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Code review skill specialized for tt-xla (Python + C++ PJRT plugin for Tenstorrent hardware). Covers C++ memory safety, PJRT API patterns, Python test standards, and project-specific conventions.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Compare two nightly GitHub Actions runs and classify failures as new, persisting, or fixed
Stash local changes, pull latest main, and display a structured commit summary. For tt_forge_models uplift commits, shows the affected models from the submodule log.
Analyze a PyTorch model, then design and implement an optimal multi-device sharding strategy for Tenstorrent hardware that minimizes collective-communication (CCL) ops. A strategy is both which parallelism to use (tensor / sequence / data) and how to shard under it - e.g. Megatron column→row is one of several tensor-parallel schemes. Use whenever the user asks to shard a model, distribute it across devices, write Shardy annotations, reduce CCLs, or analyze a model for tensor/sequence parallelism. Trigger even when the user only says "sharding strategy" or names a model with a device count, without asking for a full plan.
Analyze CI benchmark workflow runs from GitHub Actions for the tt-xla project. Produces a markdown report covering failed jobs (with root-cause error extraction via logs and Glean), successful model performance metrics (samples/sec, TTFT, device perf), perf regressions/improvements vs previous nightly, and the full dependency commit chain (tt-xla, tt-mlir, tt-metal). Use this skill whenever the user wants to analyze a CI run, review nightly benchmark results, investigate CI failures, check benchmark performance from a workflow run, or asks about "latest nightly" results. Also trigger when the user pastes a GitHub Actions run URL or mentions a run ID in the context of performance analysis, or asks about perf regressions.
Review and fix language correctness and formatting in documentation files.
Triage one tt-forge-models training test failing with a bfloat16 dtype-mismatch RuntimeError (e.g. "mat1 and mat2 must have the same dtype, but got Float and BFloat16", "'<op>' not implemented for 'BFloat16'"). For cross-dtype operands, attempts a minimal loader fix propagating `dtype_override` into the offending tensor constructor, then re-runs CPU + pytest and updates the YAML (passing -> EXPECTED_PASSING; new failure -> KNOWN_FAILURE_XFAIL). For op-not-implemented (no PyTorch kernel), goes straight to KNOWN_FAILURE_XFAIL with the verbatim error. Updates every training entry sharing the affected loader. Never edits inference YAML or `dynamic_loader.py`.
| name | code-reviewer |
| description | Code review skill specialized for tt-xla (Python + C++ PJRT plugin for Tenstorrent hardware). Covers C++ memory safety, PJRT API patterns, Python test standards, and project-specific conventions. |
Specialized code review toolkit for the tt-xla project: a PJRT-based backend that enables JAX and PyTorch/XLA on Tenstorrent AI hardware.
Languages: C++20, Python 3.12
Build: CMake + Ninja, Python setuptools (wheel packaging)
Formatting: clang-format (C++, style from .clang-format), black + isort (Python)
Testing: pytest with custom markers
Logging: loguru (C++), Python stdlib logging
CI: pre-commit hooks (black, clang-format, SPDX copyright, trailing whitespace, isort)
When invoked via /code-reviewer, Claude Code loads this file and the reference documents into context, then applies them to review the code. No external tools, containers, or scripts needed.
references/code_review_checklist.mdreferences/coding_standards.mdreferences/common_antipatterns.mdAll review focus areas, coding standards, and antipatterns are documented in the reference files below. Refer to these as the single source of truth during reviews:
references/code_review_checklist.md — Step-by-step review checklist (C++, Python, CMake, general)references/coding_standards.md — Project coding standards for C++ and Pythonreferences/common_antipatterns.md — Antipatterns specific to tt-xla with wrong/right examples