# Coding Open Agent Tools
**Deterministic code validation and analysis toolkit for AI agents - Save tokens, prevent errors**
This project provides **parsing, validation, and analysis tools** that save agent tokens by handling deterministic operations agents struggle with or waste excessive tokens on. It complements [basic-open-agent-tools](https://github.com/Open-Agent-Tools/basic-open-agent-tools) by providing higher-level code analysis capabilities.
## 🎯 Core Philosophy: Token Efficiency
**We focus on what agents waste tokens on:**
- ✅ **Validators** - Catch syntax/type errors before execution (prevents retry loops)
- ✅ **Parsers** - Convert unstructured → structured (AST, tool output, logs)
- ✅ **Extractors** - Pull specific data from complex sources (tedious for agents)
- ✅ **Formatters** - Apply deterministic rules (escaping, quoting, import sorting)
- ✅ **Scanners** - Rule-based pattern detection (secrets, anti-patterns, security)
**We avoid duplicating what agents do well:**
- ❌ Full code generation (agents excel at creative logic)
- ❌ Architecture decisions (requires judgment and context)
- ❌ Code refactoring (agents reason through transformations)
- ❌ Project scaffolding (agents use examples effectively)
## Project Status
✅ **v0.1.0-beta Released** - First beta with 39 migrated developer-focused tools from basic-open-agent-tools.
**What's Available Now:**
- ✅ Analysis Module (14 functions) - AST parsing, complexity analysis, imports, secrets
- ✅ Git Module (9 functions) - Read-only git operations
- ✅ Profiling Module (8 functions) - Performance and memory profiling
- ✅ Quality Module (7 functions) - Static analysis parsers
- ✅ Shell Module (13 functions) - Shell validation, security scanning, escaping
- ✅ Python Module (15 functions) - Syntax validation, type checking, import analysis
- ✅ **Database Module (16 functions)** - SQLite operations, safe query building, migration helpers
**Coming Next (Focused on Validation, NOT Generation):**
- 🚧 Git enhancement module (v0.3.1) - 60+ additional git operations
- 🚧 Config validation module (v0.4.0) - YAML/TOML/JSON validation, secret scanning
See [docs/ROADMAP.md](./docs/ROADMAP.md) and [docs/PRD](./docs/PRD/) for detailed plans.
## Relationship to Basic Open Agent Tools
### Division of Responsibilities
**[basic-open-agent-tools](https://github.com/Open-Agent-Tools/basic-open-agent-tools)** (Foundation Layer):
- Core file system operations
- Text and data processing
- Document format handling (PDF, Word, Excel, PowerPoint, etc.)
- System utilities and network operations
- General-purpose, low-level operations
- 200+ foundational agent tools
**coding-open-agent-tools** (Development Layer):
- Code generation and scaffolding
- Shell script creation and validation
- Project structure generation
- Development workflow automation
- Language-specific tooling
- Security analysis for generated code
### Dependency Model
```
coding-open-agent-tools (this project)
└─> basic-open-agent-tools (dependency)
└─> Python stdlib (minimal external dependencies)
```
This project will **depend on** `basic-open-agent-tools` for file operations, text processing, and other foundational capabilities, while providing specialized code generation features.
