Fetches crypto-ta-analyzer from dkyazzentwatwa/chatgpt-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/crypto-ta-analyzer
Restart Cursor to activate crypto-ta-analyzer. Access via /crypto-ta-analyzer in your agent's command palette.
⚠
Security Notice
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.
Multi-indicator technical analysis system that generates high-confidence trading signals by combining 29+ proven algorithms. Features divergence detection, Bollinger Band squeeze alerts, volume confirmation, and a 7-tier signal system. Ideal for cryptocurrency and stock market analysis.
Core Workflow
1. Data Acquisition
Fetch historical price data from any supported source:
CoinGecko (via MCP tools):
Use coingecko_get_historical_chart tool with:
- coin_id: Target cryptocurrency (e.g., 'bitcoin', 'ethereum')
- days: Time range ('7', '30', '90', '365', 'max')
- vs_currency: Base currency (default 'usd')
Other Supported Sources:
Exchange APIs (Binance, Coinbase, etc.) - OHLCV format
Yahoo Finance - Stock data
Any price-only data - Automatic OHLC approximation
Minimum Requirements:
At least 100 data points for reliable analysis (50 minimum)
Price data required, volume recommended
Recent data preferred for active trading signals
2. Convert Data to OHLCV Format
The generic data converter auto-detects and normalizes any supported format:
from scripts.data_converter import normalize_ohlcv, validate_data_quality
# Auto-detect format and convertohlcv_df, metadata = normalize_ohlcv(raw_data, source="auto")# Check conversion qualityprint(f"Format detected: {metadata['detected_format']}")print(f"Rows: {metadata['original_rows']} -> {metadata['final_rows']}")print(f"Warnings: {metadata['warnings']}")# Validate data qualityquality_report = validate_data_quality(ohlcv_df)
Backward compatible with old CoinGecko converter:
from scripts.data_converter import prepare_analysis_data
ohlcv_df = prepare_analysis_data(coingecko_json_data)
3. Run Technical Analysis
Execute the analyzer with prepared data:
from scripts.ta_analyzer import TechnicalAnalyzer
import json
# Initialize analyzer with OHLCV dataanalyzer = TechnicalAnalyzer(ohlcv_df)# Run comprehensive analysisresults = analyzer.analyze_all()# Display resultsprint(json.dumps(results, indent=2))
4. Interpret Results
Analysis returns comprehensive data including new features:
1. Call coingecko_get_historical_chart for target coin (7-30 days)
2. Convert data using coingecko_converter
3. Run ta_analyzer.analyze_all()
4. Present scoreTotal and tradeSignal to user
Comparative Analysis
To compare multiple cryptocurrencies:
1. Call coingecko_compare_coins for target coins
2. For each coin:
- Fetch historical chart data
- Run technical analysis
- Store results
3. Create comparison table with scores and signals
4. Identify strongest/weakest performers
Deep Dive Analysis
For comprehensive assessment with context:
1. Fetch multiple timeframes (7d, 30d, 90d)
2. Run analysis on each timeframe
3. Check for signal agreement across timeframes
4. Review individual indicator signals for divergences
5. Cross-reference with market data (market cap, volume, dominance)
6. Provide detailed report with confidence levels
Trend Monitoring
For ongoing market surveillance:
1. Fetch current data for watchlist
2. Run analysis on all coins
3. Filter for STRONG_UPTREND signals (score >= 7)
4. Rank by score descending
5. Present top opportunities with context
Best Practices
Data Quality
Always validate data quality before analysis using validate_data_quality()
Ensure minimum 100 data points (preferably 200+)
Check for missing values or data gaps
Use appropriate timeframe for user's trading strategy
Interpretation Guidelines
Never rely on single indicator - the power is in consensus
Consider market context - indicators behave differently in trending vs ranging markets
Watch for divergences - when price contradicts indicators, reversal may be coming
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ 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.
Learning Path
1Basic: user stories, feature specs, status updates
