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How to self-host the Skill Corpus retrieval models with Skill Corpus

Serve the released bi-encoder and reranker behind your own /embed and /score endpoint.

Authorization notice. Skill Corpus is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA platform engineer who must keep skill retrieval inside their own infrastructure instead of calling the hosted SkillHub API.

The problemDepending on a hosted endpoint may not fit data-control or deployment rules.

The outcomeA local endpoint that encodes queries and skills and reranks top results for your agents.

What you need first

  • Internet access to reach the hosted SkillHub API or the plugin install guide
  • Python 3 for the example script or the self-hosting setup
  • An OpenAI-compatible API key only if you use the --ask option of the example script
  • PyTorch and transformers (from skillcorpus/match/requirements.txt) and a machine able to run two 0.6B models for self-hosting

Steps

  1. Load the demo corpus with pip install pandas and then read skills.parquet with pandas, or use the datasets library.
  2. Run pip install -r skillcorpus/match/requirements.txt to install the serving dependencies.
  3. Set EMBEDDING_MODEL and RERANKER_MODEL to the released checkpoints, then run bash skillcorpus/match/scripts/run_server.sh.
  4. Call /embed and /score on the local endpoint and run your own encode, top-k, and rerank step.

Prompt for your agent

In the SkillCorpus repository, install skillcorpus/match/requirements.txt, start the server with run_server.sh using the released EverMind-AI bi-encoder and reranker checkpoints, then send a test query to /embed and /score. Report the top three skill ids for <task description>. Verify that the scores are returned for each candidate.

Caveat. The README says this local endpoint is not a drop-in for the hosted /openapi/v1/skills API, so you must write your own selection logic over /embed and /score.

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