## Planned Modules (v0.1.0)
### 1. Shell Script Generation Module (~15 functions)
Generate, validate, and analyze shell scripts for deployment, CI/CD, and system administration:
- **Generation**: Bash scripts, systemd services, cron jobs, Docker entrypoints, CI pipelines
- **Validation**: Syntax checking, dependency analysis, security scanning
- **Utilities**: Argument escaping, permission handling, documentation generation
**Example**:
```python
import coding_open_agent_tools as coat
script = coat.generate_bash_script(
commands=["cd /app", "git pull", "npm install", "npm run build"],
variables={"NODE_ENV": "production"},
add_error_handling=True,
add_logging=True,
set_flags=["u", "o pipefail"]
)
# Validate before using
validation = coat.validate_shell_syntax(script, "bash")
security = coat.analyze_shell_security(script)
```
### 2. Python Code Generation Module (~18 functions)
Generate high-quality Python code with type hints, docstrings, and tests:
- **Functions**: Sync/async functions, lambdas with full type annotations
- **Classes**: Regular classes, dataclasses, Pydantic models, exceptions
- **Documentation**: Google/NumPy/Sphinx docstrings, module headers
- **Tests**: Pytest skeletons, fixtures, test classes
- **Projects**: Complete project scaffolding, pyproject.toml, README, .gitignore
**Example**:
```python
import coding_open_agent_tools as coat
func = coat.generate_python_function(
name="process_data",
parameters=[
{"name": "data", "type": "list[dict[str, str]]", "description": "Input data"},
{"name": "operation", "type": "str", "description": "Operation type"}
],
return_type="dict[str, str]",
description="Process data with specified operation",
docstring_style="google",
add_type_checking=True,
add_error_handling=True,
raises=[
{"type": "TypeError", "description": "If parameters are wrong type"},
{"type": "ValueError", "description": "If operation is not supported"}
]
)
```
## Design Philosophy
### Same Principles as Basic Tools
1. **Minimal Dependencies**: Prefer stdlib, add dependencies only when substantial value added
2. **Google ADK Compliance**: All functions use JSON-serializable types, no default parameters
3. **Local Operations**: No HTTP/API calls, focus on local development tasks
4. **Type Safety**: Full mypy compliance with comprehensive type hints
5. **High Quality**: 100% ruff compliance, comprehensive testing (80%+ coverage)
6. **Agent-First Design**: Functions designed for LLM comprehension and use
7. **Smart Confirmation**: 3-mode confirmation system (bypass/interactive/agent) for write/delete operations
### Additional Focus Areas
1. **Code Quality**: Generate code that follows best practices (PEP 8, type hints)
2. **Security**: Built-in security analysis and validation for generated scripts
3. **Template-Driven**: Extensive template library for common patterns
4. **Validation**: Syntax checking and error detection before execution
5. **Self-Documenting**: All generated code includes comprehensive documentation
## Target Use Cases
### For AI Agents
- **Project Scaffolding**: Create new projects with proper structure
- **Boilerplate Reduction**: Generate repetitive code structures
- **Script Automation**: Create deployment and maintenance scripts
- **Test Generation**: Scaffold comprehensive test coverage
- **Documentation**: Generate consistent docstrings and README files
### For Developers
- **Agent Development**: Build agents that generate code
- **Automation Engineering**: Create development workflow automation
- **DevOps**: Generate deployment scripts and service configurations
- **Framework Building**: Integrate code generation into frameworks
## Integration Example
```python
import coding_open_agent_tools as coat
from basic_open_agent_tools import file_system
# Generate code using coding tools
code = coat.generate_python_function(...)
# Validate the generated code
validation = coat.validate_python_syntax(code)
if validation['is_valid'] == 'true':
# Write to file using basic tools
file_system.write_file_from_string(
file_path="/path/to/output.py",
content=code,
skip_confirm=False
)
```
## Safety Features
### Smart Confirmation System (3 Modes)
The confirmation module provides intelligent confirmation handling for future write/delete operations:
**🔄 Bypass Mode** - `skip_confirm=True` or `BYPASS_TOOL_CONSENT=true` env var
- Proceeds immediately without prompts
- Perfect for CI/CD and automation
**💬 Interactive Mode** - Terminal with `skip_confirm=False`
- Prompts user with `y/n` confirmation
- Shows preview info (file sizes, etc.)
**🤖 Agent Mode** - Non-TTY with `skip_confirm=False`
- Raises `CONFIRMATION_REQUIRED` error with instructions
- LLM agents can ask user and retry with `skip_confirm=True`
```python
from coding_open_agent_tools.confirmation import check_user_confirmation
# Safe by default - adapts to context
confirmed = check_user_confirmation(
operation="overwrite file",
target="/path/to/file.py",
skip_confirm=False, # Interactive prompt OR agent error
preview_info="1024 bytes"
)
# Automation mode
import os
os.environ['BYPASS_TOOL_CONSENT'] = 'true'
# All confirmations bypassed for CI/CD
```
**Note**: Current modules (analysis, git, profiling, quality) are read-only and don't require confirmations. The confirmation system is ready for future write/delete operations in planned modules (shell generation, code generation, etc.).
