Datasetpublic

zhangdw/astra-skills

🧰 ASTRA Skills: Agent Skill Tool-use Repository Atlas A large-scale atlas of real-world AI agent skills discovered from public GitHub repositories      ASTRA Skills (Agent Skill Tool-use Repository Atlas) is a deduplicated corpus of agent skill directories centered on SKILL.md, collected with the Astra crawler for research on tool use, agent instruction design, and skill retrieval. Quick Start · At a Glance · Directory Format ·… See the full description on the dataset page: https://huggingface.co/datasets/zhangdw/astra-skills.

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Dataset Card

🧰 ASTRA Skills: Agent Skill Tool-use Repository Atlas

A large-scale atlas of real-world AI agent skills discovered from public GitHub repositories

ASTRA Skills (Agent Skill Tool-use Repository Atlas) is a deduplicated corpus of agent skill directories centered on <code>SKILL.md</code>, collected with the Astra crawler for research on tool use, agent instruction design, and skill retrieval.

<a href="#-quick-start">Quick Start</a> · <a href="#-dataset-at-a-glance">At a Glance</a> · <a href="#-directory-format">Directory Format</a> · <a href="#-intended-use">Intended Use</a>

[!IMPORTANT] This dataset packages skill directories discovered from public GitHub repositories. The dataset-level metadata and packaging are released under Apache-2.0, but individual skill contents remain subject to their original repository licenses and terms. Please inspect upstream licenses before redistribution, training, or benchmark release.

✨ Why ASTRA Skills?

Modern coding agents increasingly rely on reusable skills: small instruction bundles, scripts, templates, and examples that teach an agent how to perform a specialized workflow. These skills are scattered across repositories and marketplaces, making large-scale study difficult.

ASTRA Skills provides a compact snapshot for questions like:

—What do real-world agent skills look like across public repositories?
—How are skills structured around `SKILL.md`, scripts, templates, and examples?
—Which instruction patterns, tool-use conventions, and retrieval signals appear in the wild?
—How can skill discovery, ranking, routing, composition, or deduplication methods be evaluated at scale?

📦 Dataset at a Glance

<table> <tr> <td align="center">148,134 deduplicated skills</td> <td align="center">19 split archive parts</td> <td align="center">3.65 GiB compressed archive</td> <td align="center">GitHub source label</td> </tr> <tr> <td align="center">2026-04-15 snapshot date</td> <td align="center">09:10:28 → 11:59:38 UTC DB crawl range</td> <td align="center">MD5 <code>SKILL.md</code> dedup key</td> <td align="center">Directory Corpus skills plus metadata</td> </tr> </table>

Snapshot Summary

Field	Value
Total deduplicated skills in `skills.db`	`148,134`
Source labels in DB	`github only`
Crawl time range, UTC	`2026-04-15 09:10:28` → `2026-04-15 11:59:38`
Archive layout	`astra-skills-part-0001.tar.gz` → `astra-skills-part-0019.tar.gz`

🗂️ Dataset Files

File	Description
`README.md`	Dataset card and usage notes.
`astra-skills-part-0001.tar.gz` → `astra-skills-part-0019.tar.gz`	Split compressed archive containing the `github/` skill directory tree.
`.gitattributes`	Hugging Face / Git LFS tracking metadata.

🚀 Quick Start

Download the dataset with the current Hugging Face Hub CLI:

bash

uvx --from huggingface_hub hf download zhangdw/astra-skills \
  --type dataset \
  --local-dir astra-skills

Merge the split archive and extract it:

bash

cat astra-skills/astra-skills-part-*.tar.gz > skills_github.tar.gz
tar xzf skills_github.tar.gz

This produces a github/ directory containing the collected skill directories.

Inspect a few skills:

bash

find github -name SKILL.md | head
find github -name _meta.json | head

🧱 Directory Format

Each saved skill directory is copied from a GitHub repository path that contains SKILL.md.

text

github/
└── {source}_{owner}_{skill_name}/
    ├── SKILL.md
    ├── _meta.json
    └── [optional files, e.g. scripts/, templates/, examples/]

_meta.json stores crawl metadata. Typical fields include source site, repository URL, owner, repository name, skill name, and relative path inside the original repository.

🔎 Example Discovery Workflows

<details open> <summary>Build a lightweight local skill index</summary>

python

from pathlib import Path

root = Path("github")
records = []

for skill_file in root.rglob("SKILL.md"):
    records.append({
        "skill_dir": str(skill_file.parent),
        "skill_name": skill_file.parent.name,
        "readme_chars": len(skill_file.read_text(errors="ignore")),
        "has_scripts": (skill_file.parent / "scripts").exists(),
        "has_templates": (skill_file.parent / "templates").exists(),
    })

print(len(records))
print(records[:3])

</details>

<details> <summary>Search skills by instruction text</summary>

bash

rg -n "browser automation|Hugging Face|spreadsheet|PowerPoint" github -g 'SKILL.md'

</details>

<details> <summary>Read upstream provenance from metadata</summary>

python

import json
from pathlib import Path

for meta_file in Path("github").rglob("_meta.json"):
    meta = json.loads(meta_file.read_text())
    print(meta_file.parent.name, meta)
    break

</details>

⚙️ Crawl Pipeline

The current snapshot was produced by the Astra crawler with the following high-level pipeline:

1.Discover GitHub repositories from skill index websites (skills.sh, skillstore.io, agent-skills.md) plus configured seed repositories.
2.Clone discovered repositories.
3.Recursively detect directories containing SKILL.md.
4.Copy each detected skill directory into the dataset layout.
5.Deduplicate by MD5 hash of SKILL.md content in skills.db.

[!NOTE] Website index coverage does not guarantee successful retrieval of every listed skill. Repository accessibility, branch availability, file layout, and upstream content changes can all affect crawl completeness.

✅ Intended Use

ASTRA Skills is designed for:

—research on AI agent skill ecosystems and instruction-following behavior;
—skill retrieval, ranking, routing, and composition experiments;
—analysis of real-world tool-use instructions, scripts, templates, and examples;
—deduplication, clustering, and taxonomy construction for agent skills;
—benchmark construction for skill discovery and agent memory systems.

🛠️ Maintenance Notes

This dataset is a snapshot, not a live mirror. The archive preserves the collected directory contents from the crawl, while _meta.json files preserve repository-level provenance. If you build downstream datasets from ASTRA Skills, keep the snapshot date and upstream license caveats with your derived artifacts.

👤 Author

—Dawei Zhang (GitHub: zhangdw156)

📚 Citation

If you use ASTRA Skills in research, please cite this dataset and any upstream repositories whose skill contents are central to your analysis.

bibtex

@misc{astraSkills2026,
  author       = {Dawei Zhang},
  title        = {ASTRA Skills: Agent Skill Tool-use Repository Atlas},
  year         = {2026},
  howpublished = {Hugging Face Dataset},
  publisher    = {Hugging Face},
  doi          = {10.57967/hf/8399},
  url          = {https://huggingface.co/datasets/zhangdw/astra-skills},
  note         = {Snapshot date: 2026-04-15; DOI record revision: 146eb8c}
}

📄 License

Dataset metadata and packaging are released under Apache-2.0. Individual skill contents remain subject to their original repository licenses.

ASTRA Skills aims to make agent skill ecosystems easier to inspect, retrieve, and study at scale.

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