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    "slug": "market-news-analyst",
    "name": "Market News Analyst",
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      "references/corporate_news_impact.md",
      "references/geopolitical_commodity_correlations.md",
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      "references/trusted_news_sources.md"
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          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
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  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Overview",
        "body": "This skill enables comprehensive analysis of market-moving news events from the past 10 days, focusing on their impact on US equity markets and commodities. The skill automatically collects news from trusted sources using WebSearch and WebFetch tools, evaluates market impact magnitude, analyzes actual market reactions, and produces structured English reports ranked by market impact significance."
      },
      {
        "title": "When to Use This Skill",
        "body": "Use this skill when:\n\nUser requests analysis of recent major market news (past 10 days)\nUser wants to understand market reactions to specific events (FOMC decisions, earnings, geopolitical)\nUser needs comprehensive market news summary with impact assessment\nUser asks about correlations between news events and commodity price movements\nUser requests analysis of how central bank policy announcements affected markets\n\nExample user requests:\n\n\"Analyze the major market news from the past 10 days\"\n\"How did the latest FOMC decision impact the market?\"\n\"What were the most important market-moving events this week?\"\n\"Analyze recent geopolitical news and commodity price reactions\"\n\"Review mega-cap tech earnings and their market impact\""
      },
      {
        "title": "Analysis Workflow",
        "body": "Follow this structured 6-step workflow when analyzing market news:"
      },
      {
        "title": "Step 1: News Collection via WebSearch/WebFetch",
        "body": "Objective: Gather comprehensive news from the past 10 days covering major market-moving events.\n\nSearch Strategy:\n\nExecute parallel WebSearch queries covering different news categories:\n\nMonetary Policy:\n\nSearch: \"FOMC meeting past 10 days\", \"Federal Reserve interest rate\", \"ECB policy decision\", \"Bank of Japan\"\nTarget: Central bank decisions, forward guidance changes, inflation commentary\n\nInflation/Economic Data:\n\nSearch: \"CPI inflation report [current month]\", \"jobs report NFP\", \"GDP data\", \"PPI producer prices\"\nTarget: Major economic data releases and surprises\n\nMega-Cap Earnings:\n\nSearch: \"Apple earnings [current quarter]\", \"Microsoft earnings\", \"NVIDIA earnings\", \"Amazon earnings\", \"Tesla earnings\", \"Meta earnings\", \"Google earnings\"\nTarget: Results, guidance, market reactions for largest companies\n\nGeopolitical Events:\n\nSearch: \"Middle East conflict oil prices\", \"Ukraine war\", \"US China tensions\", \"trade war tariffs\"\nTarget: Conflicts, sanctions, trade disputes affecting markets\n\nCommodity Markets:\n\nSearch: \"oil prices news past week\", \"gold prices\", \"OPEC meeting\", \"natural gas prices\", \"copper prices\"\nTarget: Supply disruptions, demand shifts, price movements\n\nCorporate News:\n\nSearch: \"major M&A announcement\", \"bank earnings\", \"tech sector news\", \"bankruptcy\", \"credit rating downgrade\"\nTarget: Large corporate events beyond mega-caps\n\nRecommended News Sources (Priority Order):\n\nOfficial sources: FederalReserve.gov, SEC.gov (EDGAR), Treasury.gov, BLS.gov\nTier 1 financial news: Bloomberg, Reuters, Wall Street Journal, Financial Times\nSpecialized: CNBC (real-time), MarketWatch (summaries), S&P Global Platts (commodities)\n\nSearch Execution:\n\nUse WebSearch for broad topic searches\nUse WebFetch for specific URLs from official sources or major news outlets\nCollect publication dates to ensure news is within 10-day window\nCapture: Event date, source, headline, key details, market context (pre-market, trading hours, after-hours)\n\nFiltering Criteria:\n\nFocus on Tier 1 market-moving events (see references/market_event_patterns.md)\nPrioritize news with clear market impact (price moves, volume spikes)\nExclude: Stock-specific small-cap news, minor product updates, routine filings\n\nThink in English throughout collection process. Document each significant news item with:\n\nDate and time\nEvent type (monetary policy, earnings, geopolitical, etc.)\nSource reliability tier\nInitial market reaction (if observable)"
      },
      {
        "title": "Step 2: Load Knowledge Base References",
        "body": "Objective: Access domain expertise to inform impact assessment.\n\nLoad relevant reference files based on collected news types:\n\nAlways Load:\n\nreferences/market_event_patterns.md - Comprehensive patterns for all major event types\nreferences/trusted_news_sources.md - Source credibility assessment\n\nConditionally Load (Based on News Collected):\n\nIf monetary policy news found:\n\nFocus on: market_event_patterns.md → Central Bank Monetary Policy Events section\nKey frameworks: Interest rate hike/cut reactions, QE/QT impacts, hawkish/dovish tone\n\nIf geopolitical events found:\n\nLoad: references/geopolitical_commodity_correlations.md\nFocus on: Energy Commodities, Precious Metals, regional frameworks matching event\n\nIf mega-cap earnings found:\n\nLoad: references/corporate_news_impact.md\nFocus on: Specific company sections, sector contagion patterns\n\nIf commodity news found:\n\nLoad: references/geopolitical_commodity_correlations.md\nFocus on: Specific commodity sections (Oil, Gold, Copper, etc.)\n\nKnowledge Integration:\nCompare collected news against historical patterns to:\n\nPredict expected market reactions\nIdentify anomalies (market reacted differently than historical pattern)\nAssess whether reaction was typical magnitude or outsized\nDetermine if contagion occurred as expected"
      },
      {
        "title": "Step 3: Impact Magnitude Assessment",
