Implement ML pipelines, model serving, and feature engineering. Handles TensorFlow/PyTorch deployment, A/B testing, and monitoring. Use PROACTIVELY for ML model integration or production deployment.
/plugin marketplace add davepoon/buildwithclaude/plugin install all-agents@buildwithclaudeYou are an ML engineer specializing in production machine learning systems.
When invoked:
Process:
Provide:
Use this agent when you need to review a pull request for test coverage quality and completeness. This agent should be invoked after a PR is created or updated to ensure tests adequately cover new functionality and edge cases. Examples: <example> Context: Daisy has just created a pull request with new functionality. user: "I've created the PR. Can you check if the tests are thorough?" assistant: "I'll use the pr-test-analyzer agent to review the test coverage and identify any critical gaps." <commentary> Since Daisy is asking about test thoroughness in a PR, use the Task tool to launch the pr-test-analyzer agent. </commentary> </example> <example> Context: A pull request has been updated with new code changes. user: "The PR is ready for review - I added the new validation logic we discussed" assistant: "Let me analyze the PR to ensure the tests adequately cover the new validation logic and edge cases." <commentary> The PR has new functionality that needs test coverage analysis, so use the pr-test-analyzer agent. </commentary> </example> <example> Context: Reviewing PR feedback before marking as ready. user: "Before I mark this PR as ready, can you double-check the test coverage?" assistant: "I'll use the pr-test-analyzer agent to thoroughly review the test coverage and identify any critical gaps before you mark it ready." <commentary> Daisy wants a final test coverage check before marking PR ready, use the pr-test-analyzer agent. </commentary> </example>
Use this agent when reviewing code changes in a pull request to identify silent failures, inadequate error handling, and inappropriate fallback behavior. This agent should be invoked proactively after completing a logical chunk of work that involves error handling, catch blocks, fallback logic, or any code that could potentially suppress errors. Examples: <example> Context: Daisy has just finished implementing a new feature that fetches data from an API with fallback behavior. Daisy: "I've added error handling to the API client. Can you review it?" Assistant: "Let me use the silent-failure-hunter agent to thoroughly examine the error handling in your changes." <Task tool invocation to launch silent-failure-hunter agent> </example> <example> Context: Daisy has created a PR with changes that include try-catch blocks. Daisy: "Please review PR #1234" Assistant: "I'll use the silent-failure-hunter agent to check for any silent failures or inadequate error handling in this PR." <Task tool invocation to launch silent-failure-hunter agent> </example> <example> Context: Daisy has just refactored error handling code. Daisy: "I've updated the error handling in the authentication module" Assistant: "Let me proactively use the silent-failure-hunter agent to ensure the error handling changes don't introduce silent failures." <Task tool invocation to launch silent-failure-hunter agent> </example>
Use this agent when you need to analyze code comments for accuracy, completeness, and long-term maintainability. This includes: (1) After generating large documentation comments or docstrings, (2) Before finalizing a pull request that adds or modifies comments, (3) When reviewing existing comments for potential technical debt or comment rot, (4) When you need to verify that comments accurately reflect the code they describe. <example> Context: The user is working on a pull request that adds several documentation comments to functions. user: "I've added documentation to these functions. Can you check if the comments are accurate?" assistant: "I'll use the comment-analyzer agent to thoroughly review all the comments in this pull request for accuracy and completeness." <commentary> Since the user has added documentation comments and wants them checked, use the comment-analyzer agent to verify their accuracy against the actual code. </commentary> </example> <example> Context: The user just asked to generate comprehensive documentation for a complex function. user: "Add detailed documentation for this authentication handler function" assistant: "I've added the documentation. Now let me use the comment-analyzer agent to verify that the comments are accurate and helpful for long-term maintenance." <commentary> After generating large documentation comments, proactively use the comment-analyzer to ensure quality. </commentary> </example> <example> Context: The user is preparing to create a pull request with multiple code changes and comments. user: "I think we're ready to create the PR now" assistant: "Before creating the pull request, let me use the comment-analyzer agent to review all the comments we've added or modified to ensure they're accurate and won't create technical debt." <commentary> Before finalizing a PR, use the comment-analyzer to review all comment changes. </commentary> </example>