EverMind-AI/SkillCorpus
Open-source infrastructure that turns scattered SKILL.md files into curated, retrieval-ready agent-skill corpora—with retrieval and evaluation tooling included.
安装Install
dsh plugin --profile web add github:EverMind-AI/SkillCorpus把 EverMind-AI/SkillCorpus 加入你的 DSH 配置（web profile）即可启用。
READMEREADME
What SkillCorpus gives you
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:
- Build your own skill layer — point the pipeline at your own source registry, apply the curation, safety, and license gates, and export a corpus for your agents.
- Change the behavior — modify the taxonomy, quality and dedup rules, retrieval recipe, export schema, evaluation suites, or host plugins.
- Keep control of deployment — self-host the released retrieval models and connect your own agent host instead of using the hosted SkillHub API.
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
Stronger agents, one turn at a time
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.
| Dimension | Without SkillCorpus | With SkillCorpus |
|---|---|---|
| Context | Model knowledge plus a manually maintained prompt. | Task-specific, license-audited SKILL.md retrieved on every turn. |
| Execution | Generic workflows can miss exact steps, edge cases, or supporting scripts. | Procedures, references, and optional scripts arrive before execution. |
| Integration | Each host maintains its own collection of task instructions. | One curated skill layer serves OpenClaw, Hermes, Raven, WorkBuddy, DeepSeek Harness, and other hosts. |
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.
Results
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 integrations
SkillHub brings per-turn skill retrieval to the five agent platforms below. Choose a platform to open its plugin guide:
 DeepSeek Harness |
Hermes |
OpenClaw |
Raven |
WorkBuddy |
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/.
Public artifacts
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.
Query the API directly
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 and pipelines …
[2/2] detail → fetching skill_md for 2 skill(s)
ocr-and-documents: 4916 chars u=8 r=7 s=9 files=4 flags=['no_steps']
document-workflows: 31628 chars u=9 r=9 s=9 files=7
→ built a prompt of 36,742 chars with the skill bodies injected
Endpoints, response envelope, status codes and rate limits:
docs/integrations.md.
Self-host the models
To avoid depending on the hosted endpoint, run selection yourself. The corpus and both retrieval models are released: load the data, serve the two models, and run your own encode → top-k → rerank.
# the data — a 1,000-skill demo for now; the full 114,190-skill corpus follows
from datasets import load_dataset
skills = load_dataset("EverMind-AI/skillcorpus-demo-1k", split="train") # 1,000 demo skills
# or read the file directly with pandas (no `datasets`): pip install pandas
import pandas as pd; skills = pd.read_parquet("skills.parquet")
Attachments (scripts/, references/) ship as a sibling attachments.tar.zst.
# install the serving deps (torch, transformers, …), then point the two env vars at
# the released checkpoints (the script's defaults are training outputs absent from a
# fresh clone) and serve both models behind one endpoint -> /embed + /score
pip install -r skillcorpus/match/requirements.txt
EMBEDDING_MODEL=<embedding checkpoint dir> RERANKER_MODEL=<reranker checkpoint dir> \
bash skillcorpus/match/scripts/run_server.sh
This endpoint speaks /embed + /score
(skillcorpus/match/ → Serving) — it is not a
drop-in for SkillHub's hosted-only /openapi/v1/skills API. So:
examples/skillhub_demo.pyand the section-C integrations talk only to the hosted SkillHub; a self-hosted setup runs its own selection directly over/embed+/score.- It is also the embedding endpoint the producer's dedup uses — set
embedding.provider: skillrouter_remoteto build your own corpus with it.
To curate your own sources instead, see Build your own corpus.
How it works
The collection pipeline is the foundation; the payoff is task-specific skill retrieval before the agent acts.
skillcorpus/
├── core/ data models · SQLite/faiss store · LLM & embedding clients
├── aggregate/ source registry + multi-repo clone
├── curate/ parse · safety · license · classify · quality · dedup + full-library passes
├── export/ corpus writer (parquet + attachments + dataset card)
├── match/ the 2 released models + training recipe ← isolated deps
├── evaluate/ skillsbench · qwenclawbench · gdpval benchmarks ← isolated deps
└── cli.py build · stats · export
cli build runs the whole curation chain
(ingest → quality_pass → dedup_pass → license_audit → export.corpus). LLM classification and
quality scoring degrade gracefully to rules when no model endpoint is reachable, so the pipeline
always runs end to end.
match/ and evaluate/ are standalone toolkits with their own requirements.txt
(torch / transformers, per benchmark); they are not pulled in by pip install of the producer.
