datadog
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- ▌ Manual Testing · datadogRun a manual test of the current change end-to-end and output reproducible test instructions for the PR "Test instructions" section.
- ▌ Code Generation · datadogUse pup CLI for immediate Datadog operations or generate code for integration into applications
- ▌ Test Attack Technique · datadog bundleTests 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.
- ▌ Create Attack Technique · datadog bundleCreate 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.
- ▌ Map Threat Intel Coverage · datadog bundleAnalyze a threat-intel blog post or report against Stratus Red Team coverage. Extract cloud TTPs, classify each as Covered / Partial / Open issue / Not covered, produce a markdown coverage report, and suggest GitHub issue drafts for the gaps. Use when the user pastes a threat-intel URL (Microsoft, Mandiant, CrowdStrike, Unit 42, etc.) and asks for a coverage report, gap analysis, TTP extraction, or "what does Stratus cover from this report". Also use when given a local report file (.md, .txt, .pdf) or pasted text.
- ▌ Dd Tf Provider Test Runner Agent · datadogRun Datadog Terraform provider acceptance tests with proper RECORD modes and capture output for analysis. This agent is invoked via Task tool and writes results to files.
- ▌ Generate Datadog Datasource · datadog bundleGenerate a Datadog Terraform provider data source end to end and open a review-ready GitHub PR for it. Runs in three phases: (1) Input — collect the read group (operationIds), artifact name, cardinality, description, and overwrite target; (2) Generation — build an annotated OpenAPI slice with slice_and_annotate.py, run tfgen on it, run make docs/build, and commit onto a new branch; (3) PR — a quick runtime-risk scan (trusting the generator for correctness), draft the standard PR body with disclaimers + testing guide, and open the PR with `gh`. Use this skill whenever the user wants to generate a Datadog data source, mentions tfgen / generator-v2, slice_and_annotate, an OpenAPI operation they want a data source for, opening a PR for a generated data source, evaluating generated code against goldens, or writing cassette / acceptance-test instructions — even if they don't say "skill".
- ▌ Fix Broken Integration Tests · datadog bundleEnd-to-end workflow to diagnose, reproduce, fix, and validate a failing Datadog Terraform provider integration test. Takes any input pointing at specific tests: test function names, error messages, resource names, or any other description of what's failing. Runs autonomously through 8 phases — identify → validate in CI → reproduce locally → plan fix → execute → open draft PR → monitor → report.
- ▌ Datadog Go Test Optimization Onboarding · datadog bundleUse 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.
- ▌ Datadog Java Test Optimization Onboarding · datadog bundleUse 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.
- ▌ Datadog Python Test Optimization Onboarding · datadog bundleUse 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.
- ▌ Datadog Rules Go Orchestrion Upstream Migration · datadog bundleUse 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 support lines, metadata regeneration, profile verification, smoke validation, and migration PR preparation.
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- ▌ Azure Functions · datadog bundleDev/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.
- ▌ Bump Libdatadog · datadog bundleUpdate/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 Azdo Build · datadog bundleAnalyze 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.
- ▌ Llmobs Testing · datadog bundleUse 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", "record a cassette", "test:llmobs:plugins".
- ▌ Flaky Test Fixer · datadogUse when classifying, investigating, or fixing a suspected flaky test, intermittent test result, nondeterministic CI test failure, timing race, hang, or test-order dependency in dd-trace-js. Classifies infrastructure and deterministic failures before reproduction or code search.
- ▌ Llmobs Integration · datadog bundleUse 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", "SPAN_KINDS", "span kind", any provider tag ("openai" / "anthropic" / "genai" / "google" / "langchain" / "langgraph" / "ai" llmobs), "VCR cassettes".
- ▌ Architecture Review · datadogUse when a dd-trace-js change introduces or substantially changes a class hierarchy, module boundary, shared helper layer, public API, or duplicated behavior across multiple types. Triggers: architecture decision, design review, refactor shared behavior, new abstraction, composition versus inheritance, expose internals, module coupling, public surface, hot-path architecture, score the design.
