[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-research-note":3,"mdc--f0wwkm-key":36,"related-org-openai-research-note":2250,"related-repo-openai-research-note":2455},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"research-note","generate professional research notes","Generate a professional Word document research note",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"openai","OpenAI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fopenai.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Writing","writing","tag",{"name":17,"slug":18,"type":15},"Finance","finance",{"name":20,"slug":21,"type":15},"Documents","documents",{"name":23,"slug":24,"type":15},"Word","word",3992,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins","2026-06-30T19:00:57.102",null,465,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"OpenAI Plugins","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins\u002Ftree\u002FHEAD\u002Fplugins\u002Fdaloopa\u002Fskills\u002Fresearch-note","---\nname: research-note\ndescription: Generate a professional Word document research note\n---\n\nGenerate a professional research note (HTML report) for the company specified by the user named in the user's request. If no ticker or company is provided, ask for one before proceeding.\n\n**Before starting, read `..\u002Fdata-access.md` for data access methods and `..\u002Fdesign-system.md` for formatting conventions.** Follow the data access detection logic and design system throughout this skill.\n\nThis is an orchestrator skill that gathers comprehensive data, then renders a styled HTML report using the HTML Report Template from `..\u002Fdesign-system.md` (full CSS inlined, zero dependencies).\n\n## Phase A — Company Setup\nLook up the company by ticker using `discover_companies`. Capture:\n- `company_id`\n- `latest_calendar_quarter` — anchor for all period calculations (see `..\u002Fdata-access.md` Section 1.5)\n- `latest_fiscal_quarter`\n- Firm name for report attribution (default: \"Daloopa\") — see `..\u002Fdata-access.md` Section 4.5\n\nGet current stock price, market cap, shares outstanding, beta, and trading multiples for {TICKER} using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (see `..\u002Fdata-access.md` Section 2 for how to source market data).\n\nInitialize context: `context = {company_name, ticker, date, price, market_cap, firm_name, ...}`\n\n## Phase B — Core Financials + Cost Structure\nCalculate 8 quarters backward from `latest_calendar_quarter`. Pull Income Statement metrics:\n- Revenue, Gross Profit, Operating Income, Net Income, Diluted EPS\n- EBITDA (compute as Op Income + D&A if not direct, label \"(calc.)\")\n- Operating Expenses (SG&A, R&D where available)\n\nPull Cash Flow & Balance Sheet:\n- Operating Cash Flow, CapEx, Free Cash Flow (OCF - CapEx, label \"(calc.)\")\n- Cash, Total Debt, Net Debt\n- D&A\n\n**For every value returned by `get_company_fundamentals`, record its `fundamental_id` (the `id` field).** Store each data point as `{value, fundamental_id}` so citations can be rendered in the final document.\n\nCompute margins and YoY growth rates for each quarter. Build `context.financials` with tables. Every Daloopa-sourced number must include its citation link: `[$X.XX million](https:\u002F\u002Fdaloopa.com\u002Fsrc\u002F{fundamental_id})`.\n\n### Cost Structure & Margin Analysis\nAfter the core financial pull, add:\n\n- **COGS driver identification**: Search for cost-related series (\"cost of goods\", \"materials\", \"manufacturing\", \"input cost\"). Identify 3-5 biggest cost line items and their trends over 8Q.\n- **OpEx breakdown**: Pull R&D and SG&A separately. Compute R&D % of revenue and SG&A % of revenue trends over 8Q.\n- **Margin driver analysis**: For each major margin (gross, operating, net), identify what's driving expansion or compression — pricing power, cost leverage, mix shift, or one-time items.\n\nNew context keys:\n- `cost_margin_analysis` (string) — narrative explaining what's driving margins, with Daloopa citations\n- `opex_breakdown_table` (dynamic table) — [{metric, Q1, Q2, ...}] rows for R&D, SG&A, Other OpEx, each with absolute values and % of revenue sub-rows\n\n## Phase C — KPIs, Segments & Industry Deep Dive\nThink about what KPIs matter most for THIS company's business model. Search for:\n- Company-specific operating KPIs (subscribers, units, ARPU, retention, etc.)\n- Segment revenue breakdown\n- Geographic revenue breakdown\n- Share count and buyback activity\n\nPull the same 8 quarters (from `latest_calendar_quarter`). Build `context.kpis` and `context.segments`.