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Covers scheduled catalysts — earnings, investor days, index reviews, lock-up and patent expiries, regulatory decision dates — and foreseeable unscheduled ones — litigation milestones, product cycles, contract renewals, refinancings. Each catalyst carries a date or window, likely direction, magnitude, confidence, and what to watch, ranked by expected impact rather than by date alone. 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Use whenever a request names BOTH a sector AND a country or region. Triggers: \"macro analysis of X in Y\", \"European financials outlook\", \"US technology sector view\", \"India consumer sector\", \"China EV sector\", \"Japanese semiconductor industry\", \"[sector] in [country]\".","plugin_release_skill_id":"pluginrsk_6a8d609ce07c8191bb96d9b302c84a7f"},{"name":"bigdata-cross-sector","interface":{"brand_color":null,"iconography":"hierarchy","display_name":"Bigdata Cross-Sector Comparison","default_prompt":"Use $bigdata-cross-sector to compare sectors and assess rotation.","icon_large_url":"https://files.openai.com/content?id=file_000000009eac81f4843dfec0826a5c51","icon_small_url":"https://files.openai.com/content?id=file_00000000a82881f4bdc029ff91ce5908","short_description":"Compare sectors and call rotation"},"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\".","plugin_release_skill_id":"pluginrsk_6a8d609d33488191ac773a0acc77d4da"},{"name":"bigdata-earnings-digest","interface":{"brand_color":null,"iconography":"chart","display_name":"Bigdata Earnings Digest","default_prompt":"Use $bigdata-earnings-digest to analyze the latest earnings of","icon_large_url":"https://files.openai.com/content?id=file_00000000215c81f49e1398ba0409a251","icon_small_url":"https://files.openai.com/content?id=file_000000008b7c824693a0901da0a6cd8a","short_description":"Analyze reported earnings with cited breakdowns"},"description":"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, and surprises versus expectations with sustainable-vs-one-time framing, plus a bull/bear thesis check, quality signals with forward watch-fors, sentiment and positioning, a post-print scenario refresh with probability-weighted expected value, and a valuation cross-check. Triggers: \"analyze X earnings\", \"earnings digest for X\", \"how did X do last quarter\", \"X Q3 results\", \"break down X's earnings\", \"what did X report\", \"post-earnings analysis\", \"did X beat or miss\".","plugin_release_skill_id":"pluginrsk_6a8d609d58b481918fd3d0357322c434"},{"name":"bigdata-earnings-quality-screen","interface":{"brand_color":null,"iconography":"radar","display_name":"Bigdata Earnings Quality Screen","default_prompt":"Use $bigdata-earnings-quality-screen to screen a company's earnings quality.","icon_large_url":"https://files.openai.com/content?id=file_0000000011fc8243adfe38fd190bca03","icon_small_url":"https://files.openai.com/content?id=file_0000000053d4824383ff087c34547de5","short_description":"Screen reported earnings for accounting red flags"},"description":"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-recognition and capitalization flags, the GAAP versus non-GAAP gap and the nature of the add-backs, and an optional Beneish M-Score with inputs shown — closing with a verdict on how far the reported numbers can be trusted. 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Shorter and more decision-focused than a full earnings digest. Triggers: \"earnings reaction for X\", \"how should I react to X's results\", \"does X's quarter change the thesis\", \"X print reaction\", \"revise my numbers after X earnings\", \"was X's quarter good enough\".","plugin_release_skill_id":"pluginrsk_6a8d609daf388191acab95232f203b9b"},{"name":"bigdata-g7-comparison","interface":{"brand_color":null,"iconography":"chart","display_name":"Bigdata G7 Comparison","default_prompt":"Use $bigdata-g7-comparison to compare the G7 economies.","icon_large_url":"https://files.openai.com/content?id=file_00000000727c822f84f6b149ae7389b7","icon_small_url":"https://files.openai.com/content?id=file_00000000274c82118f6441f475d5ad0e","short_description":"Benchmark the G7 economies side by side"},"description":"Benchmark the seven G7 economies side by side using Bigdata.com data — the United States, Japan, Germany, the United Kingdom, France, Italy, and Canada. 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Covers moat identification by type with evidence, moat strength via ROIC versus WACC, pricing power and share trend, a competitive advantage period estimate with erosion signals, industry structure via five forces, the capital allocation track record across M&A, buybacks, dividends and reinvestment, and governance — board independence, dual roles, compensation design, related-party exposure, insider activity. Triggers: \"does X have a moat\", \"moat review for X\", \"how durable is X's advantage\", \"is X's management any good\", \"capital allocation at X\", \"governance review of X\", \"competitive advantage of X\".","plugin_release_skill_id":"pluginrsk_6a8d609e2bc481919cd664bea04e0d97"},{"name":"bigdata-peer-comparables","interface":{"brand_color":null,"iconography":"chart","display_name":"Bigdata Peer Comparables","default_prompt":"Use $bigdata-peer-comparables to compare a company against its peers.","icon_large_url":"https://files.openai.com/content?id=file_000000004dcc820aa466c6aad7892897","icon_small_url":"https://files.openai.com/content?id=file_00000000c38881f4a65013ef0a52026b","short_description":"Compare a company against its peer