Update README with climate-anomalies-roye example
Add detailed example of climate-anomalies-roye skill created by agent-creator. Includes complete 5-phase breakdown, statistics, and usage examples. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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README.md
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README.md
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@ -321,93 +321,126 @@ Phase 3-5: Implement everything
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---
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### Example 3: Climate (Real Case from Today!)
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### Example 3: Climate Anomalies - Dominic Royé Style (Real Case from Today!)
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**👤 Input:**
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```
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"Create agent for climate analysis of Sorriso, Mato Grosso.
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Need to analyze temperature and precipitation, historical trends."
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"Create automated workflow for climate anomaly analysis following Dominic Royé methodology.
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Same procedure, always, for anywhere in the world, finding updated raw data,
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processing exactly as in the tutorial, and generating the same chart (same colors,
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same quadrants, same logic)."
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```
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**🤖 Claude executed (5 autonomous phases):**
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```
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🔍 PHASE 1: Climate API Research
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Researched: INMET, Open-Meteo, OpenWeather, Visual Crossing
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Compared: Coverage, cost, historical data
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✓ DECIDED: Open-Meteo Historical Weather API
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• Free (no API key!)
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• Data since 1940 (84+ years)
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• ERA5 reanalysis (high quality)
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• REST API (easy integration)
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Researched: Meteostat, Open-Meteo, ERA5, NOAA, Weather.gov
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Compared: Coverage, cost, historical data, quality
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✓ DECIDED: Multi-API Strategy
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• Primary: Meteostat (observational data)
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• Fallback: Open-Meteo (ERA5 reanalysis, global coverage)
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• Automatic failover for maximum reliability
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🎨 PHASE 2: Analysis Design
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Brainstorm: 15 typical questions
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✓ DEFINED: 6 main analyses
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1. Basic time series
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2. Temporal comparison (YoY)
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3. Trend analysis (CAGR, regression)
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4. Anomaly detection (Z-score)
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5. Seasonal analysis (monthly climatology)
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6. Descriptive statistics
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Based on Dominic Royé methodology:
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✓ DEFINED: Climate Anomaly Scatter Plot
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• Axis X: Precipitation anomaly (%)
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• Axis Y: Temperature anomaly (°C)
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• 4 colored quadrants (wet-warm, dry-warm, wet-cold, dry-cold)
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• Seasonal analysis (DJF, MAM, JJA, SON)
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• Climate normal baseline (1991-2020 or 1981-2010)
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• Labeled extreme years with auto-repulsion
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🏗️ PHASE 3: Architecture
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✓ STRUCTURED:
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✓ STRUCTURED (modular design):
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scripts/
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- fetch_climate.py (320 lines)
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- parse_climate.py (180 lines)
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- analyze_climate.py (420 lines)
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- climate_anomalies.py (580 lines) - Integrated pipeline
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- fetch_climate.py (480 lines) - Multi-API client
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- parse_climate.py (370 lines) - Seasonal aggregation
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- plot_anomalies.py (350 lines) - Dominic Royé visualization
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utils/
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- cache_manager.py (350 lines)
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- validators.py (450 lines)
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- statistics.py (350 lines)
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✓ Cache: Historical permanent, current year 24h
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✓ Validations: Ranges, physical consistency, continuity
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- api_factory.py (210 lines) - Factory pattern
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- cache_manager.py (260 lines) - Smart cache (TTL by year)
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- validators.py (200 lines) - Blocking validations
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- context_validators.py (311 lines) - Context warnings
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✓ Cache: Historical permanent, previous year 30d, current 24h
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✓ Validations: 16 types (8 blocking + 8 contextual)
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🎯 PHASE 4: Automatic Detection
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✓ KEYWORDS: Sorriso, temperature, precipitation, rain, climate,
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trend, historical, anomaly, compare
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✓ DESCRIPTION: 200 words optimized
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✓ KEYWORDS: Dominic Royé, climate anomalies, quadrants,
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DJF MAM JJA SON, temperature precipitation,
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wet-warm dry-warm wet-cold dry-cold
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✓ DESCRIPTION: Optimized for multi-language detection
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⚙️ PHASE 5: Complete Implementation
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✓ .claude-plugin/marketplace.json
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✓ SKILL.md (6,800 words)
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✓ Python scripts (2,070 lines)
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✓ References (1,500 words)
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✓ Configs (config.json, metadata.json)
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✓ README.md + DECISIONS.md
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✓ .claude-plugin/marketplace.json (validated)
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✓ SKILL.md (7,000 words)
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✓ Python scripts (2,761 lines)
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✓ References (8,000 words)
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- roye-methodology.md
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- meteostat-guide.md
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- open-meteo-guide.md
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- troubleshooting.md
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✓ Exact colors: #F7A699, #C23B33, #2C6CB0, #D4E3F3
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✓ Labeled points = HOLLOW circles (confirmed by reference image)
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✓ README.md + CORRECTIONS.md + EXAMPLES.md
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✅ AGENT CREATED: ./climate-analysis-sorriso-mt/
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✅ AGENT CREATED: ./climate-anomalies-roye/
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```
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**📊 Statistics:**
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- **Code:** 2,070 lines of Python
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- **Documentation:** 13,600 words
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- **Files:** 16 main files
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- **Code:** 2,761 lines of Python (10 scripts)
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- **Documentation:** 20,000+ words (12 files)
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- **Files:** 24 main files
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- **Time:** ~90 minutes of autonomous creation
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- **Corrections:** 3 critical fixes applied (v1.0.1)
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**🎨 Visual Output:**
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Generates scatter plots identical to Dominic Royé's methodology:
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- Temperature anomaly vs Precipitation anomaly
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- 4 colored quadrants (exact hex colors)
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- Labeled extreme years (hollow circles)
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- High quality: 11×8 inches, 130 DPI
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**💻 Installation of created agent:**
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```bash
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# In terminal
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cd climate-analysis-sorriso-mt
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cd climate-anomalies-roye
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pip install -r requirements.txt
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# In Claude Code
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/plugin marketplace add ./climate-analysis-sorriso-mt
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/plugin marketplace add ./climate-anomalies-roye
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```
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**🎯 Using the created agent:**
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```
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👤 "What's the average temperature in Sorriso over the last 10 years?"
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👤 "Climate anomalies for Buenos Aires, summer season DJF"
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🤖 [Skill activates automatically]
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[Fetches data from API]
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[Analyzes and responds]
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[Fetches data: Meteostat or Open-Meteo]
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[Processes: seasonal aggregation, anomaly calculation]
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[Validates: PHASE 2.5 - comprehensive context report]
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[Generates: PNG chart in Dominic Royé style]
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[Returns: Chart + interpretation with context]
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👤 "Rain trend in Sorriso since 1990"
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🤖 [34-year trend analysis]
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[Returns rate of change, significance, projection]
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👤 "Anomalies for Paris, winter DJF, baseline 1981-2010"
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🤖 [Complete analysis with custom normal period]
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[Chart shows extreme years labeled]
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Output files generated:
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• data/raw/location_daily.csv (raw data, for audit)
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• data/processed/location_season_normal.csv (climatology + anomalies)
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• data/out/location_season_normal.png (Dominic Royé chart) ✨
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```
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**🛡️ Quality Guarantees:**
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- ✅ Multi-API with automatic fallback
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- ✅ 16 validation layers (blocking + contextual)
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- ✅ Users NEVER receive data without adequate context
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- ✅ Automatic detection of climate change trends
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- ✅ 100% reproducible (same inputs → same outputs)
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- ✅ Auditable (raw data saved for verification)
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---
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## 🔄 How It Works: The 5 Autonomous Phases
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