{ "schemaVersion": "1.0", "item": { "slug": "sector-analyst", "name": "Sector Analyst", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/Veeramanikandanr48/sector-analyst", "canonicalUrl": "https://clawhub.ai/Veeramanikandanr48/sector-analyst", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/sector-analyst", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=sector-analyst", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md", "references/sector_rotation.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "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." } ] }, "sourceHealth": { "source": "tencent", "slug": "sector-analyst", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T03:33:45.993Z", "expiresAt": "2026-05-07T03:33:45.993Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=sector-analyst", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=sector-analyst", "contentDisposition": "attachment; filename=\"sector-analyst-0.1.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "sector-analyst" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/sector-analyst" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/sector-analyst", "agentPageUrl": "https://openagent3.xyz/skills/sector-analyst/agent", "manifestUrl": "https://openagent3.xyz/skills/sector-analyst/agent.json", "briefUrl": "https://openagent3.xyz/skills/sector-analyst/agent.md" }, "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." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Overview", "body": "This skill enables comprehensive analysis of sector and industry performance charts to identify market cycle positioning and predict likely rotation scenarios. The analysis combines observed performance data with established sector rotation principles to provide objective market assessment and probabilistic scenario forecasting." }, { "title": "When to Use This Skill", "body": "Use this skill when:\n\nUser provides sector performance charts (typically 1-week and 1-month timeframes)\nUser provides industry performance charts showing relative performance data\nUser requests analysis of current market cycle positioning\nUser asks for sector rotation assessment or predictions\nUser needs probability-weighted scenarios for market positioning\n\nExample user requests:\n\n\"Analyze these sector performance charts and tell me where we are in the market cycle\"\n\"Based on these performance charts, what sectors should outperform next?\"\n\"What's the probability of a defensive rotation based on this data?\"\n\"Review these sector and industry charts and provide scenario analysis\"" }, { "title": "Analysis Workflow", "body": "Follow this structured workflow when analyzing sector/industry performance charts:" }, { "title": "Step 1: Data Collection and Observation", "body": "First, carefully examine all provided chart images to extract:\n\nSector-level performance: Identify which sectors (Technology, Financials, Consumer Discretionary, etc.) are outperforming/underperforming\nIndustry-level performance: Note specific industries showing strength or weakness\nTimeframe comparison: Compare 1-week vs 1-month performance to identify trend consistency or divergence\nMagnitude of moves: Assess the size of relative performance differences\nBreadth of movement: Determine if performance is concentrated or broad-based\n\nThink in English while analyzing the charts. Document specific numerical performance figures for key sectors and industries." }, { "title": "Step 2: Market Cycle Assessment", "body": "Load the sector rotation knowledge base to inform analysis:\n\nRead references/sector_rotation.md to access market cycle and sector rotation frameworks\nCompare observed performance patterns against expected patterns for each cycle phase:\n\nEarly Cycle Recovery\nMid Cycle Expansion\nLate Cycle\nRecession\n\nIdentify which cycle phase best matches current observations by:\n\nMapping outperforming sectors to typical cycle leaders\nMapping underperforming sectors to typical cycle laggards\nAssessing consistency across multiple sectors\nEvaluating alignment with defensive vs cyclical sector performance" }, { "title": "Step 3: Current Situation Analysis", "body": "Synthesize observations into an objective assessment:\n\nState which market cycle phase current performance most closely resembles\nHighlight supporting evidence (which sectors/industries confirm this view)\nNote any contradictory signals or unusual patterns\nAssess