set"},"description":"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\".","plugin_release_skill_id":"pluginrsk_6a8d609e595481918622e380b4b7cc36"},{"name":"bigdata-post-ipo-day1","interface":{"brand_color":null,"iconography":"pen","display_name":"Bigdata Post-IPO Day 1","default_prompt":"Use $bigdata-post-ipo-day1 for a first-trading-day post-IPO reaction note.","icon_large_url":"https://files.openai.com/content?id=file_000000006e3481f7bc5fd0a92309bd08","icon_small_url":"https://files.openai.com/content?id=file_0000000075b081f681aa17a544734c92","short_description":"Analyze a first-trading-day IPO debut"},"description":"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 mechanics including stabilization, resets valuation against peers at the close, notes the quiet-period coverage gap, and maps the dated post-IPO timeline. Balanced, no buy/avoid call. Triggers: \"post-IPO day 1\", \"first day trading reaction for X\", \"how did X's IPO debut\", \"X IPO pop\", \"X first day of trading\", \"IPO debut analysis\".","plugin_release_skill_id":"pluginrsk_6a8d609ea3c48191bbb4693650c26a8a"},{"name":"bigdata-post-ipo-day14","interface":{"brand_color":null,"iconography":"chart","display_name":"Bigdata Post-IPO Day 14","default_prompt":"Use $bigdata-post-ipo-day14 for a NASDAQ-100 fast-track inclusion note.","icon_large_url":"https://files.openai.com/content?id=file_00000000c7f481f9b8bf440fff4c022c","icon_small_url":"https://files.openai.com/content?id=file_00000000fbf481f49c57bc2326f00561","short_description":"Assess NASDAQ-100 fast-track inclusion impact"},"description":"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 assumed), a float-adjusted index weight and implied passive-demand estimate with the math shown, days-to-cover versus ADV, the historical index effect and reversal risk, and dated watch points. Balanced, no buy/avoid call. Triggers: \"NASDAQ-100 fast track for X\", \"index inclusion impact on X\", \"post-IPO day 14\", \"will X be added to the Nasdaq-100\", \"passive flows from index inclusion\".","plugin_release_skill_id":"pluginrsk_6a8d609ecf048191b240c08c766a38a1"},{"name":"bigdata-post-ipo-day179","interface":{"brand_color":null,"iconography":"pen","display_name":"Bigdata Post-IPO Day 179","default_prompt":"Use $bigdata-post-ipo-day179 for a 180-day lock-up expiry note.","icon_large_url":"https://files.openai.com/content?id=file_00000000d214822fadec2d1507bd0376","icon_small_url":"https://files.openai.com/content?id=file_00000000f59081fdbd7aa22f157d9bf3","short_description":"Size the 180-day IPO lock-up overhang"},"description":"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\".","plugin_release_skill_id":"pluginrsk_6a8d609ef8f881918483bd1112ee96ab"},{"name":"bigdata-post-ipo-day365","interface":{"brand_color":null,"iconography":"code","display_name":"Bigdata Post-IPO Day 365","default_prompt":"Use $bigdata-post-ipo-day365 for a founder lock-up and float-expansion note.","icon_large_url":"https://files.openai.com/content?id=file_00000000cca081f48d39d5153214e3fb","icon_small_url":"https://files.openai.com/content?id=file_00000000320081f4b0362e43ec607269","short_description":"Analyze founder lock-up expiry and float expansion"},"description":"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\".","plugin_release_skill_id":"pluginrsk_6a8d609f21308191b5364d7aee36625b"},{"name":"bigdata-pre-ipo-analysis","interface":{"brand_color":null,"iconography":"search","display_name":"Bigdata Pre-IPO Analysis","default_prompt":"Use $bigdata-pre-ipo-analysis to research an upcoming IPO.","icon_large_url":"https://files.openai.com/content?id=file_00000000224881f48e57390853d0205f","icon_small_url":"https://files.openai.com/content?id=file_00000000db9c81f7aae0705a792dc787","short_description":"Research an upcoming IPO from its S-1"},"description":"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\".","plugin_release_skill_id":"pluginrsk_6a8d609f484c8191ab79988f1c92baaa"},{"name":"bigdata-quick-take","interface":{"brand_color":null,"iconography":"default","display_name":"Bigdata Quick Take","default_prompt":"Use $bigdata-quick-take for a fast PM-style view on a stock.","icon_large_url":"https://files.openai.com/content?id=file_000000002a28820aba1d9358da359c28","icon_small_url":"https://files.openai.com/content?id=file_000000002328820cae9e10e2d507f707","short_description":"Give a fast PM-style view on a stock"},"description":"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\".","plugin_release_skill_id":"pluginrsk_6a8d609f76088191a5554cb5f2e85b6e"},{"name":"bigdata-regional-comparison","interface":{"brand_color":null,"iconography":"chart","display_name":"Bigdata Regional Comparison","default_prompt":"Use $bigdata-regional-comparison to compare regional economies and markets.","icon_large_url":"https://files.openai.com/content?id=file_00000000cc4481f797397c140d068b83","icon_small_url":"https://files.openai.com/content?id=file_00000000fddc820ca43e19178886a95f","short_description":"Compare regions and allocate across them"},"description":"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\".","plugin_release_skill_id":"pluginrsk_6a8d609f9b34819193d134e03a439343"},{"name":"bigdata-risk-assessment","interface":{"brand_color":null,"iconography":"radar","display_name":"Bigdata Risk Assessment","default_prompt":"Use $bigdata-risk-assessment to assess the risks facing a public company.","icon_large_url":"https://files.openai.com/content?id=file_000000009df0820ca6febbc56ee88068","icon_small_url":"https://files.openai.com/content?id=file_00000000588c821180e2bf94d29faa2a","short_description":"Rate company risks by likelihood and impact"},"description":"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. 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