bigdata-peer-comparables
Skill from Bigdata-com/bigdata-plugins-marketplace
Install
npx skills add Bigdata-com/bigdata-plugins-marketplace --skill bigdata-peer-comparables
Runs the open skills CLI and installs the skill for your agent (Claude Code, Codex, Cursor …). Alternatively copy the skill folder into .claude/skills/ or upload it in Claude.ai under Settings → Skills.
As a Claude Code plugin
/plugin marketplace add anthropics/claude-plugins-official
/plugin install bigdata-com@bigdata-plugins-marketplace
This skill is part of the plugin “bigdata-com”. In Claude Code the plugin installs it together with the plugin’s other skills, commands and hooks.
What does the bigdata-peer-comparables skill do?
Description from the SKILL.md in the source repository.
Questions about bigdata-peer-comparables
What does the bigdata-peer-comparables skill do?
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".
How do I install bigdata-peer-comparables?
In a terminal: npx skills add Bigdata-com/bigdata-plugins-marketplace --skill bigdata-peer-comparables. The skills CLI asks which agents to install it for. In Claude.ai you can upload the skill folder as a ZIP under Settings → Skills.
Which agents can use bigdata-peer-comparables?
Skills use the open SKILL.md format, which Claude Code, Claude.ai, the Claude API, OpenAI Codex, Cursor, Gemini CLI and other agents support.
Is bigdata-peer-comparables safe to use?
Our automatic check found no risky patterns (piped shell scripts, hidden characters, secret access, prompt overrides). It is not a full audit – read the SKILL.md and any scripts before installing.
More skills from Bigdata-com/bigdata-plugins-marketplace
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/downside skew and risk-reward ratio, and what would move probability between the cases. Triggers: "scenario analysis for X", "bull base bear for X", "what's the upside and downside on X", "expected value for X", "probability-weighted view on X", "risk reward on X", "model out the cases for X".
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), insider and VC selling-intention signals, positioning into the event (short interest, borrow, options skew), the historical lock-up-expiry effect with analogs, and a two-sided read. Balanced, no buy/avoid call. Triggers: "180-day lock-up expiry for X", "lockup expiration impact", "post-IPO day 179", "shares unlocking for X", "insider selling after lockup", "float expansion at lockup".
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 float-adjusted index reweight demand netted against new supply, a realistic read on whether founders actually sell, the dual-class governance angle, and a two-sided setup. Balanced, no buy/avoid call. Triggers: "366-day lock-up for X", "founder lock-up expiry", "float expansion for X", "post-IPO one year lockup", "index reweight after float increase", "founder selling after IPO".
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 history, TAM and listed comparables, IPO-window conditions, and 90-day sentiment — closing with bull and bear debates and watch points, never a participate/avoid call. Triggers: "analyze the IPO of X", "S-1 analysis", "upcoming listing for X", "IPO report on X", "should I look at X's IPO", "pre-IPO research on X", "X IPO valuation".
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: "quick take on X", "what do you think of X", "give me a fast view on X", "thoughts on X", "X in a nutshell", "one-liner on X", "is X interesting right now".
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 fixed income and currency views for each. Triggers: "compare US vs Europe vs Asia", "which regions look attractive", "regional allocation", "developed vs emerging markets", "Europe vs US equities", "global allocation view".
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 governance — each rated by likelihood and impact, with a distress screen when leverage is stretched, mitigation status, a priority matrix, and a scenario bridge to value drivers. Triggers: "risk assessment for X", "assess risks for X", "what are the risks with X", "what could go wrong at X", "risk factors for X", "how risky is X", "downside risks for X".
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 states the variant view as a specific, falsifiable claim with a time horizon — plus what the market is missing, why the mispricing persists, the evidence for the view, and what would disprove it. Triggers: "variant perception on X", "where do I differ from consensus on X", "what is the market missing on X", "non-consensus view on X", "what's priced in versus reality for X", "contrarian case for X".
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