\n\n### Industry-Specific Deep Dive\nAfter the KPI\u002Fsegment pull, determine the company's sector and apply the relevant analysis template:\n\n- **Manufacturing\u002FIndustrial**: Bookings & backlog, book-to-bill ratio, pipeline by geography, capacity utilization\n- **SaaS\u002FTechnology**: ARR\u002FMRR trajectory, net retention rate, customer cohort analysis, RPO\u002Fdeferred revenue trends\n- **Retail\u002FConsumer**: Same-store sales, store count trajectory, traffic vs ticket decomposition, inventory health\n- **Financials\u002FBanks**: NIM trajectory, provision trends, loan growth by category, capital ratios (CET1, TCE)\n- **Healthcare\u002FPharma**: Pipeline summary (drug, indication, phase, milestone), product revenue breakdown, patent cliff timeline\n- **Energy**: Production volumes, realized pricing vs benchmark, proved reserves, breakeven analysis\n\nSearch for relevant series using `discover_company_series` with sector-appropriate keywords. Pull available data and build the narrative.\n\nNew context key:\n- `industry_deep_dive` (string) — sector-specific analysis narrative with Daloopa citations, organized by the relevant template above\n\n## Phase D — Guidance Track Record (follows \u002Fguidance-tracker methodology)\nSearch for guidance series (\"guidance\", \"outlook\", \"forecast\", \"estimate\", \"target\").\nPull guidance and corresponding actuals. Apply +1 quarter offset rule.\nCompute beat\u002Fmiss rates and patterns.\nBuild `context.guidance` (set `context.has_guidance = true\u002Ffalse`).\n\n## Phase E — What You Need to Believe (replaces Scenario Analysis)\nUsing the financial baseline from Phase B:\n- Compute trailing 4Q totals for key metrics (revenue, EBITDA, EPS, FCF)\n- Analyze segment-level trends and inflections\n\nBuild **falsifiable bull\u002Fbear beliefs** instead of probability-weighted scenarios:\n\n### Bull Beliefs (To Go Long)\nWrite 4-6 numbered beliefs, each with:\n- One **bold statement** (the belief itself)\n- 2-3 sentences of **evidence** with Daloopa citations supporting why this could be true\n- Each belief must be **falsifiable** — testable with observable data within 6 months\n\nExample format: \"1. **Revenue growth re-accelerates to 15%+ as AI monetization scales.** Cloud segment grew [$X.Xbn](link) last quarter, up X% YoY, with management noting...\"\n\n### Bear Beliefs (To Go Short)\nSame format — 4-6 numbered falsifiable beliefs with evidence for the downside case.\n\n### Valuation Math\nFor each side:\n- Bull target: forward multiple × forward earnings estimate = price target. Show the math.\n- Bear target: same structure with bear-case multiple and earnings.\n\n### Risk\u002FReward Assessment\n- Compare bull upside % vs bear downside % from current price\n- If asymmetry is significant (e.g., 30% upside vs 40% downside), flag it explicitly\n- State which side has the better risk\u002Freward and why\n\nNew context keys:\n- `bull_beliefs` (string) — numbered falsifiable beliefs with evidence\n- `bear_beliefs` (string) — numbered falsifiable beliefs with evidence\n- `bull_target` (string) — price target + valuation math\n- `bear_target` (string) — price target + valuation math\n- `risk_reward_assessment` (string) — asymmetry analysis\n\n## Phase F — Capital Allocation (follows \u002Fcapital-allocation methodology)\nPull buyback, dividend, share count, FCF data.\nCompute shareholder yield, FCF payout ratio, net leverage.\nBuild `context.capital_allocation`.\n\n## Phase G — Valuation (follows \u002Fdcf + \u002Fcomps methodology)\n\n**DCF:**\n- Get risk-free rate using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (see `..\u002Fdata-access.md` Section 2)\n- Calculate WACC using CAPM\n- Project FCF 5 years manually (describe methodology inline and perform calculations directly)\n- Compute terminal value, implied share price, sensitivity table\n- Build `context.dcf` (set `context.has_dcf = true`)\n\n**Comps:**\n- Identify 5-8 peers\n- Get peer trading multiples using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (see `..\u002Fdata-access.md` Section 2)\n- If consensus forward estimates are available (`..\u002Fdata-access.md` Section 3), include forward multiples\n- Compute implied valuation range from peer multiples\n- Build `context.comps` (set `context.has_comps = true`)\n\n## Phase H — Qualitative Research + News & Catalysts\n### SEC Filing Research\nSearch SEC filings across multiple queries:\n- \"risk\" \u002F \"uncertainty\" \u002F \"challenge\" for risk factors\n- \"growth\" \u002F \"opportunity\" \u002F \"expansion\" for growth drivers\n- \"competition\" \u002F \"market share\" for competitive dynamics\n- \"outlook\" \u002F \"guidance\" for management's forward view\n- Company-specific strategic topics (e.g., \"AI\", \"cloud\", etc.)\n\nExtract and organize into:\n- `context.risks` — ranked list of risks with impact\u002Fprobability\n- `context.investment_thesis` — variant perception, thesis pillars, catalysts\n- `context.company_description` — 2-3 sentence business description\n\n### News & Catalysts via WebSearch\nRun 4 WebSearch queries to gather recent external context:\n1. `\"{TICKER} {company_name} news {year}\"` — recent headlines and developments\n2. `\"{TICKER} analyst upgrade downgrade price target\"` — sell-side sentiment shifts\n3. `\"{TICKER} catalysts risks\"` — forward-looking events and risk factors\n4. `\"{company_name} industry outlook {sector}\"` — macro and industry trends\n\nOrganize results into three new context keys:\n\n- `news_timeline` (string) — 6-10 key events from the last 6-12 months in reverse chronological order. Each event: date, headline, 1-sentence impact, sentiment tag (Positive \u002F Negative \u002F Mixed \u002F Upcoming). Format as a numbered list.