confidence level based on consistency of signals\n\nUse data-driven language and specific references to performance figures." }, { "title": "Step 4: Scenario Development", "body": "Based on sector rotation principles and current positioning, develop 2-4 potential scenarios for the next phase:\n\nFor each scenario:\n\nDescribe the market cycle transition\nIdentify which sectors would likely outperform\nIdentify which sectors would likely underperform\nSpecify the catalysts or conditions that would confirm this scenario\nAssign a probability (see Probability Assessment Framework in sector_rotation.md)\n\nScenarios should range from most likely (highest probability) to alternative/contrarian scenarios." }, { "title": "Step 5: Output Generation", "body": "Create a structured Markdown document with the following sections:\n\nRequired Sections:\n\nExecutive Summary: 2-3 sentence overview of key findings\nCurrent Situation: Detailed analysis of current performance patterns and market cycle positioning\nSupporting Evidence: Specific sector and industry performance data supporting the cycle assessment\nScenario Analysis: 2-4 scenarios with descriptions and probability assignments\nRecommended Positioning: Strategic and tactical positioning recommendations based on scenario probabilities\nKey Risks: Notable risks or contradictory signals to monitor" }, { "title": "Output Format", "body": "Save analysis results as a Markdown file with naming convention: sector_analysis_YYYY-MM-DD.md\n\nUse this structure:\n\n# Sector Performance Analysis - [Date]\n\n## Executive Summary\n\n[2-3 sentences summarizing key findings]\n\n## Current Situation\n\n### Market Cycle Assessment\n[Which cycle phase and why]\n\n### Performance Patterns Observed\n\n#### 1-Week Performance\n[Analysis of recent performance]\n\n#### 1-Month Performance\n[Analysis of medium-term trends]\n\n#### Sector-Level Analysis\n[Detailed breakdown by sector]\n\n#### Industry-Level Analysis\n[Notable industry-specific observations]\n\n## Supporting Evidence\n\n### Confirming Signals\n- [List data points supporting cycle assessment]\n\n### Contradictory Signals\n- [List any conflicting indicators]\n\n## Scenario Analysis\n\n### Scenario 1: [Name] (Probability: XX%)\n**Description**: [What happens]\n**Outperformers**: [Sectors/industries]\n**Underperformers**: [Sectors/industries]\n**Catalysts**: [What would confirm this scenario]\n\n### Scenario 2: [Name] (Probability: XX%)\n[Repeat structure]\n\n[Additional scenarios as appropriate]\n\n## Recommended Positioning\n\n### Strategic Positioning (Medium-term)\n[Sector allocation recommendations]\n\n### Tactical Positioning (Short-term)\n[Specific adjustments or opportunities]\n\n## Key Risks and Monitoring Points\n\n[What to watch that could invalidate the analysis]\n\n---\n*Analysis Date: [Date]*\n*Data Period: [Timeframe of charts analyzed]*" }, { "title": "Key Analysis Principles", "body": "When conducting analysis:\n\nObjectivity First: Let the data guide conclusions, not preconceptions\nProbabilistic Thinking: Express uncertainty through probability ranges\nMultiple Timeframes: Compare 1-week and 1-month data for trend confirmation\nRelative Performance: Focus on relative strength, not absolute returns\nBreadth Matters: Broad-based moves are more significant than isolated movements\nNo Absolutes: Markets rarely follow textbook patterns exactly\nHistorical Context: Reference typical rotation patterns but acknowledge uniqueness" }, { "title": "Probability Guidelines", "body": "Apply these probability ranges based on evidence strength:\n\n70-85%: Strong evidence with multiple confirming signals across sectors and timeframes\n50-70%: Moderate evidence with some confirming signals but mixed indicators\n30-50%: Weak evidence with limited or conflicting signals\n15-30%: Speculative scenario contrary to current indicators but possible\n\nTotal probabilities across all scenarios should sum to approximately 100%." }, { "title": "references/", "body": "sector_rotation.md - Comprehensive knowledge base covering market cycle phases, typical sector performance patterns, and probability assessment frameworks" }, { "title": "assets/", "body": "Sample charts demonstrating the expected input format:\n\nsector_performance.jpeg - Example sector-level performance chart (1-week and 1-month)\nindustory_performance_1.jpeg - Example industry performance chart (outperformers)\nindustory_performance_2.jpeg - Example