Professional investment reports combining real-time market data, technical indicators, and AI-powered analysis.
Works with
Calculates 10+ technical indicators (RSI, MACD, Moving Averages, Bollinger Bands) and generates 4 types of high-resolution charts (price, indicators, volatility, dashboard)
Accepts stock ticker symbols with optional client name and custom report title; processes multiple symbols in a single request
Outputs institutional-grade Markdown reports with executive summary, technic
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontrading-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches trading-analysis from gracefullight/stock-checker and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate trading-analysis. Access via /trading-analysis in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
3
total installs
3
this week
14
GitHub stars
0
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Run in your terminal
3
installs
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this week
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This skill generates comprehensive investment reports combining real-time market data, technical analysis, Claude AI insights, and professional visualizations.
Use this skill when the user requests:
Required:
symbol: Stock ticker (e.g., SPY, AAPL, TSLA, QQQ)Optional:
client_name: Name of the client/investor (default: "Institutional Investors")report_title: Custom report title (auto-generated if not provided)period: Historical data period (default: "6mo")The skill generates the following files in the reports/ directory:
Markdown Report ({SYMBOL}_analysis_report_{timestamp}.md)
JSON Data ({SYMBOL}_analysis_report_{timestamp}_data.json)
Charts (PNG format, 300 DPI):
{SYMBOL}_price_chart.png: Price with moving averages{SYMBOL}_indicators_chart.png: RSI and MACD{SYMBOL}_volatility_chart.png: Historical volatility{SYMBOL}_summary_dashboard.png: Performance dashboardSimple request: "Generate an investment report for SPY"
Detailed request: "Create a market analysis report for AAPL for Acme Capital with the title 'Q4 2025 AAPL Investment Strategy'"
Multiple symbols: "Generate investment reports for SPY, QQQ, and DIA"
All reports automatically include:
.env fileMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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trading-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
trading-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: trading-analysis is focused, and the summary matches what you get after install.
Keeps context tight: trading-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added trading-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: trading-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend trading-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
trading-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
trading-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
trading-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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