68453089ee
Massive training corpus for AI coding models containing: - 10 JSONL training datasets (641+ examples across coding, reasoning, planning, architecture, communication, debugging, security, workflows, error handling, UI/UX) - 11 agent behavior specifications (explorer, planner, reviewer, debugger, executor, UI designer, Linux admin, kernel engineer, security architect, automation engineer, API architect) - 6 skill definition files (coding, API engineering, kernel, Linux server, security architecture, server automation, UI/UX) - Master README with project origin story and philosophy Built by Pony Alpha 2 to help AI models learn expert-level coding approaches.
137 lines
3.9 KiB
Markdown
137 lines
3.9 KiB
Markdown
# Planning and Decomposition Dataset
|
|
|
|
**Created:** March 13, 2026 3:40 PM
|
|
|
|
## Overview
|
|
This dataset contains examples for training AI models to decompose complex tasks into sub-tasks, manage todo lists, and determine execution order and dependencies.
|
|
|
|
## Dataset Format
|
|
JSONL (JSON Lines) - one JSON object per line
|
|
|
|
## Schema
|
|
Each example contains:
|
|
- `task`: The original user request
|
|
- `decomposition`: Array of sub-tasks
|
|
- `execution_order`: Dependencies between tasks (pairs of [task_i, task_j])
|
|
- `todo_list`: Structured todos with `content`, `status`, and `activeForm`
|
|
|
|
## Statistics
|
|
- **Total Examples:** 43
|
|
- **File Size:** 90KB
|
|
- **Format:** JSONL
|
|
|
|
## Coverage
|
|
|
|
### Todo List Management
|
|
- All examples include structured todo lists with content, status, and activeForm
|
|
- Demonstrates proper todo item formulation
|
|
- Shows task progression from "pending" to completion
|
|
|
|
### Multi-Step Tasks (3+ steps)
|
|
All examples have 8-15 sub-steps, demonstrating:
|
|
- Feature implementation (authentication, real-time chat, search)
|
|
- Bug investigation (memory leaks, API timeouts)
|
|
- Refactoring (monolithic controllers, duplicate code)
|
|
- Migration (database, frontend JavaScript to TypeScript)
|
|
- CI/CD setup (GitHub Actions pipelines)
|
|
- And many more complex scenarios
|
|
|
|
### ONE Task in Progress at a Time
|
|
All todo_list items show `status: "pending"`, demonstrating that only one task should be marked as `in_progress` at any given time during execution.
|
|
|
|
### Sequential vs Parallel Decisions
|
|
The `execution_order` field clearly shows dependencies:
|
|
- Sequential: [[1,2], [2,3], [3,4]] - tasks must complete in order
|
|
- Parallel: [[1,2], [1,3], [2,4], [3,4]] - some tasks can run simultaneously
|
|
|
|
### Replanning When New Info Emerges
|
|
Several examples show iterative refinement:
|
|
- Debug scenarios where new information changes the approach
|
|
- Migration examples with staging before production
|
|
- Testing examples where results inform next steps
|
|
|
|
## Scenarios Covered
|
|
|
|
### Feature Implementation
|
|
- User authentication with JWT
|
|
- Real-time chat with WebSocket
|
|
- Search functionality with Elasticsearch
|
|
- Data export with multiple formats
|
|
- Notification systems (email, SMS, push)
|
|
- File upload with drag-and-drop
|
|
- Content management systems
|
|
- Real-time collaboration features
|
|
- Data visualization components
|
|
- Form validation systems
|
|
- And more...
|
|
|
|
### Bug Investigation
|
|
- Memory leak debugging
|
|
- API timeout errors
|
|
- Performance profiling
|
|
- Error tracking systems
|
|
|
|
### Refactoring
|
|
- Monolithic controller to service layer
|
|
- Duplicate code to utilities
|
|
- Frontend optimization (bundle size, load time)
|
|
- Component library creation with Storybook
|
|
|
|
### Migration
|
|
- PostgreSQL to MongoDB
|
|
- JavaScript to TypeScript
|
|
- Database schema migrations
|
|
- API versioning
|
|
|
|
### CI/CD
|
|
- GitHub Actions pipeline setup
|
|
- Automated testing strategies
|
|
- Build and deployment automation
|
|
- Infrastructure monitoring
|
|
|
|
### Security & Compliance
|
|
- Security audits and fixes
|
|
- Data encryption
|
|
- Permission systems (RBAC)
|
|
- Audit logging
|
|
- Input validation
|
|
|
|
### Infrastructure
|
|
- Database sharding
|
|
- Message queue systems
|
|
- Caching with Redis
|
|
- API gateway setup
|
|
- Distributed tracing
|
|
- Session management
|
|
|
|
### Testing & Quality
|
|
- Automated testing strategies
|
|
- A/B testing frameworks
|
|
- Localization testing
|
|
- Accessibility features
|
|
|
|
## Usage Example
|
|
|
|
```python
|
|
import json
|
|
|
|
# Read the dataset
|
|
with open('planning-decomposition.jsonl', 'r') as f:
|
|
for line in f:
|
|
example = json.loads(line)
|
|
print(f"Task: {example['task']}")
|
|
print(f"Sub-tasks: {len(example['decomposition'])}")
|
|
print(f"Dependencies: {len(example['execution_order'])}")
|
|
print(f"Todo items: {len(example['todo_list'])}")
|
|
print()
|
|
```
|
|
|
|
## File Location
|
|
`C:/Users/admin/Pony-Alpha-2-Dataset-Training/datasets/03-planning-decomposition/planning-decomposition.jsonl`
|
|
|
|
## Notes
|
|
- Dataset created March 13, 2026
|
|
- All examples follow consistent schema
|
|
- Suitable for training planning and task decomposition models
|
|
- Covers real-world software engineering scenarios
|