From 1bffcbb09c15717e64cc933c906f4661a73c0381 Mon Sep 17 00:00:00 2001 From: Francy Lisboa Date: Sat, 18 Oct 2025 12:09:55 -0300 Subject: [PATCH] Update README with climate-anomalies-roye example MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add detailed example of climate-anomalies-roye skill created by agent-creator. Includes complete 5-phase breakdown, statistics, and usage examples. πŸ€– Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --- README.md | 129 ++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 81 insertions(+), 48 deletions(-) diff --git a/README.md b/README.md index 9c322ba..aabebfd 100644 --- a/README.md +++ b/README.md @@ -321,93 +321,126 @@ Phase 3-5: Implement everything --- -### Example 3: Climate (Real Case from Today!) +### Example 3: Climate Anomalies - Dominic RoyΓ© Style (Real Case from Today!) **πŸ‘€ Input:** ``` -"Create agent for climate analysis of Sorriso, Mato Grosso. -Need to analyze temperature and precipitation, historical trends." +"Create automated workflow for climate anomaly analysis following Dominic RoyΓ© methodology. +Same procedure, always, for anywhere in the world, finding updated raw data, +processing exactly as in the tutorial, and generating the same chart (same colors, +same quadrants, same logic)." ``` **πŸ€– Claude executed (5 autonomous phases):** ``` πŸ” PHASE 1: Climate API Research - Researched: INMET, Open-Meteo, OpenWeather, Visual Crossing - Compared: Coverage, cost, historical data - βœ“ DECIDED: Open-Meteo Historical Weather API - β€’ Free (no API key!) - β€’ Data since 1940 (84+ years) - β€’ ERA5 reanalysis (high quality) - β€’ REST API (easy integration) + Researched: Meteostat, Open-Meteo, ERA5, NOAA, Weather.gov + Compared: Coverage, cost, historical data, quality + βœ“ DECIDED: Multi-API Strategy + β€’ Primary: Meteostat (observational data) + β€’ Fallback: Open-Meteo (ERA5 reanalysis, global coverage) + β€’ Automatic failover for maximum reliability 🎨 PHASE 2: Analysis Design - Brainstorm: 15 typical questions - βœ“ DEFINED: 6 main analyses - 1. Basic time series - 2. Temporal comparison (YoY) - 3. Trend analysis (CAGR, regression) - 4. Anomaly detection (Z-score) - 5. Seasonal analysis (monthly climatology) - 6. Descriptive statistics + Based on Dominic RoyΓ© methodology: + βœ“ DEFINED: Climate Anomaly Scatter Plot + β€’ Axis X: Precipitation anomaly (%) + β€’ Axis Y: Temperature anomaly (Β°C) + β€’ 4 colored quadrants (wet-warm, dry-warm, wet-cold, dry-cold) + β€’ Seasonal analysis (DJF, MAM, JJA, SON) + β€’ Climate normal baseline (1991-2020 or 1981-2010) + β€’ Labeled extreme years with auto-repulsion πŸ—οΈ PHASE 3: Architecture - βœ“ STRUCTURED: + βœ“ STRUCTURED (modular design): scripts/ - - fetch_climate.py (320 lines) - - parse_climate.py (180 lines) - - analyze_climate.py (420 lines) + - climate_anomalies.py (580 lines) - Integrated pipeline + - fetch_climate.py (480 lines) - Multi-API client + - parse_climate.py (370 lines) - Seasonal aggregation + - plot_anomalies.py (350 lines) - Dominic RoyΓ© visualization utils/ - - cache_manager.py (350 lines) - - validators.py (450 lines) - - statistics.py (350 lines) - βœ“ Cache: Historical permanent, current year 24h - βœ“ Validations: Ranges, physical consistency, continuity + - api_factory.py (210 lines) - Factory pattern + - cache_manager.py (260 lines) - Smart cache (TTL by year) + - validators.py (200 lines) - Blocking validations + - context_validators.py (311 lines) - Context warnings + βœ“ Cache: Historical