| # 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 |
|
|
| ```csv |
| 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: |
|
|
| ```text |
| [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: |
|
|
| ```csv |
| my-model,0.70,0.45,0.72,0.80,0.60,0.75 |
| ``` |
|
|
| For a measured profile, the recommended local command is: |
|
|
| ```bash |
| 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. |
|
|
