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Found 4 Skills
Run a single experiment iteration. Edit the target file, evaluate, keep or discard.
Keep iterating on code changes until the tests pass, the build succeeds, or linting is clean. Runs in a tight loop of fix → run → check → repeat. Use when you want the agent to autonomously grind through test failures or build errors.
Write ML experiment code with iterative improvement. Generate training/evaluation pipelines, debug errors, and optimize results through code reflection. Use when implementing experiments for a research paper.
Refine AI-generated code through specific feedback—point out errors, identify gaps, show desired changes, reference style guides