Agent Creator - Meta-Skill for Claude Code

Meta-skill that teaches Claude Code to create complete agents with Claude Skills in a fully autonomous way!

You describe a repetitive workflow → Claude creates a complete production-ready agent in 60-90 minutes.

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🎯 What It Is and What It Does

Problem It Solves

Creating a Claude Code agent manually is:

• ⏰ Time-consuming: 20-30 hours of work
• 🧠 Complex: Requires knowledge of APIs, Python, architecture
• 🔍 Labor-intensive: Research APIs, define analyses, structure, code, document

Solution: Agent-Creator

You do:

"Automate this workflow: every day I download crop data,
compare current year vs previous, takes 2 hours."

Claude Code does:

1. 🔍 Research available APIs → Decide the best one
2. 🎨 Define useful analyses → Prioritize by value
3. 🏗️ Structure project → Optimal architecture
4. 💻 Implement Python code → Functional, no TODOs
5. 📝 Write documentation → 12,000+ words
6. ⚙️ Create configs → Real values
7. 📦 Deliver agent → Production-ready in subdirectory

Result: Complete agent in ~90 minutes!

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🚀 Quick Installation

Prerequisites

• Claude Code CLI installed
• Python 3.8+ (for agents that will be created)

Step 1: Install in Claude Code

In the Claude Code terminal, run:

/plugin marketplace add FrancyJGLisboa/agent-creator-skill

Step 2: Verify Installation

/plugin list

You should see:

✓ agent-creator

Done! 🎉

The meta-skill is installed and ready to use.

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💡 How to Use (Tutorial Guide)

Basic Usage - Describe Your Workflow

In Claude Code, simply describe what you do repeatedly:

👤 You type:
"Automate this workflow: every day I download US crop data,
compare current year vs previous, rank states. Takes 2 hours."

🤖 Claude Code:

[Detects keyword: "automate"]
[Activates skill: agent-creator]
[Loads 5-phase protocol]

🔍 Phase 1: Researching US agriculture APIs...
   ✓ 5 options found
   ✓ Decided: USDA NASS QuickStats API
     Reason: Free, data since 1866, complete coverage

🎨 Phase 2: Defining analyses...
   ✓ 15 typical questions identified
   ✓ 5 main analyses: YoY, Ranking, Trends, Reports

🏗️ Phase 3: Structuring project...
   ✓ 3 scripts + 3 utils planned

🎯 Phase 4: Defining automatic detection...
   ✓ 50+ keywords identified

⚙️ Phase 5: Implementing code...
   ✓ SKILL.md (6,200 words)
   ✓ fetch_nass.py (280 lines)
   ✓ parse_nass.py (180 lines)
   ✓ analyze_nass.py (520 lines)
   ✓ utils/ (350 lines)
   ✓ references/ (5,000 words)
   ✓ .claude-plugin/marketplace.json ✅
   ✓ README.md (800 words)

✅ AGENT CREATED: ./usda-agriculture-agent/

🚀 To install:
/plugin marketplace add ./usda-agriculture-agent

What You Get

A complete directory:

./usda-agriculture-agent/
├── .claude-plugin/
│   └── marketplace.json         ← For Claude Code installation
├── SKILL.md                     ← Complete orchestration (6,000 words)
├── scripts/
│   ├── fetch_nass.py           ← API client (280 lines)
│   ├── parse_nass.py           ← Parsing & validation (180 lines)
│   ├── analyze_nass.py         ← Analyses (520 lines)
│   └── utils/
│       ├── cache_manager.py    ← Smart cache
│       ├── rate_limiter.py     ← Rate limiting
│       └── validators.py       ← Validations
├── references/
│   ├── api-guide.md            ← How to use the API
│   ├── analysis-methods.md     ← Detailed methodologies
│   └── troubleshooting.md      ← Problem solving
├── assets/
│   ├── config.json             ← Real configurations
│   └── metadata.json           ← Metadata
├── DECISIONS.md                 ← Decision justifications
└── README.md                    ← Usage instructions

