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SkillsMP has collected 14 skills from DataDog/dd-trace-py. Open a skill to review its source and details.
Showing 14 of 14 collected skills.
dd-trace-py integration development guide. Use when creating, modifying, or debugging contrib integrations in the Python tracer. Covers the patch module system, context_with_data, context_with_event (new), registration, testing through the repository test…
Diagnose and fix slow base venv build times caused by unnecessary recompilation of native extensions (CMake, Cython, Rust) across test environment builds. Use when CI base venv builds are slow, when ext_cache isn't saving time, or when investigating warm…
dd-trace-py LLMObs integration development guide. Use when creating, modifying, or debugging LLMObs integrations for LLM/AI libraries in the Python tracer. Covers BaseLLMIntegration, stream handling, message extraction, token counting, tool call parsing, and…
Decide whether a release note is needed, and if so create or update a Reno fragment, following dd-trace-py's conventions (docs/releasenotes.rst).
Validate code changes by intelligently selecting and running the appropriate test suites. Use this when editing code to verify changes work correctly, run tests, validate functionality, or check for regressions. Automatically discovers affected test suites,…
Run targeted linting, formatting, and code quality checks on modified files. Use this to validate code style, type safety, security, and other quality metrics before committing. Supports running all checks or targeting specific checks on specific files for…
Run the dependency direction detector against ddtrace and propose architectural fixes for any violations found. Use this when adding or refactoring modules under ddtrace/internal, ddtrace/contrib, or any product package, or when the detect_layering_violations…
Run circular import detection against ddtrace and propose architectural fixes for any cycles found. Use this when adding or refactoring modules, or when the detect_circular_imports CI job reports new cycles on a PR.
Register a new environment variable / configuration option in dd-trace-py. Use whenever you add (or rename) a DD_*/_DD_*/OTEL_*/DATADOG_* environment variable so it is documented, validated, and tracked for cross-language feature parity. Covers…
Native crash log analysis for dd-trace-py
Run performance benchmarks to measure the impact of code changes. Discovers relevant benchmark scenarios based on changed files, executes them comparing a baseline version against local changes, and summarizes performance results. Use this when touching…
Review CI results for the current branch, commit, or PR using the Datadog MCP. Use this when CI is failing, to understand what's blocking a PR, or to get actionable fix instructions for failed jobs and tests.
Compare CPython source code between two Python versions to identify changes in headers and structs. Use this when adding support for a new Python version to understand what changed between versions.
Find all CPython internal headers and structs used in the codebase, particularly for profiling functionality. Use this when adding support for a new Python version to identify what CPython internals we depend on.