DataDogOfficial·11 Agent Skills Included

dd-trace-py

AI-assisted development toolkit for the Datadog Python tracing library

Guides AI agents through building, testing, and extending the Datadog Python tracer. Automates test selection, linting, benchmarking, CI failure review, and crash analysis without manual command guessing. Provides step-by-step patterns for adding library integrations, LLM observability, and new configuration options safely.

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npx skills add DataDog/dd-trace-py --all -g -y

Available:

Gives the AI agent strict project rules and routes every task, such as testing, linting, benchmarking, or integration work, to the correct skill instead of raw commands.

All Skills in This Repository (11)

Pure Emerald Level Indicators

📦 In Repo

DataDog

lint

Run Hatch-based lint, format, and quality checks on modified files.

find-cpython-usage

Identify CPython internal headers and structs used in the codebase.

releasenote

Create or update Reno release note YAML fragments for current branch changes.

run-tests

Identify changed files and run affected test suites via scripts/run-tests.

compare-cpython-versions

Compares two CPython versions' headers and structs to identify breaking changes and API modifications.

debug-build-times

Diagnose slow venv builds caused by unnecessary native extension recompilation across riot runs.

circular-import-analysis

Detect circular imports in ddtrace and propose architectural fixes for dependency cycles.

review-ci

Review CI failures for branches and PRs using Datadog MCP pipeline and test events.

dependency-direction-analysis

Detects layering violations in ddtrace module dependencies and proposes architectural fixes.

run-benchmarks

Runs performance benchmarks comparing baseline ddtrace releases against local code changes.

add-new-configuration

Registers new environment variables in dd-trace-py configuration files and documentation.

Official

Intermediate

Frequently Asked Questions

FAQPage Schema

How to install dd-trace-py skills?▼

Run `npx skills add DataDog/dd-trace-py --all -g -y` in your terminal to install all skills in this suite globally.

What is dd-trace-py used for?▼

It is Datadog's Python library for distributed tracing, profiling, error tracking, and LLM observability. These skills help AI agents contribute to that library correctly.

How do these skills help run tests?▼

The run-tests skill automatically finds the test suites affected by your code changes and runs only the minimal environments needed, saving hours compared to running the full suite.

Can AI add a new tracing integration with these skills?▼

Yes. The apm-integrations and llmobs-integrations skills give step-by-step patterns for patching new libraries and extracting LLM data, with tested reference examples.

Do these skills work with Claude Code and Cursor?▼

Yes. The AGENTS.md orchestrator is shared across tools, so Claude Code, Cursor, and other agents follow the same project rules and skill routing.