\n\n- `forward_catalysts` (string) — Organized by timeframe:\n  - **Near-term (0-3 months, HIGH priority)**: earnings dates, product launches, regulatory decisions\n  - **Medium-term (3-12 months, MEDIUM priority)**: strategic milestones, contract renewals, industry events\n  - **Long-term (1-3 years, LOW priority)**: secular trends, market expansion, competitive dynamics\n\n- `policy_backdrop` (string) — Macro\u002Fregulatory context affecting the company. Tariffs, regulation, interest rates, sector-specific policy. Leave empty string if not material.\n\n## Phase I — Charts\nPresent all chart data in well-formatted tables. No chart generation needed.\n\n## Phase J — Synthesis + Tensions + Monitoring\nThis is the most judgment-intensive step. Be honest and critical — the reader is a professional investor who needs your real assessment, not a balanced summary.\n\n### Core Synthesis\nWrite:\n- **Executive Summary**: 3-4 sentence TL;DR covering current state, key thesis, valuation view. Include a clear directional view — is this stock attractive, fairly valued, or overvalued at the current price?\n- **Variant Perception**: What does the market think vs what do you see in the data? Where is the consensus wrong? If you agree with consensus, say that too — but explain what could change.\n- **Key Findings**: Top 3-5 most notable data points or trends — prioritize what changes the investment thesis, not just what's interesting\n- **Red Flags & Concerns**: Any quality-of-earnings issues, sustainability questions, or risks the market may be underpricing\n- Build `context.executive_summary`, `context.variant_perception`\n\n### Five Key Tensions\nIdentify the 5 most critical bull\u002Fbear debates for this stock. Each tension is a single line that frames both sides. Alternate between bullish-leaning and bearish-leaning tensions. Every tension must reference a specific data point from the analysis.\n\nFormat as a numbered list:\n1. \"[Bullish factor] vs [Bearish factor]\" — cite the specific metric\n2. \"[Bearish factor] vs [Bullish factor]\" — cite the specific metric\n...etc.\n\nBuild `context.five_key_tensions` (string).\n\n### Monitoring Framework\nBuild two monitoring lists for ongoing tracking:\n\n**Quantitative Monitors** — 5-7 specific metrics with explicit thresholds:\n- Format: \"Metric: current value → bull threshold \u002F bear threshold\"\n- Example: \"Gross Margin: 45.2% → above 46% confirms pricing power \u002F below 43% signals cost pressure\"\n\n**Qualitative Monitors** — 5-7 factors to watch:\n- Management tone shifts on earnings calls\n- Competitive dynamics (new entrants, pricing pressure)\n- Regulatory developments\n- Customer concentration changes\n- Capital allocation pivots\n\nBuild `context.monitoring_quantitative` and `context.monitoring_qualitative` (strings, numbered lists).\n\n### Structured Tables\nAlso build structured tables for the template:\n- `context.key_metrics_table` — [{metric, value, vs_prior}] for the exec summary table\n- `context.financials_table` — [{metric, q1, q2, ...}] for the financial analysis section\n- `context.segments_table`, `context.geo_table`, `context.shares_outstanding_table`\n- `context.opex_breakdown_table` — [{metric, q1, q2, ...}] for R&D, SG&A, % of revenue rows\n- `context.guidance_table`, `context.comps_table`, etc.\n\n## Phase K — Render HTML Report\n\nUsing the HTML Report Template from `..\u002Fdesign-system.md`, generate a styled HTML report with full CSS inlined. The report should include:\n\n**Header Section:**\n- Company name and ticker\n- Report date and firm attribution\n- Five Key Tensions (numbered list)\n\n**Section 1: Executive Summary**\n- Key metrics table\n- Executive summary narrative\n- Variant perception\n\n**Section 2: Company Overview**\n- Business description\n- Investment thesis\n\n**Section 3: Recent News & Catalysts**\n- News timeline\n- Forward catalysts\n- Policy backdrop\n\n**Section 4: Financial Analysis**\n- Financials table (8 quarters)\n- Cost structure & margin analysis\n- OpEx breakdown table\n- Segment and geographic tables\n- Share count table\n\n**Section 5: Industry-Specific Analysis**\n- Industry deep dive narrative\n\n**Section 6: Guidance Track Record**\n- Guidance table and beat\u002Fmiss analysis (if available)\n\n**Section 7: What You Need to Believe**\n- Bull beliefs with valuation target\n- Bear beliefs with valuation target\n- Risk\u002Freward assessment\n\n**Section 8: Catalysts**\n- Forward catalysts\n- Policy backdrop\n\n**Section 9: Capital Allocation**\n- Capital allocation commentary\n\n**Section 10: