bambu-studio-ai
Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
Task Planner and Validator Skill Guide
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install cerbug45/task-panner-validatorInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
This skill provides a secure, step-by-step task management system for AI Agents.
# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator
# That's it! No dependencies needed - pure Python standard library
# Run tests
python test_basic.py
# Run examples
python examples.py
from task_planner import TaskPlanner
# Create planner
planner = TaskPlanner(auto_approve=False)
def my_executor(action: str, parameters: dict):
"""Your custom execution logic"""
if action == "fetch_data":
# Fetch data from API, database, etc.
return {"data": [1, 2, 3]}
elif action == "process_data":
# Process the data
return {"processed": True}
else:
return {"status": "completed"}
steps = [
{
"description": "Fetch user data",
"action": "fetch_data",
"parameters": {"source": "database"},
"expected_output": "List of users"
},
{
"description": "Process users",
"action": "process_data",
"parameters": {"validation": True},
"expected_output": "Processed data"
}
]
plan = planner.create_plan(
title="Data Processing Pipeline",
description="Fetch and process user data",
steps=steps
)
# Validate
is_valid, warnings = planner.validate_plan(plan)
if warnings:
print("Warnings:", warnings)
# Approve
planner.approve_plan(plan, approved_by="admin")
# Execute
success, results = planner.execute_plan(plan, my_executor)
# Get summary
summary = planner.get_execution_summary(plan)
print(f"Progress: {summary['progress_percentage']}%")
Automatically detects dangerous operatio...
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
A secure, step-by-step task management system designed for AI Agents to safely plan, validate, and execute complex multi-step tasks.
# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator
# No additional dependencies required - uses only Python standard library!
from task_planner import TaskPlanner
# Create a planner
planner = TaskPlanner(auto_approve=False)
# Define your steps
steps = [
{
"description": "Fetch user data from API",
"action": "fetch_data",
"parameters": {"endpoint": "/api/users", "method": "GET"},
"expected_output": "List of user objects",
"safety_check": True,
"rollback_possible": True
},
{
"description": "Process and validate data",
"action": "process_data",
"parameters": {"validation_rules": ["email", "age"]},
"expected_output": "Validated user data",
"safety_check": True,
"rollback_possible": True
}
]
# Create a plan
plan = planner.create_plan(...
# Task Planner and Validator - Skill Guide
This skill provides a secure, step-by-step task management system for AI Agents.
## Quick Installation
```bash
# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator
# That's it! No dependencies needed - pure Python standard library
```
## Verify Installation
```bash
# Run tests
python test_basic.py
# Run examples
python examples.py
```
## Basic Usage
### 1. Import and Initialize
```python
from task_planner import TaskPlanner
# Create planner
planner = TaskPlanner(auto_approve=False)
```
### 2. Define Your Executor
```python
def my_executor(action: str, parameters: dict):
"""Your custom execution logic"""
if action == "fetch_data":
# Fetch data from API, database, etc.
return {"data": [1, 2, 3]}
elif action == "process_data":
# Process the data
return {"processed": True}
else:
return {"status": "completed"}
```
### 3. Create a Plan
```python
steps = [
{
"description": "Fetch user data",
"action": "fetch_data",
"parameters": {"source": "database"},
"expected_output": "List of users"
},
{
"description": "Process users",
"action": "process_data",
"parameters": {"validation": True},
"expected_output": "Processed data"
}
]
plan = planner.create_plan(
title="Data Processing Pipeline",
description="Fetch and process user data",
steps=steps
)
```
### 4. Validate and Execute
```python
# Validate
is_valid, warnings = planner.validate_plan(plan)
if warnings:
print("Warnings:", warnings)
# Approve
planner.approve_plan(plan, approved_by="admin")
# Execute
success, results = planner.execute_plan(plan, my_executor)
# Get summary
summary = planner.get_execution_summary(plan)
print(f"Progress: {summary['progress_percentage']}%")
```
## Key Features
### Safety Validation
Automatically detects dangerous operatio...# Task Planner and Validator
A secure, step-by-step task management system designed for AI Agents to safely plan, validate, and execute complex multi-step tasks.
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
## 🎯 Features
- **✅ Step-by-Step Planning**: Break down complex tasks into manageable, ordered steps
- **🔒 Safety Validation**: Built-in security checks for dangerous operations
- **🔄 Rollback Support**: Checkpoint system for reverting failed operations
- **📝 Plan Persistence**: Save and load plans in JSON format
- **🎨 Integrity Verification**: SHA-256 checksums to prevent tampering
- **⚡ Execution Control**: Dry-run mode, auto-approve, and stop-on-error options
- **📊 Progress Tracking**: Real-time status updates and execution summaries
- **🔍 Detailed Logging**: Comprehensive logging for debugging and auditing
## 🚀 Quick Start
### Installation
```bash
# Clone the repository
git clone https://github.com/cerbug45/task-planner-validator.git
cd task-planner-validator
# No additional dependencies required - uses only Python standard library!
```
### Basic Usage
```python
from task_planner import TaskPlanner
# Create a planner
planner = TaskPlanner(auto_approve=False)
# Define your steps
steps = [
{
"description": "Fetch user data from API",
"action": "fetch_data",
"parameters": {"endpoint": "/api/users", "method": "GET"},
"expected_output": "List of user objects",
"safety_check": True,
"rollback_possible": True
},
{
"description": "Process and validate data",
"action": "process_data",
"parameters": {"validation_rules": ["email", "age"]},
"expected_output": "Validated user data",
"safety_check": True,
"rollback_possible": True
}
]
# Create a plan
plan = planner.create_plan(...Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
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