Skill to resolve Docker vulnerabilities for the firebase-cli image. Use this skill when you need to check for vulnerabilities in the firebase-cli Docker image and address them.
npx skills add https://github.com/firebase/firebase-tools --skill resolve-docker-vulnerabilities
This skill guides you through the process of listing images, checking for vulnerabilities, planning remediation, and verifying the fixes by publishing to a staging repository.
Run the build on fir-tools-builds and publish to the staging repository in firebase-cli to see the baseline vulnerabilities after the build's own updates.
./scripts/publish/firebase-docker-image/run.sh --build-project fir-tools-builds --repo staging --target firebase-cli
Check the vulnerability reports for the image just pushed to staging. You will need to find the digest of the image first.
gcloud artifacts docker images list us-docker.pkg.dev/firebase-cli/staging/firebase
Then check vulnerabilities using the digest:
gcloud artifacts vulnerabilities list us-docker.pkg.dev/firebase-cli/staging/firebase@sha256:<DIGEST>
To investigate which layers and file paths are causing the vulnerabilities, run the command with --format=json:
gcloud artifacts vulnerabilities list us-docker.pkg.dev/firebase-cli/staging/firebase@sha256:<DIGEST> --format=json
Look for fileLocation and layerDetails in the output to understand if the vulnerability is in:
/usr/local/node_packages/node_modules). Recommend updating the package.json and running the build again. You can use overrides as needed here to upgrade transitive dependencies to non-breaking versions./usr/local/lib/node_modules/npm). Recommend waiting for upstream fixes (which will be pulled in as soon as they are available)./root/.cache/firebase/emulators). Recommend raising these issues to the team owning the emulator.For each vulnerable package identified:
Present the proposed plan to the user for approval before making changes.
After making changes to the Dockerfile or related files, repeat Step 1 and Step 2 to publish a new staged image and verify that the vulnerabilities have been resolved.
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 firebase/resolve-docker-vulnerabilities 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.