Valuation**\n- DCF summary and sensitivity (if available)\n- Comps commentary (if available)\n\n**Section 11: Risks**\n- Risks summary\n\n**Section 12: Monitoring Framework**\n- Quantitative monitors\n- Qualitative monitors\n\n**Appendix:**\n- Additional context or data\n\n### Context Key Checklist\nVerify these keys exist before rendering (set empty string if data unavailable):\n\n**Cover & Summary:**\n`company_name`, `ticker`, `date`, `price`, `market_cap`, `five_key_tensions`, `executive_summary`, `key_metrics_table`\n\n**Thesis & Overview:**\n`investment_thesis`, `variant_perception`, `company_description`\n\n**News:**\n`news_timeline`\n\n**Financials:**\n`financials_table`, `cost_margin_analysis`, `opex_breakdown_table`, `segments_table`, `geo_table`, `shares_outstanding_table`\n\n**Industry:**\n`industry_deep_dive`\n\n**Guidance:**\n`has_guidance`, `guidance_track_record`\n\n**What You Need to Believe:**\n`bull_beliefs`, `bull_target`, `bear_beliefs`, `bear_target`, `risk_reward_assessment`\n\n**Catalysts:**\n`forward_catalysts`, `policy_backdrop`\n\n**Capital Allocation:**\n`capital_allocation_commentary`\n\n**Valuation:**\n`has_dcf`, `dcf_summary`, `has_comps`, `comps_commentary`\n\n**Risks:**\n`risks_summary`\n\n**Monitoring:**\n`monitoring_quantitative`, `monitoring_qualitative`\n\n**Appendix:**\n`appendix_content`\n\n## Output\nSave the styled HTML report as a local file and summarize the output. Tell the user:\n- A 3-4 sentence executive summary of the research note\n- Key findings and valuation range\n- Tell them where the HTML file was saved and that it can be opened in a browser for full formatting\n\n**Citation enforcement:** Every financial figure from Daloopa in the HTML report must use citation format: `[$X.XX million](https:\u002F\u002Fdaloopa.com\u002Fsrc\u002F{fundamental_id})`. If a number came from `get_company_fundamentals`, it must have a citation link. No exceptions.\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,49,77,89,96,109,162,174,185,191,203,221,226,244,286,307,314,319,352,357,390,396,401,424,451,457,462,525,538,543,557,563,584,590,595,608,620,626,631,670,691,697,702,708,713,726,732,750,754,810,816,828,834,842,891,899,953,959,965,970,998,1003,1039,1045,1050,1098,1103,1172,1178,1183,1189,1194,1200,1205,1266,1272,1277,1282,1319,1331,1337,1342,1352,1365,1375,1403,1422,1428,1433,1528,1534,1546,1554,1572,1580,1598,1606,1619,1627,1645,1653,1681,1689,1697,1705,1713,1721,1739,1747,1758,1766,1774,1782,1795,1803,1811,1819,1832,1840,1848,1854,1859,1922,1950,1963,2010,2023,2044,2081,2100,2114,2149,2163,2184,2197,2203,2208,2226],{"type":42,"tag":43,"props":44,"children":45},"element","p",{},[46],{"type":47,"value":48},"text","Generate a professional research note (HTML report) for the company specified by the user named in the user's request. If no ticker or company is provided, ask for one before proceeding.",{"type":42,"tag":43,"props":50,"children":51},{},[52,75],{"type":42,"tag":53,"props":54,"children":55},"strong",{},[56,58,65,67,73],{"type":47,"value":57},"Before starting, read ",{"type":42,"tag":59,"props":60,"children":62},"code",{"className":61},[],[63],{"type":47,"value":64},"..\u002Fdata-access.md",{"type":47,"value":66}," for data access methods and ",{"type":42,"tag":59,"props":68,"children":70},{"className":69},[],[71],{"type":47,"value":72},"..\u002Fdesign-system.md",{"type":47,"value":74}," for formatting conventions.",{"type":47,"value":76}," Follow the data access detection logic and design system throughout this skill.",{"type":42,"tag":43,"props":78,"children":79},{},[80,82,87],{"type":47,"value":81},"This is an orchestrator skill that gathers comprehensive data, then renders a styled HTML report using the HTML Report Template from ",{"type":42,"tag":59,"props":83,"children":85},{"className":84},[],[86],{"type":47,"value":72},{"type":47,"value":88}," (full CSS inlined, zero dependencies).",{"type":42,"tag":90,"props":91,"children":93},"h2",{"id":92},"phase-a-company-setup",[94],{"type":47,"value":95},"Phase A — Company Setup",{"type":42,"tag":43,"props":97,"children":98},{},[99,101,107],{"type":47,"value":100},"Look up the company by ticker using ",{"type":42,"tag":59,"props":102,"children":104},{"className":103},[],[105],{"type":47,"value":106},"discover_companies",{"type":47,"value":108},". Capture:",{"type":42,"tag":110,"props":111,"children":112},"ul",{},[113,123,141,150],{"type":42,"tag":114,"props":115,"children":116},"li",{},[117],{"type":42,"tag":59,"props":118,"children":120},{"className":119},[],[121],{"type":47,"value":122},"company_id",{"type":42,"tag":114,"props":124,"children":125},{},[126,132,134,139],{"type":42,"tag":59,"props":127,"children":129},{"className":128},[],[130],{"type":47,"value":131},"latest_calendar_quarter",{"type":47,"value":133}," — anchor for all period calculations (see ",{"type":42,"tag":59,"props":135,"children":137},{"className":136},[],[138],{"type":47,"value":64},{"type":47,"value":140}," Section 