🧠 Skills-2M: GitHub-scale Agent Skills Info Atlas

🔎 A GitHub-scale metadata index of 2M+ agent skills for researchers studying the agent-skill ecosystem

Skills-2M is a large-scale SQLite index of 2M+ agent skill records collected from GitHub, normalized to help researchers study skill discovery, repository structure, metadata patterns, retrieval, and corpus construction.

Quick Start · At a Glance · Schema · Queries · Responsible Use

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🧾 This dataset is an information index, not a bundled source-code archive. It stores normalized metadata, GitHub locations, names, descriptions, and crawl-derived fields for agent skills. Individual upstream repositories, skill files, and project contents remain governed by their original licenses and terms.

🌐 The dataset is designed as a high-coverage map of the agent-skill ecosystem. It is useful for discovery and analysis, but entries may contain crawler noise, stale links, duplicated naming conventions, or upstream projects that changed after the snapshot.

✨ Why Skills-2M?

Agent skills are becoming an important research object for understanding how AI agents package reusable capabilities, instructions, scripts, references, examples, and workflow knowledge. However, these skills are scattered across many GitHub repositories and are difficult to study at scale without a normalized metadata layer.

Skills-2M provides a compact metadata layer for questions like:

• 🧭 Discovery — Where are agent skills published, and how are they named?
• 🧱 Structure — Which repositories host large skill collections, and how are paths organized?
• 🔍 Retrieval — Can we rank, cluster, deduplicate, or route skills by description and repository context?
• 📈 Ecosystem analysis — Which owners, repos, branches, and metadata patterns dominate the public skill landscape?
• 🧪 Dataset construction — Which entries should be selected for downstream crawling, filtering, license review, or benchmark building?

📦 At a glance

2,119,830 skill rows 🧠	238,707 GitHub repos 🗂️	171,974 owners 👥	2,119,830 unique GitHub URLs 🔗
SQLite skills.db 🗄️	2.78 GiB local file size 💾	Normalized metadata index 🧩	Metadata only not source bundles ⚠️

Snapshot summary

Field	Value
Main file	skills.db
Table	skills
Skill rows	2,119,830
Distinct owners	171,974
Distinct owner/repo pairs	238,707
Distinct GitHub URLs	2,119,830
Distinct skill IDs	2,119,828
Most common branch	main (2,119,593 rows)
Metadata JSON validity	100% valid JSON in current snapshot

🗂️ Files

Path	Description
.gitattributes	Hugging Face / Git LFS tracking metadata.
LICENSE	Dataset compilation license and third-party rights notice.
README.md	Dataset card, usage guide, and caveats.
assets/skills-2m-growth.csv	Daily chart source data used to draw the growth curve.
assets/skills-2m-growth.png	Growth chart shown near the top of this card.
assets/skills-2m-growth.svg	Vector version of the growth chart.
preview/skills-preview.csv	Lightweight 1,000-row deterministic preview sampled across the full SQLite row range.
skills.db	Full SQLite database containing the normalized 2M+ agent-skill metadata index.

🚀 Quick start

Preview first

If you only want to inspect the schema and a small slice of records, download the CSV preview first:

hf download zhangdw/skills-2m \
  --repo-type dataset \
  --include preview/skills-preview.csv \
  --local-dir skills-2m

The preview contains 1,000 deterministic rows sampled across the SQLite rowid range. It is meant for quick inspection and dataset-page display; the full corpus remains in skills.db. The preview no longer includes local index timestamps; upstream_updated_at_* is derived from metadata_json.updatedAt.

Download the full SQLite index

hf download zhangdw/skills-2m \
  --repo-type dataset \
  --include skills.db \
  --local-dir skills-2m

Inspect with SQLite

sqlite3 skills-2m/skills.db ".tables"
sqlite3 skills-2m/skills.db "SELECT COUNT(*) FROM skills;"

Use from Python

import sqlite3

conn = sqlite3.connect("skills-2m/skills.db")
conn.row_factory = sqlite3.Row

row = conn.execute("""
    SELECT
        owner_slug,
        repo_slug,
        name,
        github_url,
        json_extract(metadata_json, '$.description') AS description,
        json_extract(metadata_json, '$.stars') AS stars
    FROM skills
    ORDER BY rowid
    LIMIT 1
""").fetchone()

print(dict(row))
conn.close()

🧬 Schema

The database currently contains one main table: skills.

Column	Type	Meaning
id	TEXT	Normalized skill identifier from the crawler/indexing layer.
owner_slug	TEXT	GitHub owner or organization slug.
repo_slug	TEXT	GitHub repository slug.
name	TEXT	Human-readable skill name.
github_url	TEXT	GitHub URL pointing to the discovered skill location.
branch	TEXT	Branch inferred from the source URL when available.
path	TEXT	Path-like field from the crawl/index source.
metadata_json	TEXT	JSON object with description, author, stars, route, forks, and other fields.

