bigdata-sector-analysis

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".

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".

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".

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".

Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.

Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.

Search, scrape, crawl, extract structured data, and monitor web pages via the ScrapeGraph AI CLI. Use when the user asks to search the web, scrape a webpage, grab content from a URL, extract JSON from a site, crawl documentation or site sections, monitor a page for changes, inspect request history, check ScrapeGraph credits, or validate API setup.

When the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'battle card,' 'battlecard,' 'competitive one-pager,' 'win-loss analysis,' 'why are we losing deals,' 'loss reasons,' 'how do I respond to this objection,' 'prospect just said,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps,' 'cold call script,' or 'voicemail script.' Use this for any document or asset that helps a sales team close deals. For public competitor comparison pages, see competitors. For running buyer interviews or churn research, see customer-research. For marketing website copy, see copywriting. For cold outreach emails, see cold-email. For the offer being sold (bonuses, guarantees, pricing structure), see offers.

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.