- Retrieval —
skillcorpus/match/is the two released models: a bi-encoder fine-tuned fromQwen3-Embedding-0.6Bfor candidate recall, and a reranker fine-tuned fromQwen3-Reranker-0.6Bthat scores the top candidates. SkillHub serves both; to run them yourself see Serving (serve.py+run_server.sh). The directory also holds the training recipe (synthetic queries → InfoNCE → listwise CE) andeval_compare.pyfor the retrieval metrics (nDCG / MRR / Hit / Recall). - Benchmarks —
skillcorpus/evaluate/:skillsbench,qwenclawbench,gdpval— each self-contained with its own README and dependencies.
Build your own corpus
Only needed if you want to curate your own sources. Requires an LLM endpoint for
classification / quality scoring and an embedding endpoint for dedup — see
docs/running.md.
git clone https://github.com/EverMind-AI/SkillCorpus.git skillcorpus && cd skillcorpus
python3 -m venv .venv && source .venv/bin/activate
python -m pip install --upgrade pip && pip install -e .
python -m skillcorpus.cli build # 4 demo sources -> curate -> export
python -m skillcorpus.cli stats # counts by source / category / license
python -m skillcorpus.cli export --out ./corpus
Only skills from GREEN-licensed sources are exported (the demo trusts the whitelist in
audit/license_safe_sources.json wholesale; production gates per source-repo SPDX). The per-row
license is each skill's declared value, so a demo corpus can still carry non-GREEN license
strings. Use --sources-config your.yaml for your own registry, or --source <name> for one source.
pip install -e ".[dev]"
python -m pytest skillcorpus/tests -p no:cacheprovider --import-mode=importlib
Roadmap
- Curation pipeline: 16-class taxonomy, 3-facet quality, per-source license audit
- Fine-tuned retrieval stack + three-benchmark evaluation
- Public SkillHub endpoint
- Retrieval models (bi-encoder + reranker) and a 1k demo corpus on HuggingFace
- Full 114,190-skill corpus on HuggingFace
- Deployment script for the two retrieval models (self-hosting
match/) - Plugins for WorkBuddy · Hermes · OpenClaw · DeepSeek Harness (+ HTTP adapter for any other host)
- Raven plugin — packaged, waiting on the upstream
context_segmentsslot
EverMind Ecosystem
EverMind is an open-source ecosystem for long-term memory, self-evolving agents, AI-native interfaces, and memory evaluation.
| EverMind Open-Source Ecosystem | |
|---|---|
| Memory Runtime | EverOS — the local memory operating system and research-backed runtime for agent and user memory. |
| Self-Improving Agent Harness | Raven — the self-improving agent harness that brings memory, proactivity, context control, and skill evolution into terminal-native agents. |
| Agent Skills & Retrieval | SkillCorpus — open curation and retrieval tooling, a public 1K demo corpus, SkillHub, agent integrations, and benchmarks. |
| Algorithm Engine | EverAlgo — stateless extraction, ranking, parsing, and memory operators that power EverOS. |
| Hypergraph Memory | HyperMem — hypergraph memory for long-term conversations, with its own benchmark-backed topic → episode → fact retrieval method. |
| Benchmarks | EverMemBench · EvoAgentBench — evaluation suites for conversational memory and agent self-evolution. |
| Long-Context Research | MSA — Memory Sparse Attention for scalable latent memory and 100M-token contexts. |
| Personal Memory Layer | EverMe — CLI and agent plugin suite for cross-device, cross-agent personal memory. |
| Developer Integrations | evermem-claude-code · everos-plugins — plugins, skills, and migration tooling for AI coding agents. |
Together, these repositories form EverMind's research-to-runtime stack: new memory methods, reusable algorithms, benchmark evidence, and practical agent integrations.
Citation
@article{wang2026skillcorpus,
title = {SkillCorpus: Consolidating and Evaluating the Open Skill Ecosystem for Real-World LLM Agents},
author = {Wang, Yanze and Yao, Pengfei and Sun, Tianyi and Hu, Chuanrui and Xiao, Yan and Luo, Xiaotian and Han, Yunyun and Chen, Yifan and Sun, Jun and Deng, Yafeng},
year = {2026},
eprint = {2607.15557},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.15557}
}
License
- Code — Apache-2.0 (the
match/andevaluate/toolkits are each MIT — see their ownLICENSE). - Corpus — every skill keeps its original upstream license; only GREEN
(MIT / Apache-2.0 / BSD / ISC / …) skills are included, none relicensed. Each row carries
source,source_url, andlicense, so downstream use must follow the per-skill terms.
Full GREEN/RED/YELLOW policy, license data flow, and opt-out:
docs/licence-and-governance.md.
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