- ▌ Serverless Integrations · datadog bundleUse when adding, modifying, debugging, or reviewing dd-trace-js serverless platform integrations that create root invocation spans for AWS Lambda, Azure Functions, Google Cloud Functions, or similar runtimes. Triggers: serverless integration, function invocation root span, Lambda runtime, Azure Functions, GCP Functions, type = 'serverless', DD_LAMBDA_HANDLER, datadog-lambda-js, deployed serverless verification, manual serverless test.
- ▌ Release · datadogPrepare a lading release. Updates CHANGELOG.md, bumps version in Cargo.toml, updates Cargo.lock, commits, and creates a PR.
- ▌ Lading Preflight · datadog bundleEnvironment 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.
- ▌ Lading Optimize Hunt · datadog bundleCoordinates optimization attempts. Captures baselines, implements changes, invokes review, and records outcomes.
- ▌ Lading Optimize Review · datadog bundleReviews optimization patches using a 5-persona peer review system. Requires unanimous approval backed by benchmarks.
- ▌ Lading Optimize Submit · datadog bundleFull optimization workflow with git branch creation, commits, and optional PR. Wraps /lading-optimize-hunt with git automation.
- ▌ Lading Optimize Find Target · datadog bundleFinds 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.
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- ▌ Test Module · datadogTest a pathrunner exploit module against a deployed pathfinding-labs scenario. Supports iterative fix-and-retry — the skill can loop up to N times, diagnosing pathrunner-side failures and applying fixes between runs.
- ▌ Batch Modules · datadogBatch-create pathrunner exploit modules from a list of pathfinding-labs coverage gaps, using a rolling pool of concurrent sub-agents that enable → build → test → disable each lab. Verify stage iteratively fixes pathrunner-side failures (default budget 5 per module). Uses multi-agent orchestration.
- ▌ Create Module · datadog bundleCreate a new pathrunner exploit module from pathfinding.cloud path definition
- ▌ Plabs Lifecycle · datadogEnable, disable, and swap pathfinding-labs scenarios via plabs, serializing terraform applies through a shared lock so concurrent agents don't collide
- ▌ Cleanup Scenario · datadogClean up AWS resources after testing a pathrunner module against a pathfinding-labs scenario
- ▌ Panoramic · datadogRun Saluki's Panoramic correctness and integration tests non-interactively and investigate their machine-readable results. Read this before running `panoramic`, `make test-integration`, or `make test-correctness`, or when diagnosing a test from its artifacts.
- ▌ Config System · datadogArchitecture and working rules for Saluki's configuration system and inventory. Read this when working with lib/agent-data-plane-config*, lib/datadog-agent/config*, GenericConfiguration, schema_overlay.yaml, SalukiConfiguration, DatadogConfiguration, or typed config migrations.
- ▌ Binary Size Analysis · datadogAnalyze 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.
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- ▌ External Pr CI Trigger · datadogTrigger 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.
- ▌ Dd Sdk IOS Open Pr · datadogUse when creating a pull request in dd-sdk-ios. Use when writing PR titles, PR body, or choosing the target branch.
- ▌ Dd Sdk IOS Git Branch · datadogUse 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.
- ▌ Dd Sdk IOS Git Commit · datadogUse when committing changes in dd-sdk-ios. Use when writing commit messages, signing commits, or staging files before a commit.
- ▌ Dd Sdk IOS Running Tests · datadogUse 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.
- ▌ Dd Sdk IOS Swift API Design · datadogUse when writing, reviewing, or renaming Swift APIs, including declarations, call sites, argument labels, Boolean names, protocols, overloads, documentation comments, and small abstractions.
- ▌ Dd Sdk IOS Update Feature Docs · datadogUse when public API changes have been made to review and update all *_FEATURE.md documentation files, or to audit whether they are still accurate.
- ▌ Dd Sdk IOS Xcode File Management · datadogUse 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.
- ▌ Techdebt · datadogReview a code diff / branch / PR for technical debt — code duplication, unnecessary complexity / over-engineering, and redundant or dead code. Use whenever the user wants a tech-debt, cleanup, or refactor review, asks to check a branch or PR for duplication / complexity / dead code before opening a PR, or mentions "techdebt". Refactor-only: it reports issues and offers behavior-preserving fixes; it never changes behavior.