## Documentation
- **[Product Requirements Documents](./docs/PRD/)**: Detailed specifications
- [Project Overview](./docs/PRD/01-project-overview.md)
- [Shell Module PRD](./docs/PRD/02-shell-module-prd.md)
- [Codegen Module PRD](./docs/PRD/03-codegen-module-prd.md)
## Installation
```bash
# Install latest beta from source
git clone https://github.com/Open-Agent-Tools/coding-open-agent-tools.git
cd coding-open-agent-tools
pip install -e ".[dev]"
# Or install specific version (when published to PyPI)
pip install coding-open-agent-tools==0.1.0-beta
# This will automatically install basic-open-agent-tools as a dependency
```
## Quick Start
```python
import coding_open_agent_tools as coat
# Load all 38 functions
all_tools = coat.load_all_tools()
# Or load by category
analysis_tools = coat.load_all_analysis_tools() # 14 functions
git_tools = coat.load_all_git_tools() # 9 functions
profiling_tools = coat.load_all_profiling_tools() # 8 functions
quality_tools = coat.load_all_quality_tools() # 7 functions
# Use with any agent framework
from google.adk.agents import Agent
agent = Agent(
tools=all_tools,
name="CodeAnalyzer",
instruction="Analyze code quality and performance"
)
# Example: Analyze code complexity
from coding_open_agent_tools import analysis
complexity = analysis.calculate_complexity("/path/to/code.py")
print(f"Cyclomatic complexity: {complexity['total_complexity']}")
# Example: Check git status
from coding_open_agent_tools import git
status = git.get_git_status("/path/to/repo")
print(f"Modified files: {len(status['modified'])}")
```
## Development Status
**Current Phase**: Planning and Requirements
**Next Steps**:
1. Initialize repository structure
2. Set up development environment
3. Implement Shell Script Generation Module (v0.1.0)
4. Implement Python Code Generation Module (v0.2.0)
## Quality Standards
- **Code Quality**: 100% ruff compliance (linting + formatting)
- **Type Safety**: 100% mypy compliance
- **Test Coverage**: Minimum 80% for all modules
- **Google ADK Compliance**: All function signatures compatible with agent frameworks
- **Security**: All generated code scanned for vulnerabilities
## Contributing (Future)
Contributions will be welcome once the initial implementation is complete. We will provide:
- Contribution guidelines
- Code of conduct
- Development setup instructions
- Testing requirements
## License
MIT License (same as basic-open-agent-tools)
## Related Projects
- **[basic-open-agent-tools](https://github.com/Open-Agent-Tools/basic-open-agent-tools)** - Foundational toolkit for AI agents
- **[Google ADK](https://github.com/google/agent-development-kit)** - Agent Development Kit
- **[Strands Agents](https://github.com/strands-ai/strands)** - Agent framework
---
**Status**: 🚧 Planning Phase
**Version**: 0.0.0 (not yet released)
**Last Updated**: 2025-10-14
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"description": "# Coding Open Agent Tools\n\n**Deterministic code validation and analysis toolkit for AI agents - Save tokens, prevent errors**\n\nThis project provides **parsing, validation, and analysis tools** that save agent tokens by handling deterministic operations agents struggle with or waste excessive tokens on. It complements [basic-open-agent-tools](https://github.com/Open-Agent-Tools/basic-open-agent-tools) by providing higher-level code analysis capabilities.