Total: ~2,000 lines of code + ~12,000 words of documentation

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🔄 How It Works Internally (5 Phases)

PHASE 1: Discovery (API Research)

What it does:

• Research available public APIs for the domain
• Uses WebSearch and WebFetch to find options
• Compares APIs by: coverage, cost, rate limits, quality
• DECIDES autonomously which to use

Example (Agriculture):

WebSearch: "US agriculture API free historical data"
WebSearch: "USDA API documentation"
WebFetch: https://quickstats.nass.usda.gov/api

→ DECISION: USDA NASS QuickStats API
  Justification: Free, data since 1866, all crops

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PHASE 2: Design (Analysis Definition)

What it does:

• Brainstorm 10-15 typical user questions
• Group by analysis type
• DEFINES 4-6 priority analyses
• Specifies methodologies (formulas, interpretations)

Example:

Typical questions:
- "Corn production in 2023?"
- "Compare soybeans 2024 vs 2023"
- "Top 10 producing states"

→ ANALYSES DEFINED:
  1. YoY Comparison (year vs year)
  2. State Ranking (top producers)
  3. Trend Analysis (trends)
  4. Report Generation (reports)

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PHASE 3: Architecture (Structuring)

What it does:

• Defines folder and file structure
• Specifies responsibilities of each script
• Plans cache strategy and performance

Example:

→ STRUCTURE:
  scripts/
    - fetch_nass.py (API requests)
    - parse_nass.py (parsing)
    - analyze_nass.py (analyses)
    utils/
      - cache_manager.py
      - rate_limiter.py

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PHASE 4: Detection (Automatic Activation)

What it does:

• Lists domain keywords
• Determines when skill should activate
• Creates optimized description

Example:

→ KEYWORDS:
  Entities: USDA, NASS, agriculture
  Commodities: corn, soybeans, wheat
  Metrics: production, area, yield
  Actions: compare, ranking, trend

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PHASE 5: Implementation (Complete Code)

What it does:

• Creates marketplace.json (REQUIRED!)
• Implements functional Python code
• Writes SKILL.md (5000+ words)
• Creates references with useful content
• Generates real configs

Commands executed internally:

mkdir -p agent-name/{scripts/utils,references,assets,.claude-plugin}
# Write: .claude-plugin/marketplace.json
# Write: SKILL.md
# Write: scripts/fetch_*.py
# Write: scripts/parse_*.py
# Write: scripts/analyze_*.py
# Write: utils/cache_manager.py
# Write: references/*.md
# Write: assets/*.json
# Write: README.md

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📖 Practical Usage Examples

Example 1: Agriculture (USDA)

👤 Input in Claude Code:

"Automate: every day I download US crop data,
compare current year vs previous, rank states.
Takes 2 hours."

🤖 Claude executes (autonomously):

Phase 1: Research → USDA NASS API
Phase 2: Define → YoY, Ranking, Trends
Phase 3: Structure → 3 scripts + 2 utils
Phase 4: Keywords → USDA, corn, soybeans, production
Phase 5: Implement → 1,410 lines of code

✅ Agent created: ./usda-agriculture-agent/

💻 How to install the created agent:

# In Claude Code
/plugin marketplace add ./usda-agriculture-agent

🎯 How to use:

"US corn production in 2023"
"Compare soybeans this year vs last year"
"Top 10 wheat producing states"

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Example 2: Finance (Stocks)

👤 Input:

"Need agent for stock analysis:
fetch prices, calculate RSI and MACD,
compare with benchmarks. Daily, 3h."

🤖 Claude creates:

Phase 1: Alpha Vantage API (or Yahoo Finance)
Phase 2: Price fetching, RSI, MACD, Benchmarking
Phase 3-5: Implement everything

✅ Created: ./stock-technical-analysis-agent/

💻 Install:

/plugin marketplace add ./stock-technical-analysis-agent

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Example 3: Climate (Real Case from Today!)