        "body": "Objective: Rank each news event by market impact significance.\n\nImpact Assessment Framework:\n\nFor each news item, evaluate across three dimensions:\n\n1. Asset Price Impact (Primary Factor):\n\nMeasure actual or estimated price movements:\n\nEquity Markets:\n\nIndex-level: S&P 500, Nasdaq 100, Dow Jones\n\nSevere: ±2%+ in day\nMajor: ±1-2%\nModerate: ±0.5-1%\nMinor: ±0.2-0.5%\nNegligible: <0.2%\n\n\n\nSector-level: Specific sector ETFs\n\nSevere: ±5%+\nMajor: ±3-5%\nModerate: ±1-3%\nMinor: <1%\n\n\n\nStock-specific: Individual mega-caps\n\nSevere: ±10%+ (and index weight causes index move)\nMajor: ±5-10%\nModerate: ±2-5%\n\nCommodity Markets:\n\nOil (WTI/Brent):\n\nSevere: ±5%+\nMajor: ±3-5%\nModerate: ±1-3%\n\n\n\nGold:\n\nSevere: ±3%+\nMajor: ±1.5-3%\nModerate: ±0.5-1.5%\n\n\n\nBase Metals (Copper, etc.):\n\nSevere: ±4%+\nMajor: ±2-4%\nModerate: ±1-2%\n\nBond Markets:\n\n10-Year Treasury Yield:\n\nSevere: ±20bps+ in day\nMajor: ±10-20bps\nModerate: ±5-10bps\n\nCurrency Markets:\n\nUSD Index (DXY):\n\nSevere: ±1.5%+\nMajor: ±0.75-1.5%\nModerate: ±0.3-0.75%\n\n2. Breadth of Impact (Multiplier):\n\nAssess how many markets/sectors affected:\n\nSystemic (3x multiplier): Multiple asset classes, global markets\n\nExamples: FOMC surprise, banking crisis, major war outbreak\n\n\n\nCross-Asset (2x multiplier): Equities + commodities, or equities + bonds\n\nExamples: Inflation surprise, geopolitical supply shock\n\n\n\nSector-Wide (1.5x multiplier): Entire sector or related sectors\n\nExamples: Tech earnings cluster, energy policy announcement\n\n\n\nStock-Specific (1x multiplier): Single company (unless mega-cap with index impact)\n\nExamples: Individual company earnings, M&A\n\n3. Forward-Looking Significance (Modifier):\n\nConsider future implications:\n\nRegime Change (+50%): Fundamental market structure shift\n\nExamples: Fed pivot from hiking to cutting, major geopolitical realignment\n\n\n\nTrend Confirmation (+25%): Reinforces existing trajectory\n\nExamples: Consecutive strong inflation prints, sustained earnings beats\n\n\n\nIsolated Event (0%): One-off with limited forward signal\n\nExamples: Single data point within range, company-specific issue\n\n\n\nContrary Signal (-25%): Contradicts prevailing narrative\n\nExamples: Good news ignored by market, bad news rallied\n\nImpact Score Calculation:\n\nImpact Score = (Price Impact Score × Breadth Multiplier) + Forward-Looking Modifier\n\nPrice Impact Score:\n- Severe: 10 points\n- Major: 7 points\n- Moderate: 4 points\n- Minor: 2 points\n- Negligible: 1 point\n\nExample Calculations:\n\nFOMC 75bps Rate Hike (hawkish tone):\n\nPrice Impact: S&P 500 -2.5% (Severe = 10 points)\nBreadth: Systemic (equities, bonds, USD, commodities all moved) = 3x\nForward: Trend confirmation (ongoing tightening) = +25%\nScore: (10 × 3) × 1.25 = 37.5\n\nNVIDIA Earnings Beat:\n\nPrice Impact: NVDA +15%, Nasdaq +1.5% (Severe = 10 points)\nBreadth: Sector-wide (semis, tech broadly) = 1.5x\nForward: Trend confirmation (AI demand) = +25%\nScore: (10 × 1.5) × 1.25 = 18.75\n\nGeopolitical Flare-up (Middle East):\n\nPrice Impact: Oil +8%, S&P -1.2% (Severe = 10 points)\nBreadth: Cross-asset (oil, equities, gold) = 2x\nForward: Isolated event (no escalation) = 0%\nScore: (10 × 2) × 1.0 = 20\n\nSingle Stock Earnings (Non-Mega-Cap):\n\nPrice Impact: Stock +12%, no index impact (Major = 7 points)\nBreadth: Stock-specific = 1x\nForward: Isolated = 0%\nScore: (7 × 1) × 1.0 = 7\n\nRanking:\nAfter scoring all news items, rank from highest to lowest impact score. This determines report ordering."
      },
      {
        "title": "Step 4: Market Reaction Analysis",
        "body": "Objective: Analyze how markets actually responded to each event.\n\nFor each significant news item (Impact Score >5), conduct detailed reaction analysis:\n\nImmediate Reaction (Intraday):\n\nDirection: Positive, negative, mixed\nMagnitude: Align with price impact categories\nTiming: Pre-market, during trading, after-hours\nVolatility: VIX movement, bid-ask spreads\n\nMulti-Asset Response:\n\nEquities:\n\nIndex performance (S&P 500, Nasdaq, Dow, Russell 2000)\nSector rotation (which sectors outperformed/underperformed)\nIndividual stock moves (mega-caps, relevant companies)\nGrowth vs Value, Large vs Small Cap divergences\n\nFixed Income:\n\nTreasury yields (2Y, 10Y, 30Y)\nYield curve shape (steepening, flattening, inversion)\nCredit spreads (IG, HY)\nTIPS breakevens (inflation expectations)\n\nCommodities:\n\nEnergy: Oil (WTI, Brent), Natural Gas\nPrecious Metals: Gold, Silver\nBase Metals: Copper, Aluminum (if relevant)\nAgricultural: Wheat, Corn, Soybeans (if relevant)\n\nCurrencies:\n\nUSD Index (DXY)\nEUR/USD, USD/JPY, GBP/USD\nEmerging market currencies\nSafe havens (JPY, CHF)\n\nDerivatives:\n\nVIX (volatility index)\nOptions activity (put/call ratio, unusual volume)\nFutures positioning\n\nPattern Comparison:\n\nCompare observed reaction against expected pattern from knowledge base:\n\nConsistent: Reaction matched historical pattern\n\nExample: Fed hike → Tech stocks down, USD up (as expected)\n\n\n\nAmplified: Reaction exceeded typical pattern\n\nExample: Inflation print +0.3% above consensus → Selloff 2x typical\nInvestigate: Positioning, sentiment, cumulative factors\n\n\n\nDampened: Reaction less than historical pattern\n\nExample: Geopolitical event → Oil barely moved\nInvestigate: Already priced in, other offsetting factors\n\n\n\nInverse: Reaction opposite of historical pattern\n\nExample: Good news ignored, bad news rallied\nInvestigate: \"Good news is bad news\" dynamics, Fed pivot hopes\n\nAnomaly Identification:\n\nFlag reactions that deviate significantly from patterns:\n\nMarket shrugged off typically market-moving news\nOverreaction to typically minor news\nContagion failed to spread as expected\nSafe havens didn't work (correlations broke)\n\nSentiment Indicators:\n\nRisk-On vs Risk-Off: Which regime dominated\nPositioning: Evidence of crowded trades unwinding\nMomentum: Follow-through in subsequent sessions or reversal"
      },
      {
        "title": "Step 5: Correlation and Causation Assessment",
