---
name: nanoresearch-experiment-a0ac13c30543
description: Generate a Python code skeleton from an experiment blueprint
version: 0.1.0
---

# Experiment Skill

## Purpose
Take the experiment blueprint and produce a runnable Python code skeleton that implements the proposed method, baselines, training loops, evaluation harness, and ablation configurations.

## Tools Required
None. This skill operates entirely through LLM code generation based on the experiment blueprint.

## Input
- `experiment_blueprint`: Path to `papers/experiment_blueprint.json` produced by the planning skill

## Process
1. Parse the experiment blueprint for datasets, baselines, metrics, and ablation groups
2. Generate the project directory structure (data loaders, models, training, evaluation, configs)
3. Produce data loading and preprocessing code for each specified dataset
4. Implement model architecture stubs for the proposed method and each baseline
5. Generate training loop with logging, checkpointing, and early stopping
6. Implement the evaluation harness computing all specified metrics
7. Create configuration files for each ablation group
8. Add a main entry point that accepts a config and runs the full train-evaluate pipeline

## Output
Produces `experiments/` directory containing:
- `data/`: Data loading and preprocessing modules
- `models/`: Model architecture implementations (proposed method and baselines)
- `training/`: Training loop and optimization utilities
- `evaluation/`: Metric computation and result aggregation
- `configs/`: YAML configuration files for each experiment and ablation variant
- `run.py`: Main entry point for launching experiments
- `requirements.txt`: Python dependencies


---
Source attribution: OpenRaiser
Original project: https://github.com/OpenRaiser/NanoResearch
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: OpenRaiser
Original project: https://github.com/OpenRaiser/NanoResearch
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Education & Research
Roles: Data Scientist, ML / AI Engineer, Researcher
Specialties: API, Backend, Cross Platform
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: a26ca3a9133e183d17d97a2e9117f34e2645e3a39cc24797ebd2c4646c12bbd5