DataDog

Generate the Agent Supply Chain newsletter by researching team activity on GitHub and Confluence, then creating a Confluence draft and Gmail draft

Explains a lading.yaml config file from the regression test suite, using the lading Rust source as ground truth for field meanings and defaults.

Review and triage PR review comments on the current branch — groups threads by file, separates bots from humans, walks through unresolved comments interactively

Give your AI agents something more useful than a prompt. Velocity through clarity.

Create a pull request for the current branch with proper labels and description

Create a new Fx component using the modern def/fx/impl pattern (NOT legacy)

Add a new configuration field to the Datadog Agent (datadog.yaml)

Create a new Go core check that collects metrics and sends them to Datadog

Datadog API CLI with 49 command groups, 300+ subcommands. Skills and domain agents for monitoring, logs, APM, security, and infrastructure.

File GitHub issues to the right repository (pup CLI or plugin)

Load when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.

Load when investigating a failing PR CI pipeline or checking PR health. Attributes each CI failure as flaky, infra, or regression, proposes a targeted action, and reports code coverage.

Log management - search, pipelines, archives, and cost control.

Monitor management - create, update, mute, and alerting best practices.

Live Debugger - inspect runtime argument/variable values in production by placing log probes on methods. Use when asked what values a function receives, what parameters look like at runtime, or to capture live data from running services without redeploying.

Symbol Database - search service symbols, find probe-able methods.

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 efficient validation.

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, selects the minimal set of venvs needed for validation, and handles test execution with Docker services as needed.

Diagnose and fix slow base venv build times caused by unnecessary recompilation of native extensions (CMake, Cython, Rust) across riot generate runs. Use when CI base venv builds are slow, when ext_cache isn't saving time, or when investigating warm build regressions.

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 performance-sensitive code paths or when asked about performance impact.

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.

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.

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.

Improve test coverage for shell features and commands using reference test suites from yash, GNU coreutils, and uutils/coreutils

Read PR review comments, evaluate validity, implement fixes, push changes, and reply/resolve threads

Diagnose and fix CI failures on a GitHub PR by analyzing failing checks, reading logs, and applying fixes

Self-review a PR, fix all issues, and re-review in a loop until clean. Coordinates code-review, address-pr-comments, and fix-ci-tests skills.

Implement a new POSIX command as a builtin in the safe shell interpreter

Systematically review and improve every shell feature and builtin command. Iterates through each feature/command, runs code-review, fixes issues, and re-reviews until clean.

Comprehensive code review covering security, correctness, bash compatibility, test coverage, and code quality. Use for PRs, commits, or any code changes.

Fix failing tests by prioritising shell implementation fixes to match bash behaviour

Dev/test workflow for tracer engineers working on the Datadog .NET tracer — build a local Datadog.AzureFunctions NuGet package, deploy it to a test Azure Function App, trigger it, and analyze traces/logs to verify instrumentation behavior. Use this skill whenever the user is working on Azure Functions instrumentation: building or testing the Datadog.AzureFunctions NuGet package, deploying to a test Function App, analyzing instrumentation logs or spans from an Azure Functions app, or configuring Datadog environment variables on Azure — even if they don't explicitly invoke /azure-functions.

Update/bump the libdatadog native library version in dd-trace-dotnet. Use when the user asks to bump, update, or upgrade libdatadog, or mentions a new libdatadog release version.

Analyze Azure DevOps CI build failures in dd-trace-dotnet pipeline. This skill should be used when the user mentions a failing CI build, PR checks failing, Azure DevOps pipeline failures, test failures in CI, or when they share a build ID or PR number and want to understand what went wrong. Analyzes build failures, categorizes them (infrastructure/flaky/real), and provides actionable recommendations.

Error Stack Trace Analysis for dd-trace-dotnet

Stack Trace Crash Analysis for dd-trace-dotnet

Perform a review on a GitHub PR, leaving comments on the PR

Environment validation checklist. Run this FIRST when starting a new Claude session to verify the environment is ready for optimization work. Checks Rust toolchain, ci/ scripts, build, benchmarking tools, profilers, memory tools, git state, and SMP CLI.

Prepare a lading release. Updates CHANGELOG.md, bumps version in Cargo.toml, updates Cargo.lock, commits, and creates a PR.

Coordinates optimization attempts. Captures baselines, implements changes, invokes review, and records outcomes.

Reviews optimization patches using a 5-persona peer review system. Requires unanimous approval backed by benchmarks.

Full optimization workflow with git branch creation, commits, and optional PR. Wraps /lading-optimize-hunt with git automation.

Finds a valid optimization target in lading. Returns a filled target.yaml template with pattern, technique, target, file, bench, and fingerprint. Use before /lading-optimize-hunt or when selecting a new optimization target.

Use when creating a pull request in dd-sdk-ios. Use when writing PR titles, PR body, or choosing the target branch.

Use when creating a new branch in dd-sdk-ios for a JIRA ticket or feature. Use when choosing a branch name or base branch for development work.

Use when committing changes in dd-sdk-ios. Use when writing commit messages, signing commits, or staging files before a commit.

Use when asked to run tests in the dd-sdk-ios project — whether a full module suite, a specific test class, or a single test method. Use when choosing between make, xcodebuild, or Xcode MCP for running iOS/tvOS/visionOS tests.

Use when adding, removing, moving, or renaming Swift source files in the dd-sdk-ios Xcode project. Use when the task involves file creation, deletion, or relocation in any module (DatadogRUM, DatadogLogs, DatadogCore, etc.). Use when you would otherwise reach for Write, Bash mv/mkdir/rm, or manual pbxproj editing for file management.

