README.md

Brick public skill tables

Public skill vectors consumed by the Brick router. The Hugging Face dataset regolo/brick-skill-tables contains one CSV file, skill_vectors.csv, with one row per model and six capability values in [0,1]. Brick uses these values as cold-start priors, so users do not need to measure a model that is already listed.

The CLI also ships richer JSON copies under this folder for offline initialization. The Hugging Face dataset is intentionally CSV-only; provenance and detailed measurement metadata remain in the Brick repository and generated records.

CSV schema

model,coding,creative_synthesis,instruction_following,math_reasoning,planning_agentic,world_knowledge
claude-haiku-4-5,0.60,0.60,0.68,0.62,0.58,0.70
qwen3.5-9b,0.58,0.45,0.72,0.75,0.62,0.76

The vector order is fixed and must never be changed:

[coding, creative_synthesis, instruction_following, math_reasoning, planning_agentic, world_knowledge]

Values are normalized scores, not raw benchmark percentages. They describe capability priors only; cost and routing preferences belong in Brick configuration. creative_synthesis is generally low-confidence because there is no reliable public cross-model evaluation for it.

The bundled JSON records may include provenance fields such as source, confidence, support, and sources. The public CSV deliberately contains only the model id and vector so it stays easy to inspect and consume.

How Brick uses the table

At startup, Brick first checks its bundled cards. When a fresh card is requested, the resolver can fetch skill_vectors.csv from Hugging Face, find the matching model row, and cache the result locally. A measured profile from Brick can supersede a benchmark prior locally.

Contributing

Add a model by pull request

1. Fork this dataset and create a branch.
2. Add one row to skill_vectors.csv.
3. Keep the exact header and capability order; use a stable model id and values between 0 and 1.
4. Sort rows by model and open a pull request against main.
5. In the PR description, explain the sources or evaluation method used for the vector. Public benchmark links and measured Brick probe results are preferred.

Example row:

my-model,0.70,0.45,0.72,0.80,0.60,0.75

For a measured profile, the recommended local command is:

brick skills extract my-model --publish

This asks for consent and updates the matching CSV row. Manual PRs are welcome when the model cannot be measured through Brick or when submitting a benchmark prior for review.