by EverMind-AI
Open-source infrastructure that turns scattered SKILL.md files into curated, retrieval-ready agent-skill corpora—with retrieval and evaluation tooling included.
# Add to your Claude Code skills
git clone https://github.com/EverMind-AI/SkillCorpusSkillCorpus is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by EverMind-AI. Open-source infrastructure that turns scattered SKILL.md files into curated, retrieval-ready agent-skill corpora—with retrieval and evaluation tooling included. It has 56 GitHub stars.
SkillCorpus's catalog security scan is still queued. You can run an instant dependency and prompt-injection check now with the "Scan for vulnerabilities" button above.
Clone the repository with "git clone https://github.com/EverMind-AI/SkillCorpus" and add it to your Claude Code skills directory (see the Installation section above).
SkillCorpus is primarily written in Python. It is open-source under EverMind-AI on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh SkillCorpus against similar tools.
No comments yet. Be the first to share your thoughts!
Unlocks once the catalog security scan passes (runs nightly).
⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
The deep catalog scan for this skill is still queued. Run an instant dependency check now instead.
SkillCorpus is EverMind's open-source pipeline for turning scattered SKILL.md files from public
repositories into reliable agent context. It aggregates sources, applies safety and license gates,
evaluates quality, and matches task-specific skills before the agent answers.
You can use the live SkillHub without cloning this repository. Clone SkillCorpus when you want the open-source machinery behind that experience:
The core code is Apache-2.0 licensed (match/ and evaluate/ are MIT); each skill retains its
upstream license. The public 1,000-skill demo, three agent benchmarks, and live SkillHub show the
result.
https://github.com/user-attachments/assets/4d9a3241-df13-4b20-9798-fb7920069995
At answer time, the practical difference is a retrieval layer: SkillHub selects vetted procedural knowledge for the task and puts it into the agent's context.
The result is the same agent with better task-specific procedures available at the moment it needs them — stronger execution without asking users to memorise skill names or wire up tool calls.
Pass rate with no skills → with SkillCorpus, same harness, same backbone (paper, Table 1):
| Harness × backbone | SkillsBench | GDPVal | QwenClawBench |
|---|---|---|---|
| OpenClaw × Qwen3.5-27B | 8.8 → 13.0 | 81.2 → 83.1 | 65.2 → 66.7 |
| OpenClaw × Qwen3.5-397B | 11.1 → 16.9 | 82.2 → 84.0 | 65.7 → 67.0 |
| Raven × Qwen3.5-27B | 10.0 → 16.5 | 82.6 → 83.8 | 66.9 → 70.8 |
| Raven × Qwen3.5-397B | 9.2 → 22.6 | 84.0 → 85.2 | 68.8 → 73.2 |
| Pooled ∆ | +7.5±2.3 (z=3.2) | +1.51±0.49 (z=3.1) | +2.79±0.70 (z=4.0) |
The gain is largest where the task needs procedural knowledge the model does not already have (SkillsBench), and smallest on open-ended economic tasks it can already do (GDPVal).
SkillHub brings per-turn skill retrieval to the five agent platforms below. Choose a platform to open its plugin guide:
Retrieval runs every turn, before the model answers: no tool call, no skill names to memorise,
no host patch. The packaged Raven plugin is ready to install, but it will claim the skills
stage once Raven merges its upstream context_segments slot; Raven's built-in retrieval keeps
working today.
Install SkillCorpus Plugins following https://github.com/EverMind-AI/SkillCorpus/blob/main/skillcorpus_plugin/INSTALL.agent.md
Paste that line to your agent and it installs itself. Per-host setup, the five settings you
will actually touch, what each turn costs and what leaves your machine —
skillcorpus_plugin/.
This is the concrete inventory of what is public today.
| Artifact | What | Link | |
|---|---|---|---|
| 🌐 | SkillHub | the current 114,190-skill catalog + the two models, hosted as an API — no install | evermind.ai/skillhub |
| 📚 | Corpus (demo) | the downloadable 1,000-skill sample — skills.parquet + attachments.tar.zst + dataset card; the full catalog is served by SkillHub |
🤗 demo-1k |
| 🔡 | Retrieval models | a bi-encoder and a reranker, fine-tuned from Qwen3-Embedding-0.6B and Qwen3-Reranker-0.6B |
🤗 bi-encoder · reranker |
| 🛠️ | Code | this repo — the pipeline that builds the corpus and trains the two models (aggregate · curate · match · evaluate · export) |
GitHub |
| 🔌 | Plugins | packaged host adapters for OpenClaw · Hermes · WorkBuddy · Raven, plus DeepSeek Harness and an HTTP adapter | skillcorpus_plugin/ |
Open source today: the code, 1,000-skill demo corpus, and retrieval models. The hosted SkillHub service is closed, and the full hosted catalog is not yet published as a downloadable dataset.
The 96,401-skill snapshot measured in the paper, organised by a 16-class taxonomy and three quality facets
(utility / robustness / safety), with 1024-dim retrieval embeddings. Column contract:
docs/corpus-schema.md.
SkillHub serves the corpus in three tiers — discover
(metadata), read (skill_md), download (zip with scripts/). Most skills are pure
instructions, so the read tier is usually sufficient.
curl "https://skillhub.evermind.ai/openapi/v1/skills?q=extract+tables+from+a+PDF"
Take an id from the results, fetch its skill_md, and inject it into your agent's
prompt. examples/skillhub_demo.py runs all three tiers:
# search + read the bodies — stdlib only, no install, no API key
python examples/skillhub_demo.py "extract tables from a scanned PDF invoice"
# also fetch the bundled scripts of the top hit
python examples/skillhub_demo.py --install ./skills "convert a PDF to images"
# retrieve AND run the task — any OpenAI-compatible LLM (OpenAI, OpenRouter, local vLLM, …)
export OPENAI_API_KEY=... # OpenRouter / vLLM: also set
# export OPENAI_BASE_URL=https://openrouter.ai/api/v1 # OPENAI_BASE_URL + --model openai/gpt-4o-mini
python examples/skillhub_demo.py --ask "extract tables from a scanned PDF invoice"
task: extract tables from a scanned PDF invoice
[1/2] search → 2 hit(s), metadata only
1. ocr-and-documents q=0.808 DOC-PROC MIT
Extract text from PDFs/scans (pymupdf, marker-pdf).
2. document-workflows q=0.86 DOC-PROC MIT
Build end-to-end document processing workflows an