| Use when adding, modifying, debugging, or reviewing dd-trace-js serverless platform integrations that create 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.
npx skills add https://github.com/DataDog/dd-trace-js --skill serverless-integrations
Use this skill for platform-boundary instrumentation where dd-trace-js owns the function invocation lifecycle.
Use apm-integrations instead for ordinary library instrumentation that runs inside a serverless function.
Classify the request before touching code:
| Request shape | Skill path | Span model |
| --- | --- | --- |
| Trace a third-party library call inside Lambda/Azure/GCP | apm-integrations | Child spans under the invocation |
| Trace the cloud function invocation itself | This skill | Root type = 'serverless' span |
| Trace an HTTP, queue, or event trigger | This skill plus trigger references | Root span plus context or links |
| Change AWS Lambda bootstrap or timeout behavior | This skill | Special-case runtime wrapper path |
Do not model ordinary library plugins after the Lambda bootstrap. Lambda is a compatibility/runtime wrapper path,
not the default architecture for new integrations.
static kind = 'server' and static type = 'serverless'.TracingPlugin unless a more specific local pattern clearly applies. Do not default to ServerPlugin justbecause the span kind is server.
timeout, and runtime shutdown when the platform exposes it.
batch span links.
published events even when the tracing plugin is disabled.
requires dynamic interception, and document why.
references/architecture.md to confirm whether the work is serverless-root or ordinary APM.references/reference-integrations.md and inspect at least one matching in-repo implementation.references/implementation-guide.md.references/testing-guide.md.packages/datadog-instrumentations/ when the runtime can be observed throughnormal hooks.
packages/datadog-plugin-<name>/ when spans are created from diagnostic-channelevents.
packages/dd-trace/src/plugins/index.js.packages/dd-trace/src/service-naming/schemas/*/serverless.js.surface changes.
web.patch, web.startServerlessSpanWithInferredProxy,and web.finishAll when they match the trigger model.
upstream contexts.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take datadog/serverless-integrations from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.