👤 Input:

"Create agent for climate analysis of Sorriso, Mato Grosso.
Need to analyze temperature and precipitation, historical trends."

🤖 Claude executed (5 autonomous phases):

🔍 PHASE 1: Climate API Research
   Researched: INMET, Open-Meteo, OpenWeather, Visual Crossing
   Compared: Coverage, cost, historical data
   ✓ DECIDED: Open-Meteo Historical Weather API
     • Free (no API key!)
     • Data since 1940 (84+ years)
     • ERA5 reanalysis (high quality)
     • REST API (easy integration)

🎨 PHASE 2: Analysis Design
   Brainstorm: 15 typical questions
   ✓ 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

🏗️ PHASE 3: Architecture
   ✓ STRUCTURED:
     scripts/
       - fetch_climate.py (320 lines)
       - parse_climate.py (180 lines)
       - analyze_climate.py (420 lines)
     utils/
       - cache_manager.py (350 lines)
       - validators.py (450 lines)
       - statistics.py (350 lines)
   ✓ Cache: Historical permanent, current year 24h
   ✓ Validations: Ranges, physical consistency, continuity

🎯 PHASE 4: Automatic Detection
   ✓ KEYWORDS: Sorriso, temperature, precipitation, rain, climate,
               trend, historical, anomaly, compare
   ✓ DESCRIPTION: 200 words optimized

⚙️ PHASE 5: Complete Implementation
   ✓ .claude-plugin/marketplace.json
   ✓ SKILL.md (6,800 words)
   ✓ Python scripts (2,070 lines)
   ✓ References (1,500 words)
   ✓ Configs (config.json, metadata.json)
   ✓ README.md + DECISIONS.md

✅ AGENT CREATED: ./climate-analysis-sorriso-mt/

📊 Statistics:

• Code: 2,070 lines of Python
• Documentation: 13,600 words
• Files: 16 main files
• Time: ~90 minutes of autonomous creation

💻 Installation of created agent:

# In terminal
cd climate-analysis-sorriso-mt
pip install -r requirements.txt

# In Claude Code
/plugin marketplace add ./climate-analysis-sorriso-mt

🎯 Using the created agent:

👤 "What's the average temperature in Sorriso over the last 10 years?"
🤖 [Skill activates automatically]
   [Fetches data from API]
   [Analyzes and responds]

👤 "Rain trend in Sorriso since 1990"
🤖 [34-year trend analysis]
   [Returns rate of change, significance, projection]

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🔄 How It Works: The 5 Autonomous Phases

PHASE 1: DISCOVERY (API Research)

Objective: DECIDE which API to use

Process:

1. Identifies domain (agriculture? finance? climate?)
2. Research available public APIs
3. Compares options (coverage, cost, quality)
4. DECIDES with justification
5. Documents decision

Autonomy: Claude decides without asking the user!

Example of internal commands:

# Claude executes internally:
WebSearch: "US agriculture API free historical data"
WebFetch: https://quickstats.nass.usda.gov/api
# Compares: NASS vs ERS vs FAO
# → DECISION: NASS (best option)

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PHASE 2: DESIGN (Analysis Definition)

Objective: DEFINE which analyses to implement

Process:

1. Brainstorm typical questions (10-15)
2. Group by type (comparisons, rankings, trends)
3. DEFINES 4-6 priority analyses
4. Specifies methodologies (mathematical formulas)

Autonomy: Claude prioritizes by value and frequency of use!

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PHASE 3: ARCHITECTURE (Structuring)

Objective: STRUCTURE the project optimally

Process:

1. Defines folder structure
2. Specifies scripts and responsibilities
3. Plans cache strategy
4. Defines validations

Autonomy: Claude chooses optimal architecture based on complexity!