1.5)",{"type":42,"tag":114,"props":142,"children":143},{},[144],{"type":42,"tag":59,"props":145,"children":147},{"className":146},[],[148],{"type":47,"value":149},"latest_fiscal_quarter",{"type":42,"tag":114,"props":151,"children":152},{},[153,155,160],{"type":47,"value":154},"Firm name for report attribution (default: \"Daloopa\") — see ",{"type":42,"tag":59,"props":156,"children":158},{"className":157},[],[159],{"type":47,"value":64},{"type":47,"value":161}," Section 4.5",{"type":42,"tag":43,"props":163,"children":164},{},[165,167,172],{"type":47,"value":166},"Get current stock price, market cap, shares outstanding, beta, and trading multiples for {TICKER} using the 3-step resolution: (1) MCP market data tools if available, (2) web search, (3) sensible defaults (see ",{"type":42,"tag":59,"props":168,"children":170},{"className":169},[],[171],{"type":47,"value":64},{"type":47,"value":173}," Section 2 for how to source market data).",{"type":42,"tag":43,"props":175,"children":176},{},[177,179],{"type":47,"value":178},"Initialize context: ",{"type":42,"tag":59,"props":180,"children":182},{"className":181},[],[183],{"type":47,"value":184},"context = {company_name, ticker, date, price, market_cap, firm_name, ...}",{"type":42,"tag":90,"props":186,"children":188},{"id":187},"phase-b-core-financials-cost-structure",[189],{"type":47,"value":190},"Phase B — Core Financials + Cost Structure",{"type":42,"tag":43,"props":192,"children":193},{},[194,196,201],{"type":47,"value":195},"Calculate 8 quarters backward from ",{"type":42,"tag":59,"props":197,"children":199},{"className":198},[],[200],{"type":47,"value":131},{"type":47,"value":202},". Pull Income Statement metrics:",{"type":42,"tag":110,"props":204,"children":205},{},[206,211,216],{"type":42,"tag":114,"props":207,"children":208},{},[209],{"type":47,"value":210},"Revenue, Gross Profit, Operating Income, Net Income, Diluted EPS",{"type":42,"tag":114,"props":212,"children":213},{},[214],{"type":47,"value":215},"EBITDA (compute as Op Income + D&A if not direct, label \"(calc.)\")",{"type":42,"tag":114,"props":217,"children":218},{},[219],{"type":47,"value":220},"Operating Expenses (SG&A, R&D where available)",{"type":42,"tag":43,"props":222,"children":223},{},[224],{"type":47,"value":225},"Pull Cash Flow & Balance Sheet:",{"type":42,"tag":110,"props":227,"children":228},{},[229,234,239],{"type":42,"tag":114,"props":230,"children":231},{},[232],{"type":47,"value":233},"Operating Cash Flow, CapEx, Free Cash Flow (OCF - CapEx, label \"(calc.)\")",{"type":42,"tag":114,"props":235,"children":236},{},[237],{"type":47,"value":238},"Cash, Total Debt, Net Debt",{"type":42,"tag":114,"props":240,"children":241},{},[242],{"type":47,"value":243},"D&A",{"type":42,"tag":43,"props":245,"children":246},{},[247,276,278,284],{"type":42,"tag":53,"props":248,"children":249},{},[250,252,258,260,266,268,274],{"type":47,"value":251},"For every value returned by ",{"type":42,"tag":59,"props":253,"children":255},{"className":254},[],[256],{"type":47,"value":257},"get_company_fundamentals",{"type":47,"value":259},", record its ",{"type":42,"tag":59,"props":261,"children":263},{"className":262},[],[264],{"type":47,"value":265},"fundamental_id",{"type":47,"value":267}," (the ",{"type":42,"tag":59,"props":269,"children":271},{"className":270},[],[272],{"type":47,"value":273},"id",{"type":47,"value":275}," field).",{"type":47,"value":277}," Store each data point as ",{"type":42,"tag":59,"props":279,"children":281},{"className":280},[],[282],{"type":47,"value":283},"{value, fundamental_id}",{"type":47,"value":285}," so citations can be rendered in the final document.",{"type":42,"tag":43,"props":287,"children":288},{},[289,291,297,299,305],{"type":47,"value":290},"Compute margins and YoY growth rates for each quarter. Build ",{"type":42,"tag":59,"props":292,"children":294},{"className":293},[],[295],{"type":47,"value":296},"context.financials",{"type":47,"value":298}," with tables. Every Daloopa-sourced number must include its citation link: ",{"type":42,"tag":59,"props":300,"children":302},{"className":301},[],[303],{"type":47,"value":304},"[$X.XX million](https:\u002F\u002Fdaloopa.com\u002Fsrc\u002F{fundamental_id})",{"type":47,"value":306},".",{"type":42,"tag":308,"props":309,"children":311},"h3",{"id":310},"cost-structure-margin-analysis",[312],{"type":47,"value":313},"Cost Structure & Margin Analysis",{"type":42,"tag":43,"props":315,"children":316},{},[317],{"type":47,"value":318},"After the core financial pull, add:",{"type":42,"tag":110,"props":320,"children":321},{},[322,332,342],{"type":42,"tag":114,"props":323,"children":324},{},[325,330],{"type":42,"tag":53,"props":326,"children":327},{},[328],{"type":47,"value":329},"COGS driver identification",{"type":47,"value":331},": Search for cost-related series (\"cost of goods\", \"materials\", \"manufacturing\", \"input cost\"). Identify 3-5 biggest cost line items and their trends over 8Q.",{"type":42,"tag":114,"props":333,"children":334},{},[335,340],{"type":42,"tag":53,"props":336,"children":337},{},[338],{"type":47,"value":339},"OpEx breakdown",{"type":47,"value":341},": Pull R&D and SG&A separately. 