| # Brick public skill tables |
|
|
| Per-model **skill vectors** consumed by the Brick router. Each `<model>.json` maps a |
| model id to a 6-dimensional capability vector in `[0,1]`. `brick init` reads this |
| folder to seed `skill_router.models[].skill_vector` for known model ids, so a user |
| never has to re-run inference for a model someone already measured. |
|
|
| This folder ships with the CLI (`templates/` is published) and mirrors the Hugging |
| Face dataset **`regolo/brick-skill-tables`**, which grows as users contribute |
| measurements for new models (`brick skills extract --publish`, opt-in). |
|
|
| ## Capability order (canonical, never reorder) |
|
|
| ``` |
| [coding, creative_synthesis, instruction_following, math_reasoning, planning_agentic, world_knowledge] |
| ``` |
|
|
| ## Record schema |
|
|
| ```jsonc |
| { |
| "model": "claude-haiku-4-5", |
| "provider": "anthropic", |
| "capabilities": ["coding", "...", "world_knowledge"], |
| "skill_vector": [0.73, 0.65, 0.70, 0.81, 0.50, 0.77], |
| "source": "benchmark", // benchmark | measured | heuristic |
| "confidence": ["medium", "low", "medium", "high", "medium", "medium"], |
| "imputed_capabilities": [], // coords filled from the model mean (NA in public benchmarks) |
| "support": null, // {correct,total} per capability — only for source=measured |
| "subset_hash": null, // frozen probe-set hash — only for source=measured |
| "date": "2026-06-11", |
| "notes": "..." |
| } |
| ``` |
|
|
| ## `source` and trust order |
|
|
| 1. **`measured`** — produced by `brick skills extract` running the frozen probe set |
| against the model and grading verifiable categories (math exact-match, code |
| tests, MMLU-Pro letter). Highest trust; carries `support` and `subset_hash`. |
| 2. **`benchmark`** — derived from published AI-lab benchmarks (SWE-bench, AIME, |
| GPQA, MMLU-Pro, tau-bench, …) normalized to `[0,1]`. Cold-start prior for |
| frontier closed models. A `measured` record for the same model **overrides** it. |
| 3. **`heuristic`** — interpolated fallback for an unknown id; marked explicitly. |
|
|
| `measured` and `benchmark` vectors are **not on the same scale** (different question |
| mixes, different grading). Treat `benchmark` as a prior; prefer `measured` when both |
| exist for a model. |
|
|
| ## NA imputation |
|
|
| Public benchmarks systematically omit some capabilities (creative writing is rarely |
| benchmarked; Google does not publish instruction-following or agentic numbers for |
| most Gemini tiers). For a missing coordinate we impute the **mean of the model's |
| known coordinates**, list the coordinate in `imputed_capabilities`, and set its |
| `confidence` to `low`. This keeps the vector usable by the router while flagging the |
| estimate as soft. |
|
|
| ## Contributing |
|
|
| Run `brick skills extract <model> --publish` (opt-in consent prompt) to measure a new |
| model on the frozen probe set and upsert a `measured` record to |
| `regolo/brick-skill-tables`. You may also open a PR adding a `<model>.json` here. |
|
|