Primary key:

PRIMARY KEY (owner_slug, repo_slug, id)

Indexes:

CREATE INDEX idx_skills_github_url ON skills(github_url);
CREATE INDEX idx_skills_id ON skills(id);
CREATE INDEX idx_skills_owner_repo ON skills(owner_slug, repo_slug);

🧩 Common metadata keys

The metadata_json field is intentionally flexible because GitHub-scale skill metadata is messy. In this snapshot, common keys include:

Note: metadata_json.updatedAt is an upstream listing/update timestamp from the source metadata, not a local SQLite insertion or crawl timestamp.

Key	Approximate coverage
author	2,119,830
description	2,119,830
stars	2,119,830
updatedAt	2,119,830
authorAvatar	2,118,605
forks	2,118,605
route	2,118,605
occupations	2,101,486
backfillSource	1,225

🔎 Example queries

Count skills by repository

SELECT
  owner_slug || '/' || repo_slug AS repo,
  COUNT(*) AS skill_count
FROM skills
GROUP BY owner_slug, repo_slug
ORDER BY skill_count DESC
LIMIT 20;

Search descriptions

SELECT
  name,
  owner_slug || '/' || repo_slug AS repo,
  github_url,
  json_extract(metadata_json, '$.description') AS description
FROM skills
WHERE lower(json_extract(metadata_json, '$.description')) LIKE '%browser%'
LIMIT 25;

Find high-star repositories represented in the index

SELECT
  owner_slug || '/' || repo_slug AS repo,
  MAX(CAST(json_extract(metadata_json, '$.stars') AS INTEGER)) AS stars,
  COUNT(*) AS skills
FROM skills
GROUP BY owner_slug, repo_slug
ORDER BY stars DESC
LIMIT 20;

Export a retrieval seed table

.headers on
.mode csv
.output skill_retrieval_seed.csv

SELECT
  id,
  name,
  owner_slug,
  repo_slug,
  github_url,
  json_extract(metadata_json, '$.description') AS description
FROM skills;

🧭 Suggested use cases

Use case	How this dataset helps
🔍 Skill discovery	Build search indexes over names, descriptions, owners, repos, and URLs.
🧠 Agent research	Study how the public ecosystem describes reusable agent capabilities.
🧪 Benchmark construction	Select candidate skills for downstream crawling, filtering, and evaluation.
🧰 Skill routing	Prototype retrieval/ranking pipelines that map user tasks or research queries to skill descriptions.
📊 Ecosystem analytics	Measure repository concentration, branch usage, metadata coverage, and topical clusters.

🧯 Scope boundaries

This dataset is intentionally lightweight: it is an index, not a complete mirror.

✅ Included:

• Skill names and normalized identifiers
• GitHub URLs and repository slugs
• Branch/path metadata when available
• JSON metadata such as descriptions, authors, stars, forks, routes, and upstream update timestamps

❌ Not included:

• Full upstream repository contents
• Complete skill directory archives
• Verified license classifications for every upstream project
• Guarantees that every URL remains live after the crawl
• Manual quality labels or human-reviewed safety annotations

⚖️ License and rights

The original Skills-2M compilation, schema, documentation, preview sample, and chart assets are released under Creative Commons Attribution 4.0 International (CC BY 4.0). See LICENSE for the full dataset compilation license and attribution guidance.

This license does not relicense upstream GitHub repositories, skill files, source code, project documentation, descriptions, avatars, repository statistics, or other third-party/source-derived metadata. Those materials remain governed by their original licenses, terms, and rights holders.

⚖️ Responsible use

Please treat Skills-2M as a discovery and research index.

• 🪪 Check upstream licenses before redistributing, training on, or repackaging any linked content.
• 🧹 Filter before use if building a training set, benchmark, or public derivative corpus.
• 🔁 Expect drift because GitHub repositories can be renamed, deleted, rewritten, or relicensed.
• 🛡️ Avoid blind execution of scripts or code referenced by any discovered skill.
• 🧾 Preserve attribution to original repositories when presenting derived artifacts.

🧪 Verification snapshot

The current uploaded skills.db was inspected locally with SQLite before publishing this card:

integrity_check: ok
rows:            2,119,830
owners:          171,974
repos:           238,707
unique URLs:     2,119,830
file size:       2,983,604,224 bytes
schema columns:  8 (no local created_at / updated_at columns)

📚 Citation

If you use this dataset in a paper, benchmark, or data report, please cite the Hugging Face dataset page and include the snapshot date or commit hash you used.

@misc{skills2m2026,
  title        = {Skills-2M: GitHub-scale Agent Skills Info Atlas},
  author       = {Dawei Zhang},
  year         = {2026},
  howpublished = {Hugging Face Dataset},
  note         = {Large-scale SQLite metadata index of public agent skills}
}

✨ Built for people mapping the next generation of reusable agent capabilities. ✨