52 个可安装 agents
该行业包含 8 个角色族群。
该行业包含 8 个角色族群。
Monitors supplier, logistics, inventory, and procurement signals that may reveal demand shifts or operational strain before company reports fully catch up.
Tracks structural themes such as AI adoption, electrification, reshoring, digitization, or cost deflation and shows how they may reshape coverage priorities.
Maps competitor positioning, strategic moves, pricing behavior, product differentiation, and share-shift drivers within a target market.
Tracks sector-specific KPIs such as pricing, utilization, churn, inventory, shipments, store traffic, or bookings to support ongoing coverage updates.
Explains industry structure, value-chain economics, customer bargaining power, and profit-pool dynamics across a covered sector.
Designs repeatable stock-screening logic for idea discovery, clarifies inclusion and exclusion rules, and explains why a screen does or does not fit the stated research objective.
Summarizes alternative-data inputs such as web traffic, app trends, card data, satellite indicators, or hiring signals while highlighting methodological limits.
Maps revenue engines, unit economics, customer concentration, pricing power, and cost structure to clarify how the business actually creates value.
Examines reinvestment choices, buybacks, dividends, M&A discipline, and balance-sheet deployment to judge whether management capital allocation supports the thesis.
Maintains a team-level catalyst calendar that consolidates company events, macro releases, industry checkpoints, and internal memo deadlines.
Maps plausible catalyst sequences and failure paths so teams can see which evidence checkpoints matter before a thesis can strengthen or break.
Designs disciplined channel-check plans by identifying what to ask, whom to ask, and what bias controls are needed before anecdotal evidence is used.
Builds a fundamentals-first company view covering business model, management quality, competitive position, operating drivers, and catalyst context for listed companies.
Maps competitor positioning, strategic moves, pricing behavior, product differentiation, and share-shift drivers within a target market.
Tracks consensus revenue, margin, EPS, and free-cash-flow expectation changes across covered names so estimate drift is visible before major events.
Tracks rates, credit spreads, commodities, FX, and volatility signals that may explain or challenge single-name equity moves and sector narratives.
Checks research datasets for stale points, inconsistent definitions, broken links, unit mismatches, and unsupported assumptions before they enter downstream memos.
Tracks open diligence items, missing evidence, source freshness, and owner handoffs so no thesis depends on unexamined assumptions.
Pressure-tests what a thesis looks like under revenue misses, margin compression, slower demand, and weaker financing conditions.
Prepares earnings preview briefs with setup, key questions, consensus framing, scenario sensitivities, and what evidence would confirm or weaken the current thesis.
Summarizes post-earnings developments, isolates what changed versus expectations, and distinguishes signal from noise in immediate market reactions.
Monitors sell-side estimate changes, target-reset patterns, and assumption revisions after major developments to show how the expectation set is moving.
Maintains a forward-looking calendar of earnings dates, investor events, product launches, data releases, lockups, and other thesis-relevant milestones.
Maintains an explicit ledger of unresolved questions, weak evidence chains, stale sources, and high-impact unknowns across active coverage.
Maps equity ideas to style, quality, momentum, leverage, and cyclicality exposures so teams understand what risk buckets a name may load into.
Assesses income statement, balance sheet, and cash flow quality to surface trend direction, capital intensity, balance-sheet resilience, and accounting red flags.
Tracks guidance revisions, management tone shifts, and estimate-reset implications to help teams understand whether thesis assumptions still hold.
Structures ambiguous stock ideas into a clean research brief with scope, horizon, thesis questions, and evidence needs for public-equity coverage.
Ranks candidate research ideas by evidence strength, novelty, catalyst timing, downside ambiguity, and expected follow-up effort without implying execution advice.
Tracks sector-specific KPIs such as pricing, utilization, churn, inventory, shipments, store traffic, or bookings to support ongoing coverage updates.
Evaluates refinancing needs, covenant headroom, liquidity runway, and balance-sheet flexibility under adverse operating and market conditions.
Tracks macro releases, rate expectations, liquidity backdrop, and broad market regime shifts that may change the interpretation of single-name equity research.
Reviews leadership credibility, capital-markets communication, incentive alignment, governance posture, and execution consistency as research inputs.
Creates structured market maps that cluster candidate names by subsector, business model, maturity, and key operating drivers before deeper diligence begins.
Normalizes research memos into a consistent structure covering thesis, evidence, assumptions, risks, catalysts, and source timestamps.
Maintains an event-driven watch across company news, policy headlines, litigation developments, financing moves, and sentiment inflections relevant to covered names.
Builds clean peer sets and compares operating model, growth quality, margins, capital efficiency, and valuation context across comparable public companies.
Watches policy proposals, rule changes, antitrust developments, reimbursement shifts, and regulatory enforcement trends that can alter sector-level research assumptions.
Reviews a research watchlist or draft portfolio for concentration, overlap, factor clustering, and common thesis dependencies without making suitability or allocation decisions.
Builds discovery screens around return quality, balance-sheet discipline, margin durability, and earnings consistency to surface higher-quality candidate names.
Compares valuation spreads, implied expectations, and business-quality differences across peer sets to identify where market pricing appears comparatively stretched or discounted.
Organizes coverage queues, standardizes memo status, tracks follow-up gaps, and keeps a research team aligned on what is ready, stale, blocked, or awaiting human review.
Converts scattered analyst notes into clean handoff memos with scope, current view, unresolved questions, and source references for the next reviewer.
Stress-tests an equity thesis for business, financial, market, liquidity, governance, and regulatory downside before any recommendation is formed.
Designs repeatable stock-screening logic for idea discovery, clarifies inclusion and exclusion rules, and explains why a screen does or does not fit the stated research objective.
Monitors sector leadership, breadth, relative strength, and narrative rotation to show how top-down flows may affect research coverage priorities.
Explains industry structure, value-chain economics, customer bargaining power, and profit-pool dynamics across a covered sector.
Builds sensitivity tables linking valuation, earnings power, and cash generation to a small set of clearly defined driver changes.
Tracks positioning extremes, narrative crowding, short-interest context, and sentiment swings that may change how price reactions should be interpreted.
Monitors supplier, logistics, inventory, and procurement signals that may reveal demand shifts or operational strain before company reports fully catch up.
Tracks structural themes such as AI adoption, electrification, reshoring, digitization, or cost deflation and shows how they may reshape coverage priorities.
Maintains an active thesis ledger showing what has been confirmed, weakened, deferred, or invalidated as new evidence arrives.
Builds base, upside, and downside operating scenarios that show which assumptions matter most and where evidence gaps still dominate the range of outcomes.
Extracts management tone shifts, disclosure changes, and decision-useful evidence from transcripts, fireside chats, and conference appearances.
Frames defensible valuation ranges using multiples, simplified DCF logic, and scenario analysis without overstating precision.
Separates consensus assumptions from differentiated hypotheses so teams can see where the market narrative may diverge from emerging evidence.
Maintains structured watchlists by theme, catalyst window, diligence stage, and thesis status so research teams can review coverage systematically.
Monitors earnings dates, guidance updates, news catalysts, and thesis-drift signals for names under active research coverage.