industry performance chart (underperformers)\n\nThese samples illustrate the type of visual data this skill analyzes. User-provided charts may vary in format but should contain similar relative performance information." }, { "title": "Important Notes", "body": "All analysis thinking should be conducted in English\nOutput Markdown files must be in English\nReference the sector rotation knowledge base for each analysis\nMaintain objectivity and avoid confirmation bias\nUpdate probability assessments if new data becomes available\nCharts typically show performance over 1-week and 1-month periods" } ], "body": "Sector Analyst\nOverview\n\nThis skill enables comprehensive analysis of sector and industry performance charts to identify market cycle positioning and predict likely rotation scenarios. The analysis combines observed performance data with established sector rotation principles to provide objective market assessment and probabilistic scenario forecasting.\n\nWhen to Use This Skill\n\nUse this skill when:\n\nUser provides sector performance charts (typically 1-week and 1-month timeframes)\nUser provides industry performance charts showing relative performance data\nUser requests analysis of current market cycle positioning\nUser asks for sector rotation assessment or predictions\nUser needs probability-weighted scenarios for market positioning\n\nExample user requests:\n\n\"Analyze these sector performance charts and tell me where we are in the market cycle\"\n\"Based on these performance charts, what sectors should outperform next?\"\n\"What's the probability of a defensive rotation based on this data?\"\n\"Review these sector and industry charts and provide scenario analysis\"\nAnalysis Workflow\n\nFollow this structured workflow when analyzing sector/industry performance charts:\n\nStep 1: Data Collection and Observation\n\nFirst, carefully examine all provided chart images to extract:\n\nSector-level performance: Identify which sectors (Technology, Financials, Consumer Discretionary, etc.) are outperforming/underperforming\nIndustry-level performance: Note specific industries showing strength or weakness\nTimeframe comparison: Compare 1-week vs 1-month performance to identify trend consistency or divergence\nMagnitude of moves: Assess the size of relative performance differences\nBreadth of movement: Determine if performance is concentrated or broad-based\n\nThink in English while analyzing the charts. Document specific numerical performance figures for key sectors and industries.\n\nStep 2: Market Cycle Assessment\n\nLoad the sector rotation knowledge base to inform analysis:\n\nRead references/sector_rotation.md to access market cycle and sector rotation frameworks\nCompare observed performance patterns against expected patterns for each cycle phase:\nEarly Cycle Recovery\nMid Cycle Expansion\nLate Cycle\nRecession\n\nIdentify which cycle phase best matches current observations by:\n\nMapping outperforming sectors to typical cycle leaders\nMapping underperforming sectors to typical cycle laggards\nAssessing consistency across multiple sectors\nEvaluating alignment with defensive vs cyclical sector performance\nStep 3: Current Situation Analysis\n\nSynthesize observations into an objective assessment:\n\nState which market cycle phase current performance most closely resembles\nHighlight supporting evidence (which sectors/industries confirm this view)\nNote any contradictory signals or unusual patterns\nAssess confidence level based on consistency of signals\n\nUse data-driven language and specific references to performance figures.\n\nStep 4: Scenario Development\n\nBased on sector rotation principles and current positioning, develop 2-4 potential scenarios for the next phase:\n\nFor each scenario:\n\nDescribe the market cycle transition\nIdentify which sectors would likely outperform\nIdentify which sectors would likely underperform\nSpecify the catalysts or conditions that would confirm this scenario\nAssign a probability (see Probability Assessment Framework in sector_rotation.md)\n\nScenarios should range from most likely (highest probability) to alternative/contrarian scenarios.