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tt-studio
tt-studio 收录了来自 tenstorrent 的 4 个 skills,并提供仓库级职业覆盖和站内 skill 详情页。
这个仓库中的 skills
Make a change in TT-Studio the team way: branch off `dev` with a `<username>/<feature>` branch, keep the diff minimal and in-scope, verify the running app via its health endpoints before pushing, leave every other branch untouched, and open a PR against `dev` with a human, professional title and description. Use whenever the user asks to start a feature/fix/branch, make a change and open a pull request, or "do this properly on a new branch" in this repo. Enforces no AI-tool attribution in commits, PR text, or review comments.
Review third-party license attribution for TT-Studio when dependencies or bundled assets change, and keep the attribution files in sync. Use when a PR adds/updates a dependency (app/backend/requirements.txt, app/agent, inference-api, docker-control-service, or app/frontend/package.json), bundles a binary/model/weights file, or when the user asks to "attribute a license", "update third-party-licenses", "check license compliance", or run the license check. Pairs with the deterministic gate dev-tools/check_license_attribution.py (freshness + new-dependency attribution) that runs on pull requests; this skill does the judgment the script cannot -- classifying licenses and flagging NonCommercial/copyleft/provenance risks.
Triage a TT-Studio bug-report ZIP / log bundle (named `tt-studio-logs-ttbr-*` or referenced as `ttbr-*`) to root-cause why a model deploy, board allocation, or Voice Agent pipeline failed. Use when the user shares a path to a TT-Studio log bundle, mentions a bug report ID like `ttbr-<hex>`, or attaches a screenshot of the TT-Studio Bug Report issue template. Fans out parallel Explore subagents across `app/backend/board_control/`, `app/backend/docker_control/`, `app/backend/shared_config/`, and `app/frontend/src/components/` to map log evidence to source code.
Project map for TT-Studio — what the platform is, where its docs live, its key components (React frontend, Django backend, FastAPI inference server, Docker), and the AI model types it supports. Use when you need a high-level orientation of the repo, want to know which doc covers a topic, or are trying to locate where a component or capability lives before diving into code.