>- Converts MLIR lit test files to `bazel run` commands. Use when you need to convert a failing MLIR lit test target (containing RUN lines) to a shell command whose flags can be modified for further analysis and debugging.
npx skills add https://github.com/google/heir --skill lit-to-bazel
This skill guides the agent in using the lit_to_bazel tool to convert MLIR
test files to executable commands.
To convert a lit test file to a bazel run command, use the following command
recipes:
bazel run //third_party/heir/scripts:lit_to_bazel -- {test_file_path}
Replace {test_file_path} with the absolute path to the test file. The absolute
path is necessary because the bazel run command runs relative to its runfiles
directory, and so a relative path will not be relative to the VCS root of the
project.
%s in RUN lines with the absolute pathof the test file to ensure it works in the sandbox.
heir-opt into heir-translate.
as lit test files with multiple RUN lines that interact via writing to and
reading from temporary files. When encountering situations like this, you
should stop and warn the user.
the lit_to_bazel tool.
Copy this checklist and track progress:
- [ ] Step 1: Identify the test file to convert.
- [ ] Step 2: Run the `lit_to_bazel` tool on the file.
- [ ] Step 3: Inspect the generated command or run it.
- [ ] Step 4: Verify execution results.
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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
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Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take google/lit-to-bazel from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.