\n\nStep 5: Output Generation\n\nCreate a structured Markdown document with the following sections:\n\nRequired Sections:\n\nExecutive Summary: 2-3 sentence overview of key findings\nCurrent Situation: Detailed analysis of current performance patterns and market cycle positioning\nSupporting Evidence: Specific sector and industry performance data supporting the cycle assessment\nScenario Analysis: 2-4 scenarios with descriptions and probability assignments\nRecommended Positioning: Strategic and tactical positioning recommendations based on scenario probabilities\nKey Risks: Notable risks or contradictory signals to monitor\nOutput Format\n\nSave analysis results as a Markdown file with naming convention: sector_analysis_YYYY-MM-DD.md\n\nUse this structure:\n\n# Sector Performance Analysis - [Date]\n\n## Executive Summary\n\n[2-3 sentences summarizing key findings]\n\n## Current Situation\n\n### Market Cycle Assessment\n[Which cycle phase and why]\n\n### Performance Patterns Observed\n\n#### 1-Week Performance\n[Analysis of recent performance]\n\n#### 1-Month Performance\n[Analysis of medium-term trends]\n\n#### Sector-Level Analysis\n[Detailed breakdown by sector]\n\n#### Industry-Level Analysis\n[Notable industry-specific observations]\n\n## Supporting Evidence\n\n### Confirming Signals\n- [List data points supporting cycle assessment]\n\n### Contradictory Signals\n- [List any conflicting indicators]\n\n## Scenario Analysis\n\n### Scenario 1: [Name] (Probability: XX%)\n**Description**: [What happens]\n**Outperformers**: [Sectors/industries]\n**Underperformers**: [Sectors/industries]\n**Catalysts**: [What would confirm this scenario]\n\n### Scenario 2: [Name] (Probability: XX%)\n[Repeat structure]\n\n[Additional scenarios as appropriate]\n\n## Recommended Positioning\n\n### Strategic Positioning (Medium-term)\n[Sector allocation recommendations]\n\n### Tactical Positioning (Short-term)\n[Specific adjustments or opportunities]\n\n## Key Risks and Monitoring Points\n\n[What to watch that could invalidate the analysis]\n\n---\n*Analysis Date: [Date]*\n*Data Period: [Timeframe of charts analyzed]*\n\nKey Analysis Principles\n\nWhen conducting analysis:\n\nObjectivity First: Let the data guide conclusions, not preconceptions\nProbabilistic Thinking: Express uncertainty through probability ranges\nMultiple Timeframes: Compare 1-week and 1-month data for trend confirmation\nRelative Performance: Focus on relative strength, not absolute returns\nBreadth Matters: Broad-based moves are more significant than isolated movements\nNo Absolutes: Markets rarely follow textbook patterns exactly\nHistorical Context: Reference typical rotation patterns but acknowledge uniqueness\nProbability Guidelines\n\nApply these probability ranges based on evidence strength:\n\n70-85%: Strong evidence with multiple confirming signals across sectors and timeframes\n50-70%: Moderate evidence with some confirming signals but mixed indicators\n30-50%: Weak evidence with limited or conflicting signals\n15-30%: Speculative scenario contrary to current indicators but possible\n\nTotal probabilities across all scenarios should sum to approximately 100%.\n\nResources\nreferences/\nsector_rotation.md - Comprehensive knowledge base covering market cycle phases, typical sector performance patterns, and probability assessment frameworks\nassets/\n\nSample charts demonstrating the expected input format:\n\nsector_performance.jpeg - Example sector-level performance chart (1-week and 1-month)\nindustory_performance_1.jpeg - Example industry performance chart (outperformers)\nindustory_performance_2.jpeg - Example industry performance chart (underperformers)\n\nThese samples illustrate the type of visual data this skill analyzes. User-provided charts may vary in format but should contain similar relative performance information.\n\nImportant Notes\nAll analysis thinking should be conducted in English\nOutput Markdown files must be in English\nReference the sector rotation knowledge base for each analysis\nMaintain objectivity and avoid confirmation bias\nUpdate probability assessments if new data becomes available\nCharts typically show performance over 1-week and 1-month periods" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/Veeramanikandanr48/sector-analyst", "publisherUrl": "https://clawhub.ai/Veeramanikandanr48/sector-analyst", "owner": "Veeramanikandanr48", "version": "0.1.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/sector-analyst", "downloadUrl": "https://openagent3.xyz/downloads/sector-analyst", "agentUrl": "https://openagent3.xyz/skills/sector-analyst/agent", "manifestUrl": "https://openagent3.xyz/skills/sector-analyst/agent.json", "briefUrl": "https://openagent3.xyz/skills/sector-analyst/agent.md" } }