        "body": "Objective: Distinguish direct impacts from coincidental timing.\n\nMulti-Event Analysis:\n\nWhen multiple significant events occurred in the 10-day period, assess interactions:\n\nReinforcing Events:\n\nSame directional impact\nExample: Hawkish FOMC + hot CPI → Both bearish for equities, amplified move\nCombined impact often non-linear (greater than sum of parts)\n\nOffsetting Events:\n\nOpposite directional impacts\nExample: Strong earnings (positive) + geopolitical tensions (negative) → Muted net reaction\nIdentify which factor dominated\n\nSequential Events:\n\nOne event set up reaction to next\nExample: First rate hike modest reaction, second rate hike severe (cumulative tightening concerns)\nPath dependence matters\n\nCoincidental Timing:\n\nEvents unrelated but occurred simultaneously\nDifficult to isolate individual impacts\nNote uncertainty in attribution\n\nGeopolitical-Commodity Correlations:\n\nFor geopolitical events, specifically analyze commodity market reactions using geopolitical_commodity_correlations.md:\n\nEnergy:\n\nMap conflict/sanction to supply disruption risk\nAssess actual vs feared supply impact\nDuration: Temporary spike vs sustained elevation\n\nPrecious Metals:\n\nSafe-haven flows vs real rate drivers\nGold response to risk-off events\nCentral bank buying implications\n\nIndustrial Metals:\n\nDemand destruction from economic slowdown fears\nSupply chain disruptions\nChina factor in copper, aluminum\n\nAgriculture:\n\nBlack Sea grain exports (Russia-Ukraine)\nWeather overlays\nFood security policy responses\n\nTransmission Mechanisms:\n\nTrace how news impacts flowed through markets:\n\nDirect Channel:\n\nNews → Immediate asset price reaction\nExample: OPEC cuts → Oil prices up immediately\n\nIndirect Channels:\n\nNews → Economic impact → Asset prices\nExample: Rate hike → Mortgage rates up → Housing slows → Homebuilder stocks down\n\nSentiment Channel:\n\nNews → Risk appetite shift → Broad asset reallocation\nExample: Banking crisis → Flight to quality → Treasuries rally, stocks sell\n\nFeedback Loops:\n\nInitial reaction creates secondary effects\nExample: Stock selloff → Margin calls → Forced selling → Deeper selloff"
      },
      {
        "title": "Step 6: Report Generation",
        "body": "Objective: Create structured English Markdown report ranked by market impact.\n\nReport Structure:\n\n# Market News Analysis Report - [Date Range]\n\n## Executive Summary\n\n[3-4 sentences covering:]\n- Period analyzed (specific dates)\n- Number of significant events identified\n- Dominant market theme/regime (risk-on/risk-off, sector rotation)\n- Top 1-2 highest-impact events\n\n## Market Impact Rankings\n\n[Table format, sorted by Impact Score descending]\n\n| Rank | Event | Date | Impact Score | Asset Classes Affected | Market Reaction |\n|------|-------|------|--------------|------------------------|-----------------|\n| 1 | [Event] | [Date] | [Score] | [Equities, Commodities, etc.] | [Brief reaction] |\n| 2 | ... | ... | ... | ... | ... |\n\n---\n\n## Detailed Event Analysis\n\n[For each event in rank order, provide comprehensive analysis]\n\n### [Rank]. [Event Name] (Impact Score: [X])\n\n**Event Date:** [Date, Time]\n**Event Type:** [Monetary Policy / Earnings / Geopolitical / Economic Data / Corporate]\n**News Source:** [Source, with credibility tier]\n\n#### Event Summary\n[3-4 sentences describing what happened]\n- Key details (e.g., rate decision, earnings beat/miss magnitude, conflict developments)\n- Context (was this expected, surprise factor)\n- Forward guidance or implications stated\n\n#### Market Reaction\n\n**Immediate (Day-of):**\n- **Equities:** S&P 500 [+/-X%], Nasdaq [+/-X%], Sector rotation [details]\n- **Bonds:** 10Y yield [change], credit spreads [movement]\n- **Commodities:** Oil [+/-X%], Gold [+/-X%], Copper [+/-X%] (if relevant)\n- **Currencies:** USD [+/-X%], [other relevant pairs]\n- **Volatility:** VIX [level/change]\n\n**Follow-Through (Subsequent Sessions):**\n- [Direction: sustained, reversed, or consolidated]\n- [Additional price action details if significant]\n\n**Pattern Comparison:**\n- **Expected Reaction:** [Based on historical patterns from knowledge base]\n- **Actual vs Expected:** [Consistent / Amplified / Dampened / Inverse]\n- **Explanation of Deviation:** [If applicable, why reaction differed]\n\n#### Impact Assessment Detail\n\n**Asset Price Impact:** [Severe/Major/Moderate/Minor] - [Justification]\n**Breadth:** [Systemic/Cross-Asset/Sector/Stock-Specific] - [Affected markets]\n**Forward Significance:** [Regime Change/Trend Confirmation/Isolated/Contrary] - [Rationale]\n\n**Calculated Score:** ([Price Score] × [Breadth Multiplier]) × [Forward Modifier] = [Total]\n\n#### Sector-Specific Impacts\n\n[If relevant, detail which sectors/industries were most affected]\n- [Sector 1]: [Impact and reason]\n- [Sector 2]: [Impact and reason]\n- [Example: Technology -3% (rate sensitivity), Energy +5% (oil price spillover)]\n\n#### Geopolitical-Commodity Correlation Analysis\n\n[Include this section only for geopolitical events]\n- [Specific commodity affected]: [Price movement]\n- [Supply/demand mechanism]: [Explanation]\n- [Historical precedent]: [Comparison to similar past events]\n- [Expected duration]: [Temporary shock vs sustained impact]\n\n[Repeat detailed analysis for each ranked event]\n\n---\n\n## Thematic Synthesis\n\n### Dominant Market Narrative\n[Identify overarching theme across the 10-day period]\n- [E.g., \"Persistent inflation concerns dominated despite mixed economic data\"]\n- [E.g., \"Tech sector strength drove markets higher despite geopolitical headwinds\"]\n\n### Interconnected Events\n[Analyze how events related or compounded]\n- [Event A] + [Event B] → [Combined impact analysis]\n- [Sequential causation if applicable]\n\n### Market Regime Assessment\n**Risk Appetite:** [Risk-On / Risk-Off / Mixed]\n**Evidence:**\n- [Supporting indicators: sector performance, safe haven flows, credit spreads, VIX]\n\n**Sector Rotation Trends:**\n- [Growth vs Value]\n- [Cyclicals vs Defensives]\n- [Outperformers and underperformers]\n\n### Anomalies and Surprises\n[Highlight unexpected market reactions]\n1. [Event]: Market reacted [unexpectedly] because [explanation]\n2. [Continue for significant anomalies]\n\n---\n\n## Commodity Market Deep Dive\n\n[Dedicated section for commodity movements]\n\n### Energy\n- **Crude Oil (WTI/Brent):** [Price level, % change over period, key drivers]\n- **Natural Gas:** [If significant movement]\n- **Key Events:** [Specific news impacting energy: OPEC, geopolitics, inventory data]\n\n### Precious Metals\n- **Gold:** [Price level, % change, safe-haven flows vs real rate dynamics]\n- **Silver:** [If significant divergence from gold]\n- **Drivers:** [Geopolitical risk premium, inflation hedging, USD strength]\n\n### Base Metals\n- **Copper:** [As economic barometer - demand signals]\n- **Aluminum, Nickel:** [If relevant supply/demand news]\n- **China Factor:** [Impact of Chinese economic data/policy]\n\n### Agricultural (If Relevant)\n- **Grains:** [Wheat, Corn, Soybeans - weather, Ukraine conflict impacts]\n\n[For each commodity, reference geopolitical events from main analysis and draw correlations]\n\n---\n\n## Forward-Looking Implications\n\n### Market Positioning