Nanoresearch Experiment Agent Skill
Generate a Python code skeleton from an experiment blueprint
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/OpenRaiser/NanoResearch --skill nanoresearch-experimentThe instruction itself
6 sections, as written by the authorExperiment 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 topapers/experiment_blueprint.jsonproduced by the planning skill
Process
- Parse the experiment blueprint for datasets, baselines, metrics, and ablation groups
- Generate the project directory structure (data loaders, models, training, evaluation, configs)
- Produce data loading and preprocessing code for each specified dataset
- Implement model architecture stubs for the proposed method and each baseline
- Generate training loop with logging, checkpointing, and early stopping
- Implement the evaluation harness computing all specified metrics
- Create configuration files for each ablation group
- 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 modulesmodels/: Model architecture implementations (proposed method and baselines)training/: Training loop and optimization utilitiesevaluation/: Metric computation and result aggregationconfigs/: YAML configuration files for each experiment and ablation variantrun.py: Main entry point for launching experimentsrequirements.txt: Python dependencies
Other skills for the same job
different authors, same section of the catalogue
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
How to use it
Take openraiser/nanoresearch-experiment from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.