- ▌ Perf Review · datadog bundlePerformance-overhead review of a code diff / branch / PR for the dd-trace-java tracer. Flags hot-path allocation, unbounded memory, repeated work, escaping objects, native-boundary crossings, and JVM-specific pitfalls (escape analysis, JNI / virtual-thread pinning, backtracking-regex ReDoS, varargs/boxing hashing, String.format, ByteBuddy-Advice anti-patterns) using the tracer performance rubric. Use whenever the user wants a performance / overhead / hot-path review, asks to check a diff or PR for allocation / GC / memory / latency / startup cost, or mentions the "perf rubric" or the "do no harm / assume hot" tracer posture — even if they just say "review this for perf" without naming the rubric. Advisory and READ-ONLY: it reports ranked, verify-first findings; it never edits code.
- ▌ Clarify Java Comments · datadog bundleClarify or review Java Javadocs, Javadoc tags, and explanatory code comments for legibility, accuracy, and source alignment. Use when asked to simplify verbose or generated comments, edit documentation in a local file, class, or member, repair Javadoc markup, propose copy-ready replacements, or add GitHub suggestions to an existing pending PR review. Documentation-focused: never change executable code or turn the task into a general code review. Do not submit a review unless explicitly requested.
- ▌ Migrate Groovy To Java · datadog bundleConverts 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.
- ▌ Review Groovy Migration · datadogPost-migration quality review. Checks Java test files produced by migrate-groovy-to-java against the shared quality rules. Use after migration, or on any branch with recently migrated .java test files. Produces structured FINDING blocks grouped by severity, then offers to auto-fix BLOCKERs and WARNINGs.
- ▌ Migrate Junit Source To Tabletest · datadogConvert JUnit 5 @MethodSource/@CsvSource/@ValueSource parameterized tests to @TableTest (JDK8)
- ▌ Code Review · datadogComprehensive code review covering security, correctness, bash compatibility, test coverage, and code quality. Use for PRs, commits, or any code changes.
- ▌ Fix CI Tests · datadogDiagnose and fix CI failures on a GitHub PR by analyzing failing checks, reading logs, and applying fixes
- ▌ Improve Loop · datadogSystematically review and improve every shell feature and builtin command. Iterates through each feature/command, runs code-review, fixes issues, and re-reviews until clean.
- ▌ Fix Local Tests · datadogFix failing tests by prioritising shell implementation fixes to match bash behaviour
- ▌ Review Fix Loop · datadogSelf-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.
- ▌ Gtfobins Validate · datadogValidate shell builtins against GTFOBins attack patterns to ensure exploits are blocked by the sandbox
- ▌ Address Pr Comments · datadogRead PR review comments, evaluate validity, implement fixes, push changes, and reply/resolve threads
- ▌ Improve Test Coverage · datadogImprove test coverage for shell features and commands using reference test suites from yash, GNU coreutils, and uutils/coreutils
- ▌ Implement Posix Command · datadogImplement a new POSIX command as a builtin in the safe shell interpreter
- ▌ Lint · datadogRun 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.
- ▌ Review CI · datadogReview 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 Tests · datadogValidate 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.
- ▌ Releasenote · datadogDecide whether a release note is needed, and if so create or update a Reno fragment, following dd-trace-py's conventions (docs/releasenotes.rst).
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- ▌ Run Benchmarks · datadogRun 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.
- ▌ Apm Integrations · datadog bundledd-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 runner, and common anti-patterns. LLM/AI integrations should use this skill for APM-side workflow only; use llmobs-integrations for LLMObs-specific lifecycle, extraction, streaming, and VCR guidance. Pin is DEPRECATED. Triggers: "dd-trace-py", "ddtrace", "contrib", "integration", "patch.py", "trace_handlers", "PATCH_MODULES", "context_with_data", "context_with_event", "TracingEvent", "VCR", "cassette", "generative-ai", "LLM integration", "run-tests", "suitespec", "new integration", "wrap", "unwrap".