Insights\n[What the news suggests for current market positioning]\n- [Trend continuation or reversal signals]\n- [Overvaluation or undervaluation indications]\n- [Sentiment extremes (complacency or panic)]\n\n### Upcoming Catalysts\n[Events on horizon that may be set up by recent news]\n- [Next FOMC meeting expectations post-recent decision]\n- [Upcoming earnings seasons based on guidance]\n- [Geopolitical developments to monitor]\n\n### Risk Scenarios\n[Based on recent news, identify key risks]\n1. **[Risk Name]:** [Description, probability, potential impact]\n2. **[Risk Name]:** [Description, probability, potential impact]\n3. [Continue for 3-5 key risks]\n\n---\n\n## Data Sources and Methodology\n\n### News Sources Consulted\n[List primary sources used, organized by tier]\n- **Official Sources:** [e.g., FederalReserve.gov, SEC.gov]\n- **Tier 1 Financial News:** [e.g., Bloomberg, Reuters, WSJ]\n- **Specialized:** [e.g., S&P Global Platts for commodities]\n\n### Analysis Period\n- **Start Date:** [Specific date]\n- **End Date:** [Specific date]\n- **Total Days:** 10\n\n### Market Data\n- Equity indices: [Data sources]\n- Commodity prices: [Data sources]\n- Economic data: [Government sources]\n\n### Knowledge Base References\n- `market_event_patterns.md` - Historical reaction patterns\n- `geopolitical_commodity_correlations.md` - Geopolitical-commodity frameworks\n- `corporate_news_impact.md` - Mega-cap impact analysis\n- `trusted_news_sources.md` - Source credibility assessment\n\n---\n\n*Analysis Date: [Date report generated]*\n*Language: English*\n*Analysis Thinking: English*\n\nFile Naming Convention:\nmarket_news_analysis_[START_DATE]_to_[END_DATE].md\n\nExample: market_news_analysis_2024-10-25_to_2024-11-03.md\n\nReport Quality Standards:\n\nObjective, fact-based analysis (no speculation beyond probability-weighted scenarios)\nQuantify price movements with specific percentages\nCite sources for major claims\nDistinguish between correlation and causation\nAcknowledge uncertainty when attributing market moves to specific news\nUse proper financial terminology\nMaintain consistent English throughout"
      },
      {
        "title": "Key Analysis Principles",
        "body": "When conducting market news analysis:\n\nImpact Over Noise: Focus on truly market-moving news, filter out minor events\nMulti-Asset Perspective: Analyze across equities, bonds, commodities, currencies to understand full impact\nPattern Recognition: Compare against historical precedents while noting unique aspects\nCausation Discipline: Be rigorous about attributing market moves to specific news vs coincidental timing\nForward-Looking: Emphasize implications for future market behavior, not just backward-looking description\nObjectivity: Separate market reaction (what happened) from personal market view (what should happen)\nQuantification: Use specific numbers (%, bps) rather than vague terms (\"significant,\" \"large\")\nSource Credibility: Weight official sources and Tier 1 news over rumors and unverified reports\nBreadth Analysis: Individual stock moves only significant if mega-cap or systemic signal\nEnglish Consistency: All thinking, analysis, and output in English for consistency"
      },
      {
        "title": "Common Pitfalls to Avoid",
        "body": "Over-Attribution:\n\nNot every market move is news-driven (technicals, flows, month-end rebalancing exist)\nAcknowledge when attribution is uncertain\n\nRecency Bias:\n\nLatest news isn't always most important\nRank by actual impact, not chronological order\n\nHindsight Bias:\n\nDistinguish \"obvious in retrospect\" from \"surprising at the time\"\nNote consensus expectations vs actual outcomes\n\nSingle-Factor Analysis:\n\nMarkets respond to multiple factors simultaneously\nAcknowledge interaction effects\n\nIgnoring Magnitude:\n\nA \"hot\" CPI that's 0.1% above consensus is different from 0.5% above\nQuantify surprise factor"
      },
      {
        "title": "references/",
        "body": "market_event_patterns.md - Comprehensive knowledge base covering:\n\nCentral bank monetary policy events (FOMC, ECB, BOJ, PBOC)\nInflation data releases (CPI, PPI, PCE)\nEmployment data (NFP, unemployment, wages)\nGDP reports\nGeopolitical events (conflicts, trade wars, sanctions)\nCorporate earnings (mega-cap technology, banks, energy)\nCredit events and rating changes\nCommodity-specific events (OPEC, weather, supply disruptions)\nRecession indicators\nHistorical case studies (2008 crisis, COVID-19, 2022 inflation)\nPattern recognition framework and sentiment analysis\n\ngeopolitical_commodity_correlations.md - Detailed correlations covering:\n\nEnergy commodities (crude oil, natural gas, coal) and geopolitical conflicts\nPrecious metals (gold, silver, platinum, palladium) safe-haven dynamics\nBase metals (copper, aluminum, nickel, zinc) and economic/political risks\nAgricultural commodities (wheat, corn, soybeans) and weather/policy\nRare earth elements and critical minerals (China dominance, supply security)\nRegional geopolitical frameworks (Middle East, Russia-Europe, Asia-Pacific, Latin America)\nCorrelation summary tables\nTime horizon considerations\n\ncorporate_news_impact.md - Mega-cap analysis framework:\n\n\"Magnificent 7\" technology stocks (NVIDIA, Apple, Microsoft, Amazon, Meta, Google, Tesla)\nFinancial sector mega-caps (JPMorgan, Bank of America, etc.)\nHealthcare mega-caps (UnitedHealth, Pfizer, J&J, Merck)\nEnergy mega-caps (Exxon Mobil, Chevron)\nConsumer staples mega-caps (P&G, Coca-Cola, PepsiCo)\nIndustrial mega-caps (Boeing, Caterpillar)\nEarnings impact frameworks, product launches, M&A, regulatory issues\nSector contagion patterns\nImpact magnitude framework\n\ntrusted_news_sources.md - Source credibility guide:\n\nTier 1 primary sources (central banks, government agencies, SEC)\nTier 2 major financial news (Bloomberg, Reuters, WSJ, FT, CNBC)\nTier 3 specialized sources (energy, tech, emerging markets, China-specific, crypto)\nTier 4 analysis and research (independent research, central bank publications, think tanks)\nSearch and aggregation tools\nSource quality assessment criteria\nSpeed vs accuracy trade-offs\nRecommended search strategies for 10-day analysis\nSource credibility framework\nRed flag sources to avoid"
      },
      {
        "title": "Important Notes",