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Believe:",{"type":42,"tag":59,"props":2053,"children":2055},{"className":2054},[],[2056],{"type":47,"value":765},{"type":47,"value":1259},{"type":42,"tag":59,"props":2059,"children":2061},{"className":2060},[],[2062],{"type":47,"value":786},{"type":47,"value":1259},{"type":42,"tag":59,"props":2065,"children":2067},{"className":2066},[],[2068],{"type":47,"value":776},{"type":47,"value":1259},{"type":42,"tag":59,"props":2071,"children":2073},{"className":2072},[],[2074],{"type":47,"value":797},{"type":47,"value":1259},{"type":42,"tag":59,"props":2077,"children":2079},{"className":2078},[],[2080],{"type":47,"value":807},{"type":42,"tag":43,"props":2082,"children":2083},{},[2084,2089,2094,2095],{"type":42,"tag":53,"props":2085,"children":2086},{},[2087],{"type":47,"value":2088},"Catalysts:",{"type":42,"tag":59,"props":2090,"children":2092},{"className":2091},[],[2093],{"type":47,"value":1125},{"type":47,"value":1259},{"type":42,"tag":59,"props":2096,"children":2098},{"className":2097},[],[2099],{"type":47,"value":1169},{"type":42,"tag":43,"props":2101,"children":2102},{},[2103,2108],{"type":42,"tag":53,"props":2104,"children":2105},{},[2106],{"type":47,"value":2107},"Capital Allocation:",{"type":42,"tag":59,"props":2109,"children":2111},{"className":2110},[],[2112],{"type":47,"value":2113},"capital_allocation_commentary",{"type":42,"tag":43,"props":2115,"children":2116},{},[2117,2122,2128,2129,2135,2136,2142,2143],{"type":42,"tag":53,"props":2118,"children":2119},{},[2120],{"type":47,"value":2121},"Valuation:",{"type":42,"tag":59,"props":2123,"children":2125},{"className":2124},[],[2126],{"type":47,"value":2127},"has_dcf",{"type":47,"value":1259},{"type":42,"tag":59,"props":2130,"children":2132},{"className":2131},[],[2133],{"type":47,"value":2134},"dcf_summary",{"type":47,"value":1259},{"type":42,"tag":59,"props":2137,"children":2139},{"className":2138},[],[2140],{"type":47,"value":2141},"has_comps",{"type":47,"value":1259},{"type":42,"tag":59,"props":2144,"children":2146},{"className":2145},[],[2147],{"type":47,"value":2148},"comps_commentary",{"type":42,"tag":43,"props":2150,"children":2151},{},[2152,2157],{"type":42,"tag":53,"props":2153,"children":2154},{},[2155],{"type":47,"value":2156},"Risks:",{"type":42,"tag":59,"props":2158,"children":2160},{"className":2159},[],[2161],{"type":47,"value":2162},"risks_summary",{"type":42,"tag":43,"props":2164,"children":2165},{},[2166,2171,2177,2178],{"type":42,"tag":53,"props":2167,"children":2168},{},[2169],{"type":47,"value":2170},"Monitoring:",{"type":42,"tag":59,"props":2172,"children":2174},{"className":2173},[],[2175],{"type":47,"value":2176},"monitoring_quantitative",{"type":47,"value":1259},{"type":42,"tag":59,"props":2179,"children":2181},{"className":2180},[],[2182],{"type":47,"value":2183},"monitoring_qualitative",{"type":42,"tag":43,"props":2185,"children":2186},{},[2187,2191],{"type":42,"tag":53,"props":2188,"children":2189},{},[2190],{"type":47,"value":1839},{"type":42,"tag":59,"props":2192,"children":2194},{"className":2193},[],[2195],{"type":47,"value":2196},"appendix_content",{"type":42,"tag":90,"props":2198,"children":2200},{"id":2199},"output",[2201],{"type":47,"value":2202},"Output",{"type":42,"tag":43,"props":2204,"children":2205},{},[2206],{"type":47,"value":2207},"Save 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Tell the user:",{"type":42,"tag":110,"props":2209,"children":2210},{},[2211,2216,2221],{"type":42,"tag":114,"props":2212,"children":2213},{},[2214],{"type":47,"value":2215},"A 3-4 sentence executive summary of the research note",{"type":42,"tag":114,"props":2217,"children":2218},{},[2219],{"type":47,"value":2220},"Key findings and valuation range",{"type":42,"tag":114,"props":2222,"children":2223},{},[2224],{"type":47,"value":2225},"Tell them where the HTML file was saved and that it can be opened in a browser for full formatting",{"type":42,"tag":43,"props":2227,"children":2228},{},[2229,2234,2236,2241,2243,2248],{"type":42,"tag":53,"props":2230,"children":2231},{},[2232],{"type":47,"value":2233},"Citation enforcement:",{"type":47,"value":2235}," Every financial figure from Daloopa in the HTML report must use citation format: ",{"type":42,"tag":59,"props":2237,"children":2239},{"className":2238},[],[2240],{"type":47,"value":304},{"type":47,"value":2242},". If a number came from ",{"type":42,"tag":59,"props":2244,"children":2246},{"className":2245},[],[2247],{"type":47,"value":257},{"type":47,"value":2249},", it must have a citation link. No exceptions.",{"items":2251,"total":2454},[2252,2271,2294,2311,2327,2346,2365,2381,2397,2411,2423,2438],{"slug":2253,"name":2253,"fn":2254,"description":2255,"org":2256,"tags":2257,"stars":2268,"repoUrl":2269,"updatedAt":2270},"prior-auth-packet-builder","build