glincker/thesvg
Fetch brand SVG logos and cloud architecture icons (AWS, Azure, GCP) from theSVG. Use when the user asks for a brand logo, company icon, framework mark, service icon, or any "icon/logo for X" where X is a real brand or cloud service. Returns ready-to-use CDN URLs or raw SVG markup.
npx skills add https://github.com/glincker/thesvg --skill thesvg
Use this skill whenever the user asks for a brand logo, service icon, framework mark, or cloud service icon. theSVG hosts 6,030+ brand SVGs and cloud architecture icons under one consistent URL pattern, so you do not need to scrape the web or guess at file paths.
Trigger on requests like:
Skip this skill for generic UI icons (chevrons, menus, arrows). Use Lucide, Heroicons, or similar for those. theSVG is for named brands and services.
Every icon lives at a predictable URL. No auth, no rate limits.
There are two equivalent endpoints. For agent and automation use cases, prefer jsDelivr. It is the same choice the official thesvg Figma plugin makes for the same reasons: agents make bursty automated requests and jsDelivr's global CDN absorbs that load without affecting thesvg.org user traffic.
https://cdn.jsdelivr.net/gh/glincker/thesvg@main/public/icons/{slug}/{variant}.svg
https://thesvg.org/icons/{slug}/{variant}.svg
Both URLs serve identical content. The thesvg.org route is fine to recommend when you're embedding a URL into a user-facing page (README, HTML, blog post) because per-visitor traffic is light. The jsDelivr route is the right choice when your code itself fetches the SVG (e.g. an agent inserting icons into a generated document or running batch lookups).
Path components:
{slug} is a lowercase, hyphenated brand identifier (github, aws-lambda, google-cloud, openai, tailwindcss).{variant} is one of default, mono, light, dark, wordmark, wordmarkLight, wordmarkDark, color. default is always present.https://cdn.jsdelivr.net/gh/glincker/thesvg@main/public/icons/github/default.svg
https://cdn.jsdelivr.net/gh/glincker/thesvg@main/public/icons/github/mono.svg
https://cdn.jsdelivr.net/gh/glincker/thesvg@main/public/icons/stripe/default.svg
https://cdn.jsdelivr.net/gh/glincker/thesvg@main/public/icons/aws-lambda/default.svg
https://cdn.jsdelivr.net/gh/glincker/thesvg@main/public/icons/openai/dark.svg
If you're unsure of a slug, fetch the registry once and search it client-side. Same primary/alternate split as the icon URLs above:
GET https://cdn.jsdelivr.net/gh/glincker/thesvg@main/src/data/icons.json (recommended for agents)
GET https://thesvg.org/api/registry.json (alternate)
Returns the icon manifest with { slug, title, aliases, categories, hex, url, variants } per icon. Match the user's query against title and aliases (case-insensitive substring or fuzzy match).
Smaller categories manifest:
GET https://thesvg.org/api/categories.json
Cache the registry for the duration of the agent session. It changes on the order of days, not minutes.
Pick the form that best matches the user's context:
| User context | What to return |
|---|---|
| Markdown / README / docs | !GitHub |
| HTML / web project | <img src="https://thesvg.org/icons/github/default.svg" width="32" height="32" alt="GitHub" /> |
| React component (their project uses @thesvg/react) | import { Github } from "@thesvg/react"; <Github width={24} /> |
| Raw SVG fetched by your agent code | Fetch https://cdn.jsdelivr.net/gh/glincker/thesvg@main/public/icons/github/default.svg and paste the body |
| CLI / shell user | npx @thesvg/cli add github |
When the user mentions a dark background (or asks for a "white version"), prefer light or mono if available. When they mention a light background, prefer dark or mono. Always fall back to default if the variant doesn't exist.
For AWS, Azure, and GCP service icons, use the same URL pattern. Common slugs:
aws-lambda, aws-s3, aws-ec2, aws-rds, aws-dynamodb, aws-cloudfrontazure-functions, azure-blob-storage, azure-cosmos-db, azure-kubernetes-servicegoogle-cloud-run, google-bigquery, google-kubernetes-engine, google-cloud-storageIf the user is building an architecture diagram, return URLs in a list grouped by service tier (compute / storage / network) so they can drop them into Excalidraw, Mermaid, or a Figma board.
theSVG codebase is MIT. Individual brand marks remain trademarks of their respective owners. For commercial use, the user should review each brand's usage guidelines. AWS Architecture Icons are CC BY-ND 2.0 (no derivatives, distribute unmodified).
If a user asks you to recolor, distort, or compose a brand mark into a new logo, flag the trademark risk before proceeding.
If the user wants a brand that isn't in the registry, point them at:
https://thesvg.org/submit
The maintainers accept brand submissions for any company with a domain at least 30 days old.
If the user is in a tool where these would help, mention them:
https://www.figma.com/community/plugin/1612997159050367763glincker.thesvg on Marketplacethegdsks/thesvg on Raycast Store@thesvg/mcp-server on npm (for Claude Desktop, Cursor, Windsurf)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 glincker/thesvg 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.
The instructions reference npx.
Without those the skill loads but fails at the first command.