        "body": "All analysis thinking must be conducted in English\nAll output Markdown files must be in English\nUse WebSearch and WebFetch tools to collect news automatically\nFocus on trusted news sources as defined in references\nRank events by impact score (price impact × breadth × forward significance)\nTarget analysis period: Past 10 days from current date\nEmphasize US equity markets and commodities as primary analysis subjects\nFOMC and other central bank policy decisions receive highest priority analysis\nDistinguish between correlation and causation rigorously\nQuantify all market reactions with specific percentages\nLoad appropriate reference files based on news types collected\nGenerate comprehensive reports ranked by market impact (highest impact first)"
      }
    ],
    "body": "Market News Analyst\nOverview\n\nThis skill enables comprehensive analysis of market-moving news events from the past 10 days, focusing on their impact on US equity markets and commodities. The skill automatically collects news from trusted sources using WebSearch and WebFetch tools, evaluates market impact magnitude, analyzes actual market reactions, and produces structured English reports ranked by market impact significance.\n\nWhen to Use This Skill\n\nUse this skill when:\n\nUser requests analysis of recent major market news (past 10 days)\nUser wants to understand market reactions to specific events (FOMC decisions, earnings, geopolitical)\nUser needs comprehensive market news summary with impact assessment\nUser asks about correlations between news events and commodity price movements\nUser requests analysis of how central bank policy announcements affected markets\n\nExample user requests:\n\n\"Analyze the major market news from the past 10 days\"\n\"How did the latest FOMC decision impact the market?\"\n\"What were the most important market-moving events this week?\"\n\"Analyze recent geopolitical news and commodity price reactions\"\n\"Review mega-cap tech earnings and their market impact\"\nAnalysis Workflow\n\nFollow this structured 6-step workflow when analyzing market news:\n\nStep 1: News Collection via WebSearch/WebFetch\n\nObjective: Gather comprehensive news from the past 10 days covering major market-moving events.\n\nSearch Strategy:\n\nExecute parallel WebSearch queries covering different news categories:\n\nMonetary Policy:\n\nSearch: \"FOMC meeting past 10 days\", \"Federal Reserve interest rate\", \"ECB policy decision\", \"Bank of Japan\"\nTarget: Central bank decisions, forward guidance changes, inflation commentary\n\nInflation/Economic Data:\n\nSearch: \"CPI inflation report [current month]\", \"jobs report NFP\", \"GDP data\", \"PPI producer prices\"\nTarget: Major economic data releases and surprises\n\nMega-Cap Earnings:\n\nSearch: \"Apple earnings [current quarter]\", \"Microsoft earnings\", \"NVIDIA earnings\", \"Amazon earnings\", \"Tesla earnings\", \"Meta earnings\", \"Google earnings\"\nTarget: Results, guidance, market reactions for largest companies\n\nGeopolitical Events:\n\nSearch: \"Middle East conflict oil prices\", \"Ukraine war\", \"US China tensions\", \"trade war tariffs\"\nTarget: Conflicts, sanctions, trade disputes affecting markets\n\nCommodity Markets:\n\nSearch: \"oil prices news past week\", \"gold prices\", \"OPEC meeting\", \"natural gas prices\", \"copper prices\"\nTarget: Supply disruptions, demand shifts, price movements\n\nCorporate News:\n\nSearch: \"major M&A announcement\", \"bank earnings\", \"tech sector news\", \"bankruptcy\", \"credit rating downgrade\"\nTarget: Large corporate events beyond mega-caps\n\nRecommended News Sources (Priority Order):\n\nOfficial sources: FederalReserve.gov, SEC.gov (EDGAR), Treasury.gov, BLS.gov\nTier 1 financial news: Bloomberg, Reuters, Wall Street Journal, Financial Times\nSpecialized: CNBC (real-time), MarketWatch (summaries), S&P Global Platts (commodities)\n\nSearch Execution:\n\nUse WebSearch for broad topic searches\nUse WebFetch for specific URLs from official sources or major news outlets\nCollect publication dates to ensure news is within 10-day window\nCapture: Event date, source, headline, key details, market context (pre-market, trading hours, after-hours)\n\nFiltering Criteria:\n\nFocus on Tier 1 market-moving events (see references/market_event_patterns.md)\nPrioritize news with clear market impact (price moves, volume spikes)\nExclude: Stock-specific small-cap news, minor product updates, routine filings\n\nThink in English throughout collection process. Document each significant news item with:\n\nDate and time\nEvent type (monetary policy, earnings, geopolitical, etc.)\nSource reliability tier\nInitial market reaction (if observable)\nStep 2: Load Knowledge Base References\n\nObjective: Access domain expertise to inform impact assessment.\n\nLoad relevant reference files based on collected news types:\n\nAlways Load:\n\nreferences/market_event_patterns.md - Comprehensive patterns for all major event types\nreferences/trusted_news_sources.md - Source credibility assessment\n\nConditionally Load (Based on News Collected):\n\nIf monetary policy news found:\n\nFocus on: market_event_patterns.md → Central Bank Monetary Policy Events section\nKey frameworks: Interest rate hike/cut reactions, QE/QT impacts, hawkish/dovish tone\n\nIf geopolitical events found:\n\nLoad: references/geopolitical_commodity_correlations.md\nFocus on: Energy Commodities, Precious Metals, regional frameworks matching event\n\nIf mega-cap earnings found:\n\nLoad: references/corporate_news_impact.md\nFocus on: Specific company sections, sector contagion patterns\n\nIf commodity news found:\n\nLoad: references/geopolitical_commodity_correlations.md\nFocus on: Specific commodity sections (Oil, Gold, Copper, etc.)\n\nKnowledge Integration: Compare collected news against historical patterns to:\n\nPredict expected market reactions\nIdentify anomalies (market reacted differently than historical pattern)\nAssess whether reaction was typical magnitude or outsized\nDetermine if contagion occurred as expected\nStep 3: Impact Magnitude Assessment\n\nObjective: Rank each news event by market impact significance.