permanent, previous year 30d, current 24h + βœ“ Validations: 16 types (8 blocking + 8 contextual) 🎯 PHASE 4: Automatic Detection - βœ“ KEYWORDS: Sorriso, temperature, precipitation, rain, climate, - trend, historical, anomaly, compare - βœ“ DESCRIPTION: 200 words optimized + βœ“ KEYWORDS: Dominic RoyΓ©, climate anomalies, quadrants, + DJF MAM JJA SON, temperature precipitation, + wet-warm dry-warm wet-cold dry-cold + βœ“ DESCRIPTION: Optimized for multi-language detection βš™οΈ PHASE 5: Complete Implementation - βœ“ .claude-plugin/marketplace.json - βœ“ SKILL.md (6,800 words) - βœ“ Python scripts (2,070 lines) - βœ“ References (1,500 words) - βœ“ Configs (config.json, metadata.json) - βœ“ README.md + DECISIONS.md + βœ“ .claude-plugin/marketplace.json (validated) + βœ“ SKILL.md (7,000 words) + βœ“ Python scripts (2,761 lines) + βœ“ References (8,000 words) + - roye-methodology.md + - meteostat-guide.md + - open-meteo-guide.md + - troubleshooting.md + βœ“ Exact colors: #F7A699, #C23B33, #2C6CB0, #D4E3F3 + βœ“ Labeled points = HOLLOW circles (confirmed by reference image) + βœ“ README.md + CORRECTIONS.md + EXAMPLES.md -βœ… AGENT CREATED: ./climate-analysis-sorriso-mt/ +βœ… AGENT CREATED: ./climate-anomalies-roye/ ``` **πŸ“Š Statistics:** -- **Code:** 2,070 lines of Python -- **Documentation:** 13,600 words -- **Files:** 16 main files +- **Code:** 2,761 lines of Python (10 scripts) +- **Documentation:** 20,000+ words (12 files) +- **Files:** 24 main files - **Time:** ~90 minutes of autonomous creation +- **Corrections:** 3 critical fixes applied (v1.0.1) + +**🎨 Visual Output:** +Generates scatter plots identical to Dominic RoyΓ©'s methodology: +- Temperature anomaly vs Precipitation anomaly +- 4 colored quadrants (exact hex colors) +- Labeled extreme years (hollow circles) +- High quality: 11Γ—8 inches, 130 DPI **πŸ’» Installation of created agent:** ```bash # In terminal -cd climate-analysis-sorriso-mt +cd climate-anomalies-roye pip install -r requirements.txt # In Claude Code -/plugin marketplace add ./climate-analysis-sorriso-mt +/plugin marketplace add ./climate-anomalies-roye ``` **🎯 Using the created agent:** ``` -πŸ‘€ "What's the average temperature in Sorriso over the last 10 years?" +πŸ‘€ "Climate anomalies for Buenos Aires, summer season DJF" πŸ€– [Skill activates automatically] - [Fetches data from API] - [Analyzes and responds] + [Fetches data: Meteostat or Open-Meteo] + [Processes: seasonal aggregation, anomaly calculation] + [Validates: PHASE 2.5 - comprehensive context report] + [Generates: PNG chart in Dominic RoyΓ© style] + [Returns: Chart + interpretation with context] -πŸ‘€ "Rain trend in Sorriso since 1990" -πŸ€– [34-year trend analysis] - [Returns rate of change, significance, projection] +πŸ‘€ "Anomalies for Paris, winter DJF, baseline 1981-2010" +πŸ€– [Complete analysis with custom normal period] + [Chart shows extreme years labeled] + +Output files generated: + β€’ data/raw/location_daily.csv (raw data, for audit) + β€’ data/processed/location_season_normal.csv (climatology + anomalies) + β€’ data/out/location_season_normal.png (Dominic RoyΓ© chart) ✨ ``` +**πŸ›‘οΈ Quality Guarantees:** +- βœ… Multi-API with automatic fallback +- βœ… 16 validation layers (blocking + contextual) +- βœ… Users NEVER receive data without adequate context +- βœ… Automatic detection of climate change trends +- βœ… 100% reproducible (same inputs β†’ same outputs) +- βœ… Auditable (raw data saved for verification) + --- ## πŸ”„ How It Works: The 5 Autonomous Phases