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PHASE 4: DETECTION (Automatic Activation)

Objective: DETERMINE keywords for detection

Process:

1. Lists domain entities
2. Lists typical actions
3. Determines keywords
4. Creates optimized description (150-250 words)

Result: Skill activates automatically when user asks relevant question!

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PHASE 5: IMPLEMENTATION (Complete Code)

Objective: IMPLEMENT everything with REAL code

Process:

1. mkdir -p agent-name/{scripts/utils,references,assets,.claude-plugin}
2. Write: .claude-plugin/marketplace.json  ← REQUIRED!
3. Write: SKILL.md (5000+ words)
4. Write: scripts/*.py (functional code)
5. Write: utils/*.py (cache, validators, etc)
6. Write: references/*.md (useful content)
7. Write: assets/*.json (real configs)
8. Write: README.md + DECISIONS.md

Quality Standards:

• ✅ Complete code (no TODO or pass)
• ✅ Detailed docstrings
• ✅ Robust error handling
• ✅ Type hints
• ✅ Comprehensive validations

Result: Production-ready agent!

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📝 Step-by-Step Commands

1. Create an Agent

In Claude Code:

👤 "Create an agent for [objective]"

OR

👤 "Automate this workflow: [description]"

2. Wait for Creation

Claude executes the 5 phases autonomously (~60-90 min)

3. Install Created Agent

In terminal:

# Go to agent directory
cd ./created-agent-name/

# Install Python dependencies
pip install -r requirements.txt

# If API key needed (follow instructions in README)
export API_KEY_VAR="your_key_here"

In Claude Code:

# Install skill
/plugin marketplace add ./created-agent-name

# Verify installation
/plugin list

4. Use the Agent

In Claude Code:

👤 Ask questions related to the domain
🤖 Skill activates automatically and responds

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🎯 ROI (Return on Investment)

Metric	Manual	With Agent-Creator	Savings
Time	20-30 hours	1.5 hours	15-20x
Required knowledge	APIs, Python, Architecture	None	100%
Code written	By you	By Claude	100%
Quality	Variable	Production-ready	High

But the best part: You do nothing, just describe the workflow! 🎉

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📚 Complete Documentation

This repository includes detailed guides in Portuguese:

1. como-compartilhar-skills.md

   • How to publish your skills
   • GitHub, ZIP, Claude.ai
   • Best practices

2. guia-completo-claude-skills.md

   • Complete guide about Claude Skills
   • Technical specifications
   • Examples

3. como_instalar_agente_creator.md

   • Detailed installation instructions
   • Troubleshooting

4. meta-prompt-autonomo-criacao-agentes.md

   • Meta-prompt for agent creation
   • Universal annotated template
   • Quality checklist

5. scripts-vs-skills-guia-didatico.md

   • Didactic comparison Scripts vs Skills
   • When to use each approach

6. agent-creator/README.md

   • Meta-skill documentation
   • Technical details

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💡 Use Cases

Agriculture

"Create agent for Brazilian crop analysis via CONAB"
→ Agent with TXT parsing, YoY analyses, regional rankings

Finance

"Automate daily stock analysis: prices, RSI, MACD"
→ Agent with technical indicators, alerts, comparisons

Climate

"Climate analysis of Sorriso-MT: temperature, rain, trends"
→ Agent with data since 1940, 6 types of analyses

Economy

"Agent for World Bank economic indicators"
→ Agent with GDP, inflation, country comparisons

Any domain with API or structured data!

