcripts, tearsheet financials). Findings are categorized into financial results, product and tech launches, M&A and partnerships, regulatory and lega

• bigdata-country-analysis

  Produce a deep country economic analysis using Bigdata.com data — GDP, inflation, monetary policy, labor markets, debt mechanics, and investment implications. Goes beyond a point-in-time snapshot: structural and historical context (sector transformation, labor productivity), debt composition and ser

• bigdata-country-sector-analysis

  Analyze a specific sector inside a specific country or region using Bigdata.com data — combining the macroeconomic backdrop (GDP, inflation, rates, policy), country-specific sector trends and valuations, fundamentals of country-domiciled sector leaders confirmed by geographic revenue exposure, the p

• bigdata-cross-sector

  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 profit

• bigdata-earnings-digest

  Analyze a public company's latest reported earnings — a cited post-print digest using Bigdata.com data (results, consensus and surprise, transcript, analyst reactions, tearsheet financials). Breaks down revenue and margins, segment and operating KPIs, management guidance, cash flow and balance sheet

• bigdata-earnings-preview

  Create a forward-looking earnings preview for a public company ahead of its next earnings call, using Bigdata.com data (estimates, tearsheet financials, news, filings, transcripts, events calendar). Produces an EPIC driver table, earnings quality screen with forward watch-fors, structured sentiment

• bigdata-earnings-quality-screen

  Screen a public company's reported earnings for quality and accounting red flags using Bigdata.com data and filings. Covers cash conversion (OCF/NI, FCF/NI across periods), accruals and the balance-sheet accrual ratio, working-capital signals (DSO, DIO, DPO versus revenue growth), revenue-recognitio

• bigdata-earnings-reaction

  Write a tight post-earnings reaction note using Bigdata.com data — headline numbers versus consensus with beat/miss magnitude, what mattered on both sides, a prior-versus-new guidance table, an explicit thesis check (Intact / Strengthened / Weakened / Broken) with evidence, the estimate and price-ta

• bigdata-g7-comparison

  Benchmark the seven G7 economies side by side using Bigdata.com data — the United States, Japan, Germany, the United Kingdom, France, Italy, and Canada. Produces a like-for-like indicator table (GDP, inflation, unemployment, policy rate, fiscal position), central bank stance and rate-path divergence

• bigdata-investment-memo

  Write a full institutional investment memo on a public company using Bigdata.com data — thesis, variant perception versus consensus, valuation, risks, catalysts, and an explicit recommendation with conviction. Runs the complete workflow: EPIC-filtered primary drivers, FaVeS variant perception, earni

• bigdata-moat-governance-review

  Assess how durable a public company's competitive advantage is and whether management can be trusted with the capital, using Bigdata.com data. Covers moat identification by type with evidence, moat strength via ROIC versus WACC, pricing power and share trend, a competitive advantage period estimate

• bigdata-peer-comparables

  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 pee

• bigdata-post-ipo-day1

  Write a first-trading-day post-IPO reaction note for a newly listed company using Bigdata.com data plus web market data. Anchors the deal (offer price vs range, shares, greenshoe, implied market cap), reconstructs day 1 (open, intraday range, close, volume, first-day return), reads demand and float

• bigdata-post-ipo-day14

  Write a day-14 post-IPO note on potential NASDAQ-100 fast-track index inclusion for a recently listed large-cap, using Bigdata.com data plus web search for index methodology and market data. Covers two-week trading status, an eligibility check against Nasdaq's current published rules (cited, never a

• bigdata-post-ipo-day179

  Write a day-179 post-IPO note on the 180-day lock-up expiry using Bigdata.com data plus filings and market data. Covers lock-up terms from the prospectus (expiry date, covered holders, share count, early-release provisions), float and overhang math (post-expiry float, days-to-trade versus ADV), insi

• bigdata-post-ipo-day365

  Write a day-365 post-IPO note on the 366-day founder and significant-investor lock-up expiry and float expansion toward 15-20%, using Bigdata.com data plus filings and market data. Covers the staggered lock-up structure from the prospectus, float expansion math and days-to-trade, the offsetting floa

• bigdata-pre-ipo-analysis

  Produce a balanced pre-IPO research note on an upcoming, not-yet-listed company using its S-1/F-1 plus Bigdata.com data. Covers deal structure (price range, shares, greenshoe, implied valuation, underwriters, lock-ups, share classes), two years plus interim financials, business model and funding his

• bigdata-quick-take

  Give a fast, PM-style quick take on a stock using Bigdata.com data — a one-line current view, the 2-3 drivers that actually matter right now, the key risks and what would change the view, and the near-term setup with the next catalyst. Deliberately short: one page, no full thesis, no model. Triggers

• bigdata-regional-comparison

  Compare regions or blocs using Bigdata.com data — economic indicators, market performance, and cross-asset views — and turn that into an allocation recommendation. Covers growth, inflation, policy and labor per region, comparative developed-versus-emerging analysis, regional equity valuations, and f

• bigdata-risk-assessment

  Produce a comprehensive risk assessment for a public company using Bigdata.com data (10-K risk factors, 8-K material events, news, tearsheet financials). Covers six categories — regulatory and legal, competitive and moat erosion, operational, financial and balance sheet, macro, and management and go

• bigdata-scenario-analysis

  Build bull, base, and bear cases for a public company using Bigdata.com data — with explicit line-item assumptions, justified probability weights summing to 100%, a value or price per scenario with the bridge shown, a probability-weighted expected value and expected return versus spot, the upside/do

• bigdata-sector-analysis

  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 bellw

• bigdata-sector-playbook

  Build an actionable investment playbook for a sector using Bigdata.com data and sector-specific frameworks — the KPIs that actually matter in that sector, how to value companies in it and why, the live debates and where consensus sits on each, valuation context against the sector's own history, a su

• bigdata-thematic-research

  Research a macro investment theme using Bigdata.com data — scope and sub-themes, investment implications, sector impact, named beneficiaries and vulnerable losers with tearsheet fundamentals, the policy and regulatory dimension, geographic impact, and concrete implementation ideas. Covers themes suc

• bigdata-valuation-snapshot

  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 alrea

• bigdata-variant-perception

  State explicitly where your view on a public company differs from consensus, using Bigdata.com data. Establishes the consensus baseline from estimates and sell-side posture, applies the EPIC filter to candidate differentiators, frames the view on FaVeS (fundamentals, valuation, sentiment), and state

MCP servers (1)

• bigdata.com

  connects to: https://mcp.bigdata.com

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