\n\nImpact Assessment Framework:\n\nFor each news item, evaluate across three dimensions:\n\n1. Asset Price Impact (Primary Factor):\n\nMeasure actual or estimated price movements:\n\nEquity Markets:\n\nIndex-level: S&P 500, Nasdaq 100, Dow Jones\n\nSevere: ±2%+ in day\nMajor: ±1-2%\nModerate: ±0.5-1%\nMinor: ±0.2-0.5%\nNegligible: <0.2%\n\nSector-level: Specific sector ETFs\n\nSevere: ±5%+\nMajor: ±3-5%\nModerate: ±1-3%\nMinor: <1%\n\nStock-specific: Individual mega-caps\n\nSevere: ±10%+ (and index weight causes index move)\nMajor: ±5-10%\nModerate: ±2-5%\n\nCommodity Markets:\n\nOil (WTI/Brent):\n\nSevere: ±5%+\nMajor: ±3-5%\nModerate: ±1-3%\n\nGold:\n\nSevere: ±3%+\nMajor: ±1.5-3%\nModerate: ±0.5-1.5%\n\nBase Metals (Copper, etc.):\n\nSevere: ±4%+\nMajor: ±2-4%\nModerate: ±1-2%\n\nBond Markets:\n\n10-Year Treasury Yield:\nSevere: ±20bps+ in day\nMajor: ±10-20bps\nModerate: ±5-10bps\n\nCurrency Markets:\n\nUSD Index (DXY):\nSevere: ±1.5%+\nMajor: ±0.75-1.5%\nModerate: ±0.3-0.75%\n\n2. Breadth of Impact (Multiplier):\n\nAssess how many markets/sectors affected:\n\nSystemic (3x multiplier): Multiple asset classes, global markets\n\nExamples: FOMC surprise, banking crisis, major war outbreak\n\nCross-Asset (2x multiplier): Equities + commodities, or equities + bonds\n\nExamples: Inflation surprise, geopolitical supply shock\n\nSector-Wide (1.5x multiplier): Entire sector or related sectors\n\nExamples: Tech earnings cluster, energy policy announcement\n\nStock-Specific (1x multiplier): Single company (unless mega-cap with index impact)\n\nExamples: Individual company earnings, M&A\n\n3. Forward-Looking Significance (Modifier):\n\nConsider future implications:\n\nRegime Change (+50%): Fundamental market structure shift\n\nExamples: Fed pivot from hiking to cutting, major geopolitical realignment\n\nTrend Confirmation (+25%): Reinforces existing trajectory\n\nExamples: Consecutive strong inflation prints, sustained earnings beats\n\nIsolated Event (0%): One-off with limited forward signal\n\nExamples: Single data point within range, company-specific issue\n\nContrary Signal (-25%): Contradicts prevailing narrative\n\nExamples: Good news ignored by market, bad news rallied\n\nImpact Score Calculation:\n\nImpact Score = (Price Impact Score × Breadth Multiplier) + Forward-Looking Modifier\n\nPrice Impact Score:\n- Severe: 10 points\n- Major: 7 points\n- Moderate: 4 points\n- Minor: 2 points\n- Negligible: 1 point\n\n\nExample Calculations:\n\nFOMC 75bps Rate Hike (hawkish tone):\n\nPrice Impact: S&P 500 -2.5% (Severe = 10 points)\nBreadth: Systemic (equities, bonds, USD, commodities all moved) = 3x\nForward: Trend confirmation (ongoing tightening) = +25%\nScore: (10 × 3) × 1.25 = 37.5\n\nNVIDIA Earnings Beat:\n\nPrice Impact: NVDA +15%, Nasdaq +1.5% (Severe = 10 points)\nBreadth: Sector-wide (semis, tech broadly) = 1.5x\nForward: Trend confirmation (AI demand) = +25%\nScore: (10 × 1.5) × 1.25 = 18.75\n\nGeopolitical Flare-up (Middle East):\n\nPrice Impact: Oil +8%, S&P -1.2% (Severe = 10 points)\nBreadth: Cross-asset (oil, equities, gold) = 2x\nForward: Isolated event (no escalation) = 0%\nScore: (10 × 2) × 1.0 = 20\n\nSingle Stock Earnings (Non-Mega-Cap):\n\nPrice Impact: Stock +12%, no index impact (Major = 7 points)\nBreadth: Stock-specific = 1x\nForward: Isolated = 0%\nScore: (7 × 1) × 1.0 = 7\n\nRanking: After scoring all news items, rank from highest to lowest impact score. This determines report ordering.\n\nStep 4: Market Reaction Analysis\n\nObjective: Analyze how markets actually responded to each event.\n\nFor each significant news item (Impact Score >5), conduct detailed reaction analysis:\n\nImmediate Reaction (Intraday):\n\nDirection: Positive, negative, mixed\nMagnitude: Align with price impact categories\nTiming: Pre-market, during trading, after-hours\nVolatility: VIX movement, bid-ask spreads\n\nMulti-Asset Response:\n\nEquities:\n\nIndex performance (S&P 500, Nasdaq, Dow, Russell 2000)\nSector rotation (which sectors outperformed/underperformed)\nIndividual stock moves (mega-caps, relevant companies)\nGrowth vs Value, Large vs Small Cap divergences\n\nFixed Income:\n\nTreasury yields (2Y, 10Y, 30Y)\nYield curve shape (steepening, flattening, inversion)\nCredit spreads (IG, HY)\nTIPS breakevens (inflation expectations)\n\nCommodities:\n\nEnergy: Oil (WTI, Brent), Natural Gas\nPrecious Metals: Gold, Silver\nBase Metals: Copper, Aluminum (if relevant)\nAgricultural: Wheat, Corn, Soybeans (if relevant)\n\nCurrencies:\n\nUSD Index (DXY)\nEUR/USD, USD/JPY, GBP/USD\nEmerging market currencies\nSafe havens (JPY, CHF)\n\nDerivatives:\n\nVIX (volatility index)\nOptions activity (put/call ratio, unusual volume)\nFutures positioning\n\nPattern Comparison:\n\nCompare observed reaction against expected pattern from knowledge base:\n\nConsistent: Reaction matched historical pattern\n\nExample: Fed hike → Tech stocks down, USD up (as expected)\n\nAmplified: Reaction exceeded typical pattern\n\nExample: Inflation print +0.3% above consensus → Selloff 2x typical\nInvestigate: Positioning, sentiment, cumulative factors\n\nDampened: Reaction less than historical pattern\n\nExample: Geopolitical event → Oil barely moved\nInvestigate: Already priced in, other offsetting factors\n\nInverse: Reaction opposite of historical pattern\n\nExample: Good news ignored, bad news rallied\nInvestigate: \"Good news is bad news\" dynamics, Fed pivot hopes\n\nAnomaly Identification:\n\nFlag reactions that deviate significantly from patterns:\n\nMarket shrugged off typically market-moving news\nOverreaction to typically minor news\nContagion failed to spread as expected\nSafe havens didn't work (correlations broke)\n\nSentiment Indicators:\n\nRisk-On vs Risk-Off: Which regime dominated\nPositioning: Evidence of crowded trades unwinding\nMomentum: Follow-through in subsequent sessions or reversal\nStep 5: Correlation and Causation Assessment\n\nObjective: Distinguish direct impacts from coincidental timing.\n\nMulti-Event Analysis:\n\nWhen multiple significant events occurred in the 10-day period, assess interactions:\n\nReinforcing Events:\n\nSame directional impact\nExample: Hawkish FOMC + hot CPI → Both bearish for equities, amplified move\nCombined impact often non-linear (greater than sum of parts)\n\nOffsetting Events:\n\nOpposite directional impacts\nExample: Strong earnings (positive) + geopolitical tensions (negative) → Muted net reaction\nIdentify which factor dominated\n\nSequential Events:\n\nOne event set up reaction to next\nExample: First rate hike modest reaction, second rate hike severe (cumulative tightening concerns)\nPath dependence matters\n\nCoincidental Timing:\n\nEvents unrelated but occurred simultaneously\nDifficult to isolate individual impacts\nNote uncertainty in attribution\n\nGeopolitical-Commodity Correlations:\n\nFor geopolitical events, specifically analyze commodity market reactions using geopolitical_commodity_correlations.md:\n\nEnergy:\n\nMap conflict/sanction to supply disruption risk\nAssess actual vs feared supply impact\nDuration: Temporary spike vs sustained elevation\n\nPrecious Metals:\n\nSafe-haven flows vs real rate drivers\nGold response to risk-off events\nCentral bank buying implications\n\nIndustrial Metals:\n\nDemand destruction from economic slowdown fears\nSupply chain disruptions\nChina factor in copper, aluminum\n\nAgriculture:\n\nBlack Sea grain exports (Russia-Ukraine)\nWeather overlays\nFood security policy responses\n\nTransmission Mechanisms:\n\nTrace how news impacts flowed through markets:\n\nDirect Channel:\n\nNews → Immediate asset price reaction\nExample: OPEC cuts → Oil prices up immediately\n\nIndirect Channels:\n\nNews → Economic impact → Asset prices\nExample: Rate hike → Mortgage rates up → Housing slows → Homebuilder stocks down\n\nSentiment Channel:\n\nNews → Risk appetite shift → Broad asset reallocation\nExample: Banking crisis → Flight to quality → Treasuries rally, stocks sell\n\nFeedback Loops:\n\nInitial reaction creates secondary effects\nExample: Stock selloff → Margin calls → Forced selling → Deeper selloff\nStep 6: Report Generation\n\nObjective: Create structured English Markdown report ranked by market impact.\n\nReport Structure:\n\n# Market News Analysis Report - [Date Range]\n\n## Executive Summary\n\n[3-4 sentences covering:]\n- Period analyzed (specific dates)\n- Number of significant events identified\n- Dominant market theme/regime (risk-on/risk-off, sector rotation)\n- Top 1-2 highest-impact events\n\n## Market Impact Rankings\n\n[Table format, sorted by Impact Score descending]\n\n| Rank | Event | Date | Impact Score | Asset Classes Affected | Market Reaction |\n|------|-------|------|--------------|------------------------|-----------------|\n| 1 | [Event] | [Date] | [Score] | [Equities, Commodities, etc.] | [Brief reaction] |\n| 2 | ... | ... | ... | ... | ... |\n\n---\n\n## Detailed Event Analysis\n\n[For each event in rank order, provide comprehensive analysis]\n\n### [Rank]. [Event Name] (Impact Score: [X])\n\n**Event Date:** [Date, Time]\n**Event Type:** [Monetary Policy / Earnings / Geopolitical / Economic Data / Corporate]\n**News Source:** [Source, with credibility tier]\n\n#### Event Summary\n[3-4 sentences describing what happened]\n- Key details (e.g., rate decision, earnings beat/miss magnitude, conflict developments)\n- Context (was this expected, surprise factor)\n- Forward guidance or implications stated\n\n#### Market Reaction\n\n**Immediate (Day-of):**\n- **Equities:** S&P 500 [+/-X%], Nasdaq [+/-X%], Sector rotation [details]\n- **Bonds:** 10Y yield [change], credit spreads [movement]\n- **Commodities:** Oil [+/-X%], Gold [+/-X%], Copper [+/-X%] (if relevant)\n- **Currencies:** USD [+/-X%], [other relevant pairs]\n- **Volatility:** VIX [level/change]\n\n**Follow-Through (Subsequent Sessions):**\n- [Direction: sustained, reversed, or consolidated]\n- [Additional price action details if significant]\n\n**Pattern Comparison:**\n- **Expected Reaction:** [Based on historical patterns from knowledge base]\n- **Actual vs Expected:** [Consistent / Amplified / Dampened / Inverse]\n- **Explanation of Deviation:** [If applicable, why reaction differed]\n\n#### Impact Assessment Detail\n\n**Asset Price Impact:** [Severe/Major/Moderate/Minor] - [Justification]\n**Breadth:** [Systemic/Cross-Asset/Sector/Stock-Specific] - [Affected markets]\n**Forward Significance:** [Regime Change/Trend Confirmation/Isolated/Contrary] - [Rationale]\n\n**Calculated Score:** ([Price Score] × [Breadth Multiplier]) × [Forward Modifier] = [Total]\n\n#### Sector-Specific Impacts\n\n[If relevant, detail which sectors/industries were most affected]\n- [Sector 1]: [Impact and reason]\n- [Sector 2]: [Impact and reason]\n- [Example: Technology -3% (rate sensitivity), Energy +5% (oil price spillover)]\n\n#### Geopolitical-Commodity Correlation Analysis\n\n[Include this section only for geopolitical events]\n- [Specific commodity affected]: [Price movement]\n- [Supply/demand mechanism]: [Explanation]\n- [Historical precedent]: [Comparison to similar past events]\n- [Expected duration]: [Temporary shock vs sustained impact]\n\n[Repeat detailed analysis for each ranked event]\n\n---\n\n## Thematic Synthesis\n\n### Dominant Market Narrative\n[Identify overarching theme across the 10-day period]\n- [E.g., \"Persistent inflation concerns dominated despite mixed economic data\"]\n- [E.g., \"Tech sector strength drove markets higher despite geopolitical headwinds\"]\n\n### Interconnected Events\n[Analyze how events related or compounded]\n- [Event A] + [Event B] → [Combined impact analysis]\n- [Sequential causation if applicable]\n\n### Market Regime Assessment\n**Risk Appetite:** [Risk-On / Risk-Off / Mixed]\n**Evidence:**\n- [Supporting indicators: sector performance, safe haven flows, credit spreads, VIX]\n\n**Sector Rotation Trends:**\n- [Growth vs Value]\n- [Cyclicals vs Defensives]\n- [Outperformers and underperformers]\n\n### Anomalies and Surprises\n[Highlight unexpected market reactions]\n1. [Event]: Market reacted [unexpectedly] because [explanation]\n2. [Continue for significant anomalies]\n\n---\n\n## Commodity Market Deep Dive\n\n[Dedicated section for commodity movements]\n\n### Energy\n- **Crude Oil (WTI/Brent):** [Price level, % change over period, key drivers]\n- **Natural Gas:** [If significant movement]\n- **Key Events:** [Specific news impacting energy: OPEC, geopolitics, inventory data]\n\n### Precious Metals\n- **Gold:** [Price level, % change, safe-haven flows vs real rate dynamics]\n- **Silver:** [If significant divergence from gold]\n- **Drivers:** [Geopolitical risk premium, inflation hedging, USD strength]\n\n### Base Metals\n- **Copper:** [As economic barometer - demand signals]\n- **Aluminum, Nickel:** [If relevant supply/demand news]\n- **China Factor:** [Impact of Chinese economic data/policy]\n\n### Agricultural (If Relevant)\n- **Grains:** [Wheat, Corn, Soybeans - weather, Ukraine conflict impacts]\n\n[For each commodity, reference geopolitical events from main analysis and draw correlations]\n\n---\n\n## Forward-Looking Implications\n\n### Market Positioning Insights\n[What the news suggests for current market positioning]\n- [Trend continuation or reversal signals]\n- [Overvaluation or undervaluation indications]\n- [Sentiment extremes (complacency or panic)]\n\n### Upcoming Catalysts\n[Events on horizon that may be set up by recent news]\n- [Next FOMC meeting expectations post-recent decision]\n- [Upcoming earnings seasons based on guidance]\n- [Geopolitical developments to monitor]\n\n### Risk