healthcare prior authorization packets","Build a concise prior authorization packet from local case files and payer policy docs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2258,2259,2262,2265],{"name":20,"slug":21,"type":15},{"name":2260,"slug":2261,"type":15},"Healthcare","healthcare",{"name":2263,"slug":2264,"type":15},"Insurance","insurance",{"name":2266,"slug":2267,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":2272,"name":2272,"fn":2273,"description":2274,"org":2275,"tags":2276,"stars":2291,"repoUrl":2292,"updatedAt":2293},"aspnet-core","build ASP.NET Core web applications","Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2277,2280,2282,2285,2288],{"name":2278,"slug":2279,"type":15},".NET","dotnet",{"name":2281,"slug":2272,"type":15},"ASP.NET Core",{"name":2283,"slug":2284,"type":15},"Blazor","blazor",{"name":2286,"slug":2287,"type":15},"C#","csharp",{"name":2289,"slug":2290,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":2295,"name":2295,"fn":2296,"description":2297,"org":2298,"tags":2299,"stars":2291,"repoUrl":2292,"updatedAt":2310},"chatgpt-apps","build ChatGPT Apps SDK applications","Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2300,2303,2306,2309],{"name":2301,"slug":2302,"type":15},"Apps SDK","apps-sdk",{"name":2304,"slug":2305,"type":15},"ChatGPT","chatgpt",{"name":2307,"slug":2308,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":2312,"name":2312,"fn":2313,"description":2314,"org":2315,"tags":2316,"stars":2291,"repoUrl":2292,"updatedAt":2326},"cli-creator","build CLIs from API docs","Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read\u002Fwrite commands, return stable JSON, manage auth, and pair with a companion skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2317,2320,2323],{"name":2318,"slug":2319,"type":15},"API Development","api-development",{"name":2321,"slug":2322,"type":15},"CLI","cli",{"name":2324,"slug":2325,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":2328,"name":2328,"fn":2329,"description":2330,"org":2331,"tags":2332,"stars":2291,"repoUrl":2292,"updatedAt":2345},"cloudflare-deploy","deploy projects to Cloudflare","Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2333,2336,2339,2342],{"name":2334,"slug":2335,"type":15},"Cloudflare","cloudflare",{"name":2337,"slug":2338,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":2340,"slug":2341,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":2343,"slug":2344,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":2347,"name":2347,"fn":2348,"description":2349,"org":2350,"tags":2351,"stars":2291,"repoUrl":2292,"updatedAt":2364},"define-goal","define and set measurable project goals","Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2352,2355,2358,2361],{"name":2353,"slug":2354,"type":15},"Productivity","productivity",{"name":2356,"slug":2357,"type":15},"Project Management","project-management",{"name":2359,"slug":2360,"type":15},"Strategy","strategy",{"name":2362,"slug":2363,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":2366,"name":2366,"fn":2367,"description":2368,"org":2369,"tags":2370,"stars":2291,"repoUrl":2292,"updatedAt":2380},"figma","translate Figma designs into code","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2371,2374,2376,2379],{"name":2372,"slug":2373,"type":15},"Design","design",{"name":2375,"slug":2366,"type":15},"Figma",{"name":2377,"slug":2378,"type":15},"Frontend","frontend",{"name":2307,"slug":2308,"type":15},"2026-04-12T05:06:47.939943",{"slug":2382,"name":2382,"fn":2383,"description":2384,"org":2385,"tags":2386,"stars":2291,"repoUrl":2292,"updatedAt":2396},"figma-code-connect-components","connect Figma designs to code components","Connects Figma design components to code components using Code Connect mapping tools. Use when user says \"code connect\", \"connect this component to code\", \"map this component\", \"link component to code\", \"create code connect mapping\", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2387,2388,2391,2392,2393],{"name":2372,"slug":2373,"type":15},{"name":2389,"slug":2390,"type":15},"Design System","design-system",{"name":2375,"slug":2366,"type":15},{"name":2377,"slug":2378,"type":15},{"name":2394,"slug":2395,"type":15},"UI Components","ui-components","2026-05-10T05:59:52.971881",{"slug":2398,"name":2398,"fn":2399,"description":2400,"org":2401,"tags":2402,"stars":2291,"repoUrl":2292,"updatedAt":2410},"figma-create-design-system-rules","generate design system rules from Figma","Generates custom design system rules for the user's codebase. Use when user says \"create design system rules\", \"generate rules for my project\", \"set up design rules\", \"customize design system guidelines\", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2403,2404,2405,2408,2409],{"name":2372,"slug":2373,"type":15},{"name":2389,"slug":2390,"type":15},{"name":2406,"slug":2407,"type":15},"Documentation","documentation",{"name":2375,"slug":2366,"type":15},{"name":2377,"slug":2378,"type":15},"2026-05-16T06:07:47.821474",{"slug":2412,"name":2412,"fn":2413,"description":2414,"org":2415,"tags":2416,"stars":2291,"repoUrl":2292,"updatedAt":2422},"figma-implement-design","translate