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🛠️ Useful Commands

Check Installed Skills

# In Claude Code
/plugin list

Install Agent-Creator

# In Claude Code
/plugin marketplace add FrancyJGLisboa/agent-creator-skill

Create an Agent

# In Claude Code (natural language)
"Create an agent for [objective]"
"Automate workflow of [description]"

Install Created Agent

# Terminal
cd ./created-agent/
pip install -r requirements.txt

# Claude Code
/plugin marketplace add ./created-agent

Use Agent

# In Claude Code (natural language)
Ask questions related to the agent's domain

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⚙️ Technical Requirements

To Use Agent-Creator

• Claude Code CLI installed
• Internet connection (for API research)

For Created Agents

• Python 3.8+
• pip (to install dependencies)
• Specific dependencies (listed in requirements.txt of each agent)
• API key (if chosen API requires - instructions in agent's README)

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🎓 Understanding the Output

Main Files Created

.claude-plugin/marketplace.json

• Configuration for Claude Code installation
• CRITICAL: Without it, skill cannot be installed

SKILL.md

• Complete skill orchestration
• Detailed workflows
• Analysis documentation
• ~5000-7000 words

scripts/

• Functional Python code
• Separated by responsibility (fetch, parse, analyze)
• Reusable utils (cache, validators)
• ~1500-2000 lines total

references/

• Technical guides (API docs, methodologies)
• Troubleshooting
• Domain knowledge
• ~5000 words

README.md

• Installation instructions
• Usage examples
• Troubleshooting

DECISIONS.md

• Justifications for all decisions
• Which API chosen and why
• Which analyses and why
• Trade-offs considered

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⭐ Features

• ✅ Total Autonomy: Claude decides everything
• ✅ Production-Ready: Functional code, no TODOs
• ✅ Complete Documentation: 10,000+ words
• ✅ Smart Cache: TTL based on data type
• ✅ Robust Validations: Guaranteed data quality
• ✅ Error Handling: Retry, fallbacks, clear messages
• ✅ Marketplace.json: Guaranteed Claude Code installation

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🚧 Limitations

DO NOT use for:

• ❌ Editing existing skills (edit directly)
• ❌ Debugging skills (debug directly)
• ❌ Asking questions about skills (ask directly)

USE ONLY for:

• ✅ Creating new agents from scratch
• ✅ Automating repetitive workflows

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🤝 Contributing

Contributions are welcome!

1. Fork this repository
2. Create a branch (git checkout -b feature/improvement)
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

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📄 License

Apache 2.0 (same license as Anthropic's official skills)

Free to use, modify, and distribute.

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🙏 Credits

Inspired by:

• Anthropic Agent Skills Spec
• skill-creator skill

Differentiator: Total autonomy - Claude decides everything, not just executes instructions.

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📊 Repository Statistics

Agent-Creator Meta-Skill:

• 8 main files
• ~5,000 words in SKILL.md
• 6 detailed references
• 5-phase autonomous protocol

Documentation:

• 5 complete guides in Portuguese
• ~150 KB of documentation
• Complete coverage of Claude Skills ecosystem

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🌟 Examples of Agents Created with Agent-Creator

1. USDA Agriculture Agent

• API: USDA NASS
• Analyses: YoY, Ranking, Trends
• Output: 1,410 lines of code

2. Climate Analysis Sorriso-MT (created today!)

• API: Open-Meteo
• Analyses: 6 types (series, trends, anomalies, etc.)
• Output: 2,070 lines of code

All created autonomously by the meta-skill!

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📞 Support

Issues: https://github.com/FrancyJGLisboa/agent-creator-skill/issues Discussions: https://github.com/FrancyJGLisboa/agent-creator-skill/discussions

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🚀 Quick Start

# 1. Install agent-creator
/plugin marketplace add FrancyJGLisboa/agent-creator-skill

# 2. Create an agent (in Claude Code)
"Automate workflow for analyzing [your domain]"

# 3. Wait for creation (~60-90 min)

# 4. Install created agent
/plugin marketplace add ./created-agent

# 5. Use it!
"[Ask domain questions]"

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Start automating today! Transform repetitive workflows into powerful agents! 🚀

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Version: 1.0.0 Updated: October 2025 Author: Created with Claude Code Repository: https://github.com/FrancyJGLisboa/agent-creator-skill