Scenarios\n[Based on recent news, identify key risks]\n1. **[Risk Name]:** [Description, probability, potential impact]\n2. **[Risk Name]:** [Description, probability, potential impact]\n3. [Continue for 3-5 key risks]\n\n---\n\n## Data Sources and Methodology\n\n### News Sources Consulted\n[List primary sources used, organized by tier]\n- **Official Sources:** [e.g., FederalReserve.gov, SEC.gov]\n- **Tier 1 Financial News:** [e.g., Bloomberg, Reuters, WSJ]\n- **Specialized:** [e.g., S&P Global Platts for commodities]\n\n### Analysis Period\n- **Start Date:** [Specific date]\n- **End Date:** [Specific date]\n- **Total Days:** 10\n\n### Market Data\n- Equity indices: [Data sources]\n- Commodity prices: [Data sources]\n- Economic data: [Government sources]\n\n### Knowledge Base References\n- `market_event_patterns.md` - Historical reaction patterns\n- `geopolitical_commodity_correlations.md` - Geopolitical-commodity frameworks\n- `corporate_news_impact.md` - Mega-cap impact analysis\n- `trusted_news_sources.md` - Source credibility assessment\n\n---\n\n*Analysis Date: [Date report generated]*\n*Language: English*\n*Analysis Thinking: English*\n\n\n\nFile Naming Convention: market_news_analysis_[START_DATE]_to_[END_DATE].md\n\nExample: market_news_analysis_2024-10-25_to_2024-11-03.md\n\nReport Quality Standards:\n\nObjective, fact-based analysis (no speculation beyond probability-weighted scenarios)\nQuantify price movements with specific percentages\nCite sources for major claims\nDistinguish between correlation and causation\nAcknowledge uncertainty when attributing market moves to specific news\nUse proper financial terminology\nMaintain consistent English throughout\nKey Analysis Principles\n\nWhen conducting market news analysis:\n\nImpact Over Noise: Focus on truly market-moving news, filter out minor events\nMulti-Asset Perspective: Analyze across equities, bonds, commodities, currencies to understand full impact\nPattern Recognition: Compare against historical precedents while noting unique aspects\nCausation Discipline: Be rigorous about attributing market moves to specific news vs coincidental timing\nForward-Looking: Emphasize implications for future market behavior, not just backward-looking description\nObjectivity: Separate market reaction (what happened) from personal market view (what should happen)\nQuantification: Use specific numbers (%, bps) rather than vague terms (\"significant,\" \"large\")\nSource Credibility: Weight official sources and Tier 1 news over rumors and unverified reports\nBreadth Analysis: Individual stock moves only significant if mega-cap or systemic signal\nEnglish Consistency: All thinking, analysis, and output in English for consistency\nCommon Pitfalls to Avoid\n\nOver-Attribution:\n\nNot every market move is news-driven (technicals, flows, month-end rebalancing exist)\nAcknowledge when attribution is uncertain\n\nRecency Bias:\n\nLatest news isn't always most important\nRank by actual impact, not chronological order\n\nHindsight Bias:\n\nDistinguish \"obvious in retrospect\" from \"surprising at the time\"\nNote consensus expectations vs actual outcomes\n\nSingle-Factor Analysis:\n\nMarkets respond to multiple factors simultaneously\nAcknowledge interaction effects\n\nIgnoring Magnitude:\n\nA \"hot\" CPI that's 0.1% above consensus is different from 0.5% above\nQuantify surprise factor\nResources\nreferences/\n\nmarket_event_patterns.md - Comprehensive knowledge base covering:\n\nCentral bank monetary policy events (FOMC, ECB, BOJ, PBOC)\nInflation data releases (CPI, PPI, PCE)\nEmployment data (NFP, unemployment, wages)\nGDP reports\nGeopolitical events (conflicts, trade wars, sanctions)\nCorporate earnings (mega-cap technology, banks, energy)\nCredit events and rating changes\nCommodity-specific events (OPEC, weather, supply disruptions)\nRecession indicators\nHistorical case studies (2008 crisis, COVID-19, 2022 inflation)\nPattern recognition framework and sentiment analysis\n\ngeopolitical_commodity_correlations.md - Detailed correlations covering:\n\nEnergy commodities (crude oil, natural gas, coal) and geopolitical conflicts\nPrecious metals (gold, silver, platinum, palladium) safe-haven dynamics\nBase metals (copper, aluminum, nickel, zinc) and economic/political risks\nAgricultural commodities (wheat, corn, soybeans) and weather/policy\nRare earth elements and critical minerals (China dominance, supply security)\nRegional geopolitical frameworks (Middle East, Russia-Europe, Asia-Pacific, Latin America)\nCorrelation summary tables\nTime horizon considerations\n\ncorporate_news_impact.md - Mega-cap analysis framework:\n\n\"Magnificent 7\" technology stocks (NVIDIA, Apple, Microsoft, Amazon, Meta, Google, Tesla)\nFinancial sector mega-caps (JPMorgan, Bank of America, etc.)\nHealthcare mega-caps (UnitedHealth, Pfizer, J&J, Merck)\nEnergy mega-caps (Exxon Mobil, Chevron)\nConsumer staples mega-caps (P&G, Coca-Cola, PepsiCo)\nIndustrial mega-caps (Boeing, Caterpillar)\nEarnings impact frameworks, product launches, M&A, regulatory issues\nSector contagion patterns\nImpact magnitude framework\n\ntrusted_news_sources.md - Source credibility guide:\n\nTier 1 primary sources (central banks, government agencies, SEC)\nTier 2 major financial news (Bloomberg, Reuters, WSJ, FT, CNBC)\nTier 3 specialized sources (energy, tech, emerging markets, China-specific, crypto)\nTier 4 analysis and research (independent research, central bank publications, think tanks)\nSearch and aggregation tools\nSource quality assessment criteria\nSpeed vs accuracy trade-offs\nRecommended search strategies for 10-day analysis\nSource credibility framework\nRed flag sources to avoid\nImportant Notes\nAll analysis thinking must be conducted in English\nAll output Markdown files must be in English\nUse WebSearch and WebFetch tools to collect news automatically\nFocus on trusted news sources as defined in references\nRank events by impact score (price impact × breadth × forward significance)\nTarget analysis period: Past 10 days from current date\nEmphasize US equity markets and commodities as primary analysis subjects\nFOMC and other central bank policy decisions receive highest priority analysis\nDistinguish between correlation and causation rigorously\nQuantify all market reactions with specific percentages\nLoad appropriate reference files based on news types collected\nGenerate comprehensive reports ranked by market impact (highest impact first)"
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