Diagnose and fix Single Step Instrumentation (SSI) issues on Linux hosts — SSI automatically instruments applications for APM without code changes. Only use if the agent and SSI are configured but traces are missing or instrumentation is not working.
Verify Single Step Instrumentation (SSI) is working end-to-end on Linux hosts — SSI automatically instruments applications for APM without code changes. Only use after enable-ssi has run.
Upgrade Datadog Browser SDK from v6 to v7. Use when encountering removed options like betaEncodeCookieOptions, allowFallbackToLocalStorage, trackBfcacheViews, usePciIntake, or when a project references datadoghq-browser-agent.com CDN with /v6/ paths.
Review a product direction before you build, launch or automate it. UX3 connects User Flow, Evidence Flow and Business Flow, then returns continue, verify or reframe with evidence gaps, human-owned decisions and a bounded next action. Use it for feature proposals, onboarding changes, experiments, launches and feedback reviews. Includes Quick Gate, Standard Gate and optional UX3 Council modes, product briefs, handoff templates, JSON schemas, worked examples and local Python validators. MIT open source; keep the complete resources folder. Local validation needs Python 3 and jsonschema. Examples are illustrative; host permissions and human approvals remain required. Source and install: https://github.com/cis2042/ux3-product-design-harness Website: https://cis2042.github.io/ux3-product-design-harness/ For adoption inquiries, share your team and one real product decision: https://www.linkedin.com/in/cis2042/
Investigate a potentially compromised Datadog API key — timeline of actions, geo/IP breakdown, endpoints called, anomaly flags, and remediation steps.
Datadog Browser SDK — RUM, Logs, Session Replay, profiling, product analytics, and error tracking setup, configuration, and migration. Use when upgrading Browser SDK versions, setting up RUM or Logs, or troubleshooting browser-side Datadog instrumentation.
Answer "who did what" security questions from Audit Trail — deletions, config changes, login activity, permission changes, actions from a specific user or IP.
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare experiments", "analyze against baseline", or provides one or two experiment IDs for analysis.
Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. Use when the user says "generate Python experiment", "write an SDK experiment", "create a ddtrace experiment", "Python notebook experiment", "use the LLM Obs SDK", or has `ddtrace` installed and wants idiomatic SDK code.
Classify whether user intent was satisfied in a Datadog LLM Obs trace or session. Three modes: (1) session_id — classify a single CMD+I assistant session with RUM; (2) trace_id — classify a single LLM Obs trace without RUM; (3) ml_app — sample and classify multiple sessions or traces from a given LLM app. Output is compact by default (verdict + one-sentence reason). Use when evaluating satisfaction, classifying sessions/traces, labeling data, or generating signal for llm-obs-eval-pipeline or llm-obs-trace-rca.
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 and quality/security status.
Azure Communication Services common utilities for Java. Use when working with CommunicationTokenCredential, user identifiers, token refresh, or shared authentication across ACS services.