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Review and improve code changes on the current branch using Pipecat conventions. Refine names and structure, add type hints and documentation, check example patterns, and make conservative performance improvements while preserving compatibility.

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• Cleaning up branch changes before review
• Adding missing type hints and documentation
• Checking Pipecat service and example patterns

Research new models and API capabilities offered by providers behind Pipecat services. Produce dated reports for each service and local committed branches for clear updates, with review commands and separate publishing steps.

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• Checking providers for new models and API features
• Researching selected providers or service units
• Preparing local branches for clear updates

Combine all service research reports for a selected date into a local Markdown digest. Add up to five highlights to help maintainers prioritize updates, review proposed changes, and spot research errors.

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• Summarizing a day's service research
• Highlighting updates that need review
• Refreshing a digest after more research

Add or improve Google-style docstrings, the explanations embedded in Python code, for modules and classes. Document constructor arguments, public methods, configuration fields, and enum values while preserving existing documentation that already meets the project’s conventions.

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• Document a Python module
• Explain constructor arguments and public methods
• Describe configuration fields and enum values

Update documentation to match source changes on the current branch. Map changed files to existing pages, verify behavior against the code, revise affected references and examples, and report missing documentation using repository-specific mapping rules.

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• Updating API documentation after code changes
• Fixing guides and examples affected by renamed parameters
• Finding missing documentation pages or sections

Draft or update GitHub pull request descriptions using the actual changes and related context. Explain the problem, changed behavior, and review concerns in plain language, including breaking changes and explicit issue-closing references when applicable.

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• Draft a description for a new pull request
• Rewrite a description to reflect the final implementation
• Explain behavior changes and review concerns

Review newly added comments, docstrings, and changelog entries so future readers can understand the current code. Revise development history, redundant explanations, and unclear shorthand while preserving constraints that the code cannot explain on its own.

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• Review comments before committing
• Check prose added across a branch
• Clarify function documentation for future readers

Reorganize a branch's commit history into a few logical commits without changing file contents. Create a backup first, propose groups for approval, remove merges from main, and check the resulting history and working tree.

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• Tidying commit history before a pull request
• Grouping changes by feature, fix, tests, or documentation
• Removing merges from main while preserving file contents

Submit current work as a GitHub pull request, handling relevant uncommitted changes and reviewing prose before pushing. After creating the PR, run companion skills to generate changelog files, update the description, and return its URL.

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• Submitting current work as a GitHub pull request
• Creating a branch and commits for uncommitted changes
• Reviewing prose and adding a changelog before handoff

Review GitHub pull requests with specialized agents that check for clear bugs and violations of project instructions in CLAUDE.md. Validate bug findings before posting focused comments on the affected lines, with fix suggestions where appropriate.

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• Reviewing a pull request for clear bugs
• Checking changes against CLAUDE.md rules
• Posting validated findings on affected lines

Create changelog files for significant changes in a pull request, a proposed code change. Describe effects users will notice and organize entries by PR number and change type, such as features or fixes.

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• Writing changelog entries for a pull request
• Separating new features from bug fixes
• Explaining changes in user-facing language

Create, refine, or fix NVIDIA voice agents (Cascaded or Omni) with Pipecat or LiveKit. Use when building, scaffolding, or iterating on a real-time voice-agent pipeline, including speech (ASR/TTS) customization and cloud or local deployment. Not for offline/batch speech-to-text, text-only chat or RAG, or generic Docker or CUDA work unrelated to a voice agent.

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Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

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