\n\n## \ud83c\udfaf Core Philosophy: Token Efficiency\n\n**We focus on what agents waste tokens on:**\n- \u2705 **Validators** - Catch syntax/type errors before execution (prevents retry loops)\n- \u2705 **Parsers** - Convert unstructured \u2192 structured (AST, tool output, logs)\n- \u2705 **Extractors** - Pull specific data from complex sources (tedious for agents)\n- \u2705 **Formatters** - Apply deterministic rules (escaping, quoting, import sorting)\n- \u2705 **Scanners** - Rule-based pattern detection (secrets, anti-patterns, security)\n\n**We avoid duplicating what agents do well:**\n- \u274c Full code generation (agents excel at creative logic)\n- \u274c Architecture decisions (requires judgment and context)\n- \u274c Code refactoring (agents reason through transformations)\n- \u274c Project scaffolding (agents use examples effectively)\n\n## Project Status\n\n\u2705 **v0.1.0-beta Released** - First beta with 39 migrated developer-focused tools from basic-open-agent-tools.\n\n**What's Available Now:**\n- \u2705 Analysis Module (14 functions) - AST parsing, complexity analysis, imports, secrets\n- \u2705 Git Module (9 functions) - Read-only git operations\n- \u2705 Profiling Module (8 functions) - Performance and memory profiling\n- \u2705 Quality Module (7 functions) - Static analysis parsers\n- \u2705 Shell Module (13 functions) - Shell validation, security scanning, escaping\n- \u2705 Python Module (15 functions) - Syntax validation, type checking, import analysis\n- \u2705 **Database Module (16 functions)** - SQLite operations, safe query building, migration helpers\n\n**Coming Next (Focused on Validation, NOT Generation):**\n- \ud83d\udea7 Git enhancement module (v0.3.1) - 60+ additional git operations\n- \ud83d\udea7 Config validation module (v0.4.0) - YAML/TOML/JSON validation, secret scanning\n\nSee [docs/ROADMAP.md](./docs/ROADMAP.md) and [docs/PRD](./docs/PRD/) for detailed plans.\n\n## Relationship to Basic Open Agent Tools\n\n### Division of Responsibilities\n\n**[basic-open-agent-tools](https://github.com/Open-Agent-Tools/basic-open-agent-tools)** (Foundation Layer):\n- Core file system operations\n- Text and data processing\n- Document format handling (PDF, Word, Excel, PowerPoint, etc.)\n- System utilities and network operations\n- General-purpose, low-level operations\n- 200+ foundational agent tools\n\n**coding-open-agent-tools** (Development Layer):\n- Code generation and scaffolding\n- Shell script creation and validation\n- Project structure generation\n- Development workflow automation\n- Language-specific tooling\n- Security analysis for generated code\n\n### Dependency Model\n\n```\ncoding-open-agent-tools (this project)\n \u2514\u2500> basic-open-agent-tools (dependency)\n \u2514\u2500> Python stdlib (minimal external dependencies)\n```\n\nThis project will **depend on** `basic-open-agent-tools` for file operations, text processing, and other foundational capabilities, while providing specialized code generation features.\n\n## Planned Modules (v0.1.0)\n\n### 1. Shell Script Generation Module (~15 functions)\nGenerate, validate, and analyze shell scripts for deployment, CI/CD, and system administration:\n\n- **Generation**: Bash scripts, systemd services, cron jobs, Docker entrypoints, CI pipelines\n- **Validation**: Syntax checking, dependency analysis, security scanning\n- **Utilities**: Argument escaping, permission handling, documentation generation\n\n**Example**:\n```python\nimport coding_open_agent_tools as coat\n\nscript = coat.generate_bash_script(\n commands=[\"cd /app\", \"git pull\", \"npm install\", \"npm run build\"],\n variables={\"NODE_ENV\": \"production\"},\n add_error_handling=True,\n add_logging=True,\n set_flags=[\"u\", \"o pipefail\"]\n)\n\n# Validate before using\nvalidation = coat.validate_shell_syntax(script, \"bash\")\nsecurity = coat.analyze_shell_security(script)\n```\n\n### 2. Python Code Generation Module (~18 functions)\nGenerate high-quality Python code with type hints, docstrings, and tests:\n\n- **Functions**: Sync/async functions, lambdas with full type annotations\n- **Classes**: Regular classes, dataclasses, Pydantic models, exceptions\n- **Documentation**: Google/NumPy/Sphinx docstrings, module headers\n- **Tests**: Pytest skeletons, fixtures, test classes\n- **Projects**: Complete project scaffolding, pyproject.toml, README, .gitignore\n\n**Example**:\n```python\nimport coding_open_agent_tools as coat\n\nfunc = coat.generate_python_function(\n name=\"process_data\",\n parameters=[\n {\"name\": \"data\", \"type\": \"list[dict[str, str]]\", \"description\": \"Input data\"},\n {\"name\": \"operation\", \"type\": \"str\", \"description\": \"Operation type\"}\n ],\n return_type=\"dict[str, str]\",\n description=\"Process data with specified operation\",\n docstring_style=\"google\",\n add_type_checking=True,\n add_error_handling=True,\n raises=[\n {\"type\": \"TypeError\", \"description\": \"If parameters are wrong type\"},\n {\"type\": \"ValueError\", \"description\": \"If operation is not supported\"}\n ]\n)\n```\n\n## Design Philosophy\n\n### Same Principles as Basic Tools\n\n1. **Minimal Dependencies**: Prefer stdlib, add dependencies only when substantial value added\n2. **Google ADK Compliance**: All functions use JSON-serializable types, no default parameters\n3. **Local Operations**: No HTTP/API calls, focus on local development tasks\n4. **Type Safety**: Full mypy compliance with comprehensive type hints\n5. **High Quality**: 100% ruff compliance, comprehensive testing (80%+ coverage)\n6. **Agent-First Design**: Functions designed for LLM comprehension and use\n7. **Smart Confirmation**: 3-mode confirmation system (bypass/interactive/agent) for write/delete operations\n\n### Additional Focus Areas\n\n1. **Code Quality**: Generate code that follows best practices (PEP 8, type hints)\n2. **Security**: Built-in security analysis and validation for generated scripts\n3. **Template-Driven**: Extensive template library for common patterns\n4. **Validation**: Syntax checking and error detection before execution\n5. **Self-Documenting**: All generated code includes comprehensive documentation\n\n## Target Use Cases\n\n### For AI Agents\n- **Project Scaffolding**: Create new projects with proper structure\n- **Boilerplate Reduction**: Generate repetitive code structures\n- **Script Automation**: Create deployment and maintenance scripts\n- **Test Generation**: Scaffold comprehensive test coverage\n- **Documentation**: Generate consistent docstrings and README files\n\n### For Developers\n- **Agent Development**: Build agents that generate code\n- **Automation Engineering**: Create development workflow automation\n- **DevOps**: Generate deployment scripts and service configurations\n- **Framework Building**: Integrate code generation into frameworks\n\n## Integration Example\n\n```python\nimport coding_open_agent_tools as coat\nfrom basic_open_agent_tools import file_system\n\n# Generate code using coding tools\ncode = coat.generate_python_function(...)\n\n# Validate the generated code\nvalidation = coat.validate_python_syntax(code)\n\nif validation['is_valid'] == 'true':\n # Write to file using basic tools\n file_system.write_file_from_string(\n file_path=\"/path/to/output.py\",\n content=code,\n skip_confirm=False\n )\n```\n\n## Safety Features\n\n### Smart Confirmation System (3 Modes)\n\nThe confirmation module provides intelligent confirmation handling for future write/delete operations:\n\n**\ud83d\udd04 Bypass Mode** - `skip_confirm=True` or `BYPASS_TOOL_CONSENT=true` env var\n- Proceeds immediately without prompts\n- Perfect for CI/CD and automation\n\n**\ud83d\udcac Interactive Mode** - Terminal with `skip_confirm=False`\n- Prompts user with `y/n` confirmation\n- Shows preview info (file sizes, etc.)