Figma designs into application code","Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions \"implement design\", \"generate code\", \"implement component\", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2417,2418,2419,2420,2421],{"name":2372,"slug":2373,"type":15},{"name":2375,"slug":2366,"type":15},{"name":2377,"slug":2378,"type":15},{"name":2394,"slug":2395,"type":15},{"name":2289,"slug":2290,"type":15},"2026-05-16T06:07:40.583615",{"slug":2424,"name":2424,"fn":2425,"description":2426,"org":2427,"tags":2428,"stars":2291,"repoUrl":2292,"updatedAt":2437},"hatch-pet","create animated pets for Codex","Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2429,2432,2433,2436],{"name":2430,"slug":2431,"type":15},"Animation","animation",{"name":2324,"slug":2325,"type":15},{"name":2434,"slug":2435,"type":15},"Creative","creative",{"name":2372,"slug":2373,"type":15},"2026-05-02T05:31:48.48485",{"slug":2439,"name":2439,"fn":2440,"description":2441,"org":2442,"tags":2443,"stars":2291,"repoUrl":2292,"updatedAt":2453},"imagegen","generate and edit raster images","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2444,2445,2446,2449,2452],{"name":2434,"slug":2435,"type":15},{"name":2372,"slug":2373,"type":15},{"name":2447,"slug":2448,"type":15},"Image Generation","image-generation",{"name":2450,"slug":2451,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675,{"items":2456,"total":2569},[2457,2473,2489,2501,2519,2537,2557],{"slug":2458,"name":2458,"fn":2459,"description":2460,"org":2461,"tags":2462,"stars":25,"repoUrl":26,"updatedAt":27},"accessibility-and-inclusive-visualization","make data visualizations accessible","Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2463,2466,2469,2472],{"name":2464,"slug":2465,"type":15},"Accessibility","accessibility",{"name":2467,"slug":2468,"type":15},"Charts","charts",{"name":2470,"slug":2471,"type":15},"Data Visualization","data-visualization",{"name":2372,"slug":2373,"type":15},{"slug":2474,"name":2474,"fn":2475,"description":2476,"org":2477,"tags":2478,"stars":25,"repoUrl":26,"updatedAt":2488},"agent-browser","automate browser interactions for agents","Browser automation CLI for AI agents. Use when the user needs to interact with websites, verify dev server output, test web apps, navigate pages, fill forms, click buttons, take screenshots, extract data, or automate any browser task. Also triggers when a dev server starts so you can verify it visually.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2479,2482,2485],{"name":2480,"slug":2481,"type":15},"Agents","agents",{"name":2483,"slug":2484,"type":15},"Browser Automation","browser-automation",{"name":2486,"slug":2487,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":2490,"name":2490,"fn":2491,"description":2492,"org":2493,"tags":2494,"stars":25,"repoUrl":26,"updatedAt":2500},"agent-browser-verify","verify dev server output with automated browser","Automated browser verification for dev servers. Triggers when a dev server starts to run a visual gut-check with agent-browser — verifies the page loads, checks for console errors, validates key UI elements, and reports pass\u002Ffail before continuing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2495,2496,2499],{"name":2483,"slug":2484,"type":15},{"name":2497,"slug":2498,"type":15},"Local Development","local-development",{"name":2486,"slug":2487,"type":15},"2026-04-06T18:41:17.526867",{"slug":2502,"name":2502,"fn":2503,"description":2504,"org":2505,"tags":2506,"stars":25,"repoUrl":26,"updatedAt":2518},"agents-sdk","build AI agents on Cloudflare Workers","Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2507,2508,2509,2512,2515],{"name":2480,"slug":2481,"type":15},{"name":2340,"slug":2341,"type":15},{"name":2510,"slug":2511,"type":15},"SDK","sdk",{"name":2513,"slug":2514,"type":15},"Serverless","serverless",{"name":2516,"slug":2517,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":2520,"name":2520,"fn":2521,"description":2522,"org":2523,"tags":2524,"stars":25,"repoUrl":26,"updatedAt":2536},"ai-elements","build chat UIs with AI Elements","AI Elements component library guidance — pre-built React components for AI interfaces built on shadcn\u002Fui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2525,2526,2529,2532,2533],{"name":2377,"slug":2378,"type":15},{"name":2527,"slug":2528,"type":15},"React","react",{"name":2530,"slug":2531,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":2394,"slug":2395,"type":15},{"name":2534,"slug":2535,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":2538,"name":2538,"fn":2539,"description":2540,"org":2541,"tags":2542,"stars":25,"repoUrl":26,"updatedAt":2556},"ai-gateway","configure Vercel AI Gateway","Vercel AI Gateway expert guidance. 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