Converts Spock/Groovy test files in a Gradle module to equivalent JUnit 5 Java tests. Use when asked to "migrate groovy", "convert groovy to java", "g2j", or when a module has .groovy test files that need to be replaced with .java equivalents.

Write a new library instrumentation end-to-end. Use when the user ask to add a new APM integration or a library instrumentation.

Convert JUnit 5 @MethodSource/@CsvSource/@ValueSource parameterized tests to @TableTest (JDK8)

Analyze branch changes for technical debt, code duplication, and unnecessary complexity

Use when instrumenting a Bazel Java repository or monorepo with Datadog Test Optimization. Applies to Bzlmod and WORKSPACE consumers, direct java_test targets, repository-owned Java/JUnit wrapper macros, doctor/uploader validation, and RFC-safe setup that avoids manual tracer payload wiring, DD_GIT_* test environment variables, uploader credentials in test sandboxes, and missing remote outputs.

Use when instrumenting a Bazel Go repository or monorepo with Datadog Test Optimization and Orchestrion. Applies to WORKSPACE and Bzlmod consumers, large monorepos with local Go wrappers, doctor/uploader validation, and RFC-safe setup that avoids patches, payload proxies, DD_GIT_* test environment variables, and missing remote outputs.

Use when porting this repository's vendored Orchestrion-enabled rules_go fork from its current upstream base to another upstream rules_go tag or commit. Applies to base and complete variants, metadata regeneration, variant-drift checks, smoke validation, and migration PR preparation.

Use when instrumenting a Bazel Python repository or monorepo with Datadog Test Optimization. Applies to Bzlmod and WORKSPACE consumers, managed pytest targets, repository-owned pytest wrappers, consumer_runner mode, doctor/uploader validation, and RFC-safe setup that avoids payload proxies, DD_GIT_* test environment variables, and missing remote outputs.

Use when adding, debugging, or modifying LLMObs plugins for an LLM library in dd-trace-js. Triggers: "add LLMObs support", "instrument chat completions / streaming / embeddings / agent runs / orchestration / tool calls / retrieval", "LLMObsPlugin", "getLLMObsSpanRegisterOptions", "setLLMObsTags", "LlmObsCategory", "LlmObsSpanKind", any provider tag ("openai" / "anthropic" / "genai" / "google" / "langchain" / "langgraph" / "ai-sdk" llmobs), "VCR cassettes".

Use when writing, modifying, or debugging tests for an LLMObs plugin in dd-trace-js. Triggers: "write LLMObs tests", "test an LLMObs plugin", "assertLlmObsSpanEvent", "useLlmObs", "getEvents", any MOCK_* matcher ("MOCK_STRING" / "MOCK_NOT_NULLISH" / "MOCK_NUMBER" / "MOCK_OBJECT"), "VCR cassette", "vcr proxy", "127.0.0.1:9126", any LlmObsCategory test ("LLM_CLIENT" / "MULTI_PROVIDER" / "ORCHESTRATION" / "INFRASTRUCTURE").

This skill should be used when the user asks to "add a new integration", "instrument a library", "add instrumentation for", "create instrumentation", "new dd-trace integration", "add tracing for", "TracingPlugin", "DatabasePlugin", "CachePlugin", "ClientPlugin", "ServerPlugin", "CompositePlugin", "ConsumerPlugin", "ProducerPlugin", "addHook", "shimmer.wrap", "orchestrion", "bindStart", "bindFinish", "startSpan", "diagnostic channel", "runStores", "reference plugin", "example plugin", "similar integration", or needs to build, modify, or debug the instrumentation and plugin layers for a third-party library in dd-trace-js.

Analyze agent-data-plane binary size to identify top crates and modules contributing to binary size, and evaluate optimization opportunities. TRIGGER when: user asks about binary size, code size, shrinking the binary, binary optimization, reducing binary footprint, evaluating what contributes to binary size, or asks to find ways to make the binary smaller. DO NOT TRIGGER when: user asks about runtime memory usage, memory leaks, allocation profiling, or performance tuning.

Trigger CI for an external contributor's pull request by mirroring their fork branch onto DataDog/saluki under an `<owner>/<branch>` name. External forks don't run CI by default for security reasons; this skill lets an authorized maintainer push the fork's branch up to the main repo so the PR's CI checks attach to a trusted branch. TRIGGER when: user asks to run CI on an external/contributor PR, push an external branch for CI, mirror a fork branch, or invokes `/external-pr-ci-trigger`. DO NOT TRIGGER when: user wants to run CI on their own branch or on an internal PR — those run CI automatically.

Audit configuration keys (ConfKeys) that are used in agent-data-plane and in the Datadog Agent looking for differences that should be either documented or fixed. Maintains files at: - docs/agent-data-plane/configuration/dogstatsd.md - docs/agent-data-plane/configuration/dogstatsd/ Available in multiple modes. See .claude/skills/confkey/README.md Note, this skill is currently specialized for DogStatsD configuration, but may be more general in the future.

Create or review Stratus Red Team attack techniques. Use when asked to add, create, or implement a new attack technique for AWS, Azure, GCP, Entra ID, EKS, or Kubernetes in the stratus-red-team project. Also use when reviewing PRs or code that implements attack techniques — the guidelines serve as a review checklist.

Tests and validates Stratus Red Team attack techniques by executing warmup, detonation, and cleanup phases with comprehensive validation. Validates cloud credentials, parses command output, checks expected outcomes, and generates HTML reports. Use when testing attack techniques, validating TTPs, verifying technique behavior, or when user mentions stratus, warmup, detonate, or technique IDs like aws.*, azure.*, gcp.*, k8s.*, or entra-id.*. Supports AWS, Azure, GCP, Kubernetes, and Entra ID.