\n\n**\ud83e\udd16 Agent Mode** - Non-TTY with `skip_confirm=False`\n- Raises `CONFIRMATION_REQUIRED` error with instructions\n- LLM agents can ask user and retry with `skip_confirm=True`\n\n```python\nfrom coding_open_agent_tools.confirmation import check_user_confirmation\n\n# Safe by default - adapts to context\nconfirmed = check_user_confirmation(\n operation=\"overwrite file\",\n target=\"/path/to/file.py\",\n skip_confirm=False, # Interactive prompt OR agent error\n preview_info=\"1024 bytes\"\n)\n\n# Automation mode\nimport os\nos.environ['BYPASS_TOOL_CONSENT'] = 'true'\n# All confirmations bypassed for CI/CD\n```\n\n**Note**: Current modules (analysis, git, profiling, quality) are read-only and don't require confirmations. The confirmation system is ready for future write/delete operations in planned modules (shell generation, code generation, etc.).\n\n## Documentation\n\n- **[Product Requirements Documents](./docs/PRD/)**: Detailed specifications\n - [Project Overview](./docs/PRD/01-project-overview.md)\n - [Shell Module PRD](./docs/PRD/02-shell-module-prd.md)\n - [Codegen Module PRD](./docs/PRD/03-codegen-module-prd.md)\n\n## Installation\n\n```bash\n# Install latest beta from source\ngit clone https://github.com/Open-Agent-Tools/coding-open-agent-tools.git\ncd coding-open-agent-tools\npip install -e \".[dev]\"\n\n# Or install specific version (when published to PyPI)\npip install coding-open-agent-tools==0.1.0-beta\n\n# This will automatically install basic-open-agent-tools as a dependency\n```\n\n## Quick Start\n\n```python\nimport coding_open_agent_tools as coat\n\n# Load all 38 functions\nall_tools = coat.load_all_tools()\n\n# Or load by category\nanalysis_tools = coat.load_all_analysis_tools() # 14 functions\ngit_tools = coat.load_all_git_tools() # 9 functions\nprofiling_tools = coat.load_all_profiling_tools() # 8 functions\nquality_tools = coat.load_all_quality_tools() # 7 functions\n\n# Use with any agent framework\nfrom google.adk.agents import Agent\n\nagent = Agent(\n tools=all_tools,\n name=\"CodeAnalyzer\",\n instruction=\"Analyze code quality and performance\"\n)\n\n# Example: Analyze code complexity\nfrom coding_open_agent_tools import analysis\n\ncomplexity = analysis.calculate_complexity(\"/path/to/code.py\")\nprint(f\"Cyclomatic complexity: {complexity['total_complexity']}\")\n\n# Example: Check git status\nfrom coding_open_agent_tools import git\n\nstatus = git.get_git_status(\"/path/to/repo\")\nprint(f\"Modified files: {len(status['modified'])}\")\n```\n\n## Development Status\n\n**Current Phase**: Planning and Requirements\n**Next Steps**:\n1. Initialize repository structure\n2. Set up development environment\n3. Implement Shell Script Generation Module (v0.1.0)\n4. Implement Python Code Generation Module (v0.2.0)\n\n## Quality Standards\n\n- **Code Quality**: 100% ruff compliance (linting + formatting)\n- **Type Safety**: 100% mypy compliance\n- **Test Coverage**: Minimum 80% for all modules\n- **Google ADK Compliance**: All function signatures compatible with agent frameworks\n- **Security**: All generated code scanned for vulnerabilities\n\n## Contributing (Future)\n\nContributions will be welcome once the initial implementation is complete. We will provide:\n- Contribution guidelines\n- Code of conduct\n- Development setup instructions\n- Testing requirements\n\n## License\n\nMIT License (same as basic-open-agent-tools)\n\n## Related Projects\n\n- **[basic-open-agent-tools](https://github.com/Open-Agent-Tools/basic-open-agent-tools)** - Foundational toolkit for AI agents\n- **[Google ADK](https://github.com/google/agent-development-kit)** - Agent Development Kit\n- **[Strands Agents](https://github.com/strands-ai/strands)** - Agent framework\n\n---\n\n**Status**: \ud83d\udea7 Planning Phase\n**Version**: 0.0.0 (not yet released)\n**Last Updated**: 2025-10-14\n",
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