github-code

name	github-code
description	Discover GitHub repositories, find example scripts within them, and read source files with line-range support and automatic Jupyter notebook conversion. Use BEFORE writing any ML training, fine-tuning, or inference code — your knowledge of library APIs is outdated and example scripts show the current patterns.

github-code

Three scripts:

• scripts/list_repos.py — list and sort a user/org's repositories.
• scripts/find_examples.py — find example/tutorial files in a specific repo using fuzzy directory matching (examples/, scripts/, notebooks/, cookbook/, …).
• scripts/read_file.py — read file contents with line ranges; .ipynb → Markdown conversion.

When to use

MANDATORY before writing ML training / fine-tuning / inference code. Your internal knowledge WILL hallucinate imports, trainer arguments, and config parameters. Working examples show current APIs.

Also useful when:

• Exploring what libraries exist for a task (list_repos on huggingface, pytorch, etc.).
• Checking recently updated repos for latest features (--sort updated).
• Studying a specific implementation before adapting it.

Canonical pattern

# 1. Which libraries does an org have?
python scripts/list_repos.py --owner huggingface --sort stars --limit 10

# 2. What examples exist in the chosen repo?
python scripts/find_examples.py --repo trl --keyword sft
python scripts/find_examples.py --repo transformers --keyword 'flash attention' --max-results 20

# 3. Read the implementation
python scripts/read_file.py --repo huggingface/trl --path examples/scripts/sft.py
python scripts/read_file.py --repo huggingface/transformers \
    --path src/transformers/models/llama/modeling_llama.py \
    --line-start 300 --line-end 450

Flags cheat-sheet

list_repos.py:

• --owner (required), --owner-type {user|org}
• --sort {stars|forks|updated|created}, --order {asc|desc}, --limit N

find_examples.py:

• --repo (required), --org (default huggingface)
• --keyword (optional fuzzy match), --max-results N, --min-score N (0–100)

read_file.py:

• --repo owner/repo (required), --path (required)
• --ref (branch / tag / commit, default HEAD)
• --line-start, --line-end (1-indexed inclusive)

All accept --json.

Environment

• GITHUB_TOKEN — required. A fine-grained token with public-repo read scope is enough.

Notebook handling

read_file.py detects .ipynb automatically, parses the notebook JSON, strips outputs, removes cells tagged hide/hidden/remove, and converts code+markdown cells to Markdown. Install nbformat + nbconvert (already in pyproject.toml) or the raw JSON is returned untouched.

Related skills

• hf-papers — use find_datasets / find_models on a paper, then jump here to find the implementation.
• hf-docs — use alongside: github-code gives you a working recipe, hf-docs gives you the authoritative API reference.