- ▌ Debug Build Times · datadogDiagnose 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.
- ▌ Llmobs Integrations · datadog bundledd-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 VCR-based testing patterns. Triggers: "llmobs", "LLMObs", "BaseLLMIntegration", "llmobs_set_tags", "_llmobs_set_tags", "BaseStreamHandler", "submit_to_llmobs", "integration.trace", "LLM span", "VCR", "cassette", "anthropic", "openai", "google_genai", "claude_agent_sdk", "generative-ai", "LLM integration", "llmobs_enabled".
- ▌ Add New Configuration · datadogRegister 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 supported-configurations.json, the generated _supported_configurations.py module, docs/configuration.rst, and the feature-parity registry hand-off.
- ▌ Circular Import Analysis · datadogRun 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.
- ▌ Dependency Direction Analysis · datadogRun 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 CI job reports new violations on a PR.
- ▌ Pup · datadog bundleDatadog API CLI with 49 command groups, 300+ subcommands. Skills and domain agents for monitoring, logs, APM, security, and infrastructure.
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- ▌ Dd Debugger · datadogLive 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.
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- ▌ Dd Unblock Pr · datadogLoad 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.
- ▌ Dd Code Generation · datadogUse pup CLI for immediate Datadog operations or generate code for integration into applications
- ▌ Dd Triage Flaky Test · datadogLoad when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.
- ▌ Allium · datadog bundleGive your AI agents something more useful than a prompt. Velocity through clarity.
- ▌ Run E2e · datadog bundleRun one already-written new-e2e test locally and triage the setup failures that stop it — "run the containers e2e tests", "my e2e run fails before any test starts".
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- ▌ Auto Jira · datadog bundleAutonomously work on Jira backlog tickets, creating PRs and shepherding them to merge
- ▌ Create Pr · datadogCreate a pull request for the current branch with proper labels and description. Any agent opening a PR in this repo (via `gh pr create` or otherwise), whether invoked directly as /create-pr or as part of a larger task, MUST follow this skill's process rather than improvising.
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- ▌ Follow Pr · datadog bundleMonitor the current PR's GitLab pipeline to completion, then report success or investigate a failure. Use when the user asks to follow, babysit, watch, or wait on a PR/pipeline, or just after pushing to / creating a PR.
- ▌ Write E2e · datadog bundleWrite or extend Datadog Agent new-e2e tests, including fakeintake coverage and the GitLab wiring that runs them; derives scope from the current diff when no target is named. Not for running tests that already exist (run-e2e, run-windows-e2e), or for judging whether a behavior belongs in E2E at all (e2e-audit).
- ▌ Injector Dev · datadogBuild, deploy, and test Datadog Agent components (agent, cluster-agent, operator, CSI driver) on a local Kubernetes cluster using the injector-dev CLI. Use when the user wants to iterate on local Agent or Operator change, spin up a local k8s test environment.
- ▌ Cws Btfhub Sync · datadogSync CWS BTFHub constants after adding a new constantfetch offset, so pre-BTF kernels can resolve it. Use when KMT secagent jobs log "failed to fetch constant for <name>".
- ▌ Run Windows E2e · datadog bundleRun Windows E2E tests (MSI install tests or Fleet Automation/installer tests) locally against AWS-provisioned VMs
- ▌ Create Component · datadogCreate a new Fx component using the modern def/fx/impl pattern (NOT legacy)
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- ▌ Update Otel Deps · datadogUse when a user asks to update, bump, or troubleshoot OpenTelemetry Collector dependencies in datadog-agent, including OCB build failures, ddflareextension golden files, static quality gates, or OTel transitive dependency conflicts.
- ▌ Create Core Check · datadogCreate a new Go core check that collects metrics and sends them to Datadog
- ▌ Create Epic Recap · datadog bundleUse when an engineer or manager asks to recap, summarize, or post an update on a Jira Epic — a progress update for an in-progress Epic (how far along it is, what's shipped so far, what's next) or a resolution recap for a finished one. Gathers child-issue progress, merged GitHub PRs, release notes, and Epic/child comments, previews a stakeholder-ready recap, and posts only after approval.