Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.
npx skills add https://github.com/lingxling/awesome-skills-cn --skill appdeploy
Deploy web apps to AppDeploy via HTTP API.
.appdeploy file in the project rootapi_key, skip to Usage curl -X POST https://api-v2.appdeploy.ai/mcp/api-key \
-H "Content-Type: application/json" \
-d '{"client_name": "claude-code"}'
Response:
{
"api_key": "ak_...",
"user_id": "agent-claude-code-a1b2c3d4",
"created_at": 1234567890,
"message": "Save this key securely - it cannot be retrieved later"
}
.appdeploy: {
"api_key": "ak_...",
"endpoint": "https://api-v2.appdeploy.ai/mcp"
}
Add .appdeploy to .gitignore if not already present.
Make JSON-RPC calls to the MCP endpoint:
curl -X POST {endpoint} \
-H "Content-Type: application/json" \
-H "Accept: application/json, text/event-stream" \
-H "Authorization: Bearer {api_key}" \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "{tool_name}",
"arguments": { ... }
}
}'
Call get_deploy_instructions to understand constraints and requirements.
Call get_app_template with your chosen app_type and frontend_template.
Call deploy_app with your app files. For new apps, set app_id to null.
Call get_app_status to check if the build succeeded.
Use get_apps to list your deployed apps.
Use this when you are about to call deploy_app in order to get the deployment constraints and hard rules. You must call this tool before starting to generate any code. This tool returns instructions only and does not deploy anything.
Parameters:
Use this when the user asks to deploy or publish a website or web app and wants a public URL.
Before generating files or calling this tool, you must call get_deploy_instructions and follow its constraints.
Parameters:
app_id: any (required) - existing app id to update, or null for new appapp_type: string (required) - app architecture: frontend-only or frontend+backendapp_name: string (required) - short display namedescription: string (optional) - short description of what the app doesfrontend_template: any (optional) - REQUIRED when app_id is null. One of: 'html-static' (simple sites), 'react-vite' (SPAs, games), 'nextjs-static' (multi-page). Template files auto-included.files: array (optional) - Files to write. NEW APPS: only custom files + diffs to template files. UPDATES: only changed files using diffs[]. At least one of files[] or deletePaths[] required.deletePaths: array (optional) - Paths to delete. ONLY for updates (app_id required). Cannot delete package.json or framework entry points.model: string (required) - The coding agent model used for this deployment, to the best of your knowledge. Examples: 'codex-5.3', 'chatgpt', 'opus 4.6', 'claude-sonnet-4-5', 'gemini-2.5-pro'intent: string (required) - The intent of this deployment. User-initiated examples: 'initial app deploy', 'bugfix - ui is too noisy'. Agent-initiated examples: 'agent fixing deployment error', 'agent retry after lint failure'Call get_deploy_instructions first. Then call this once you've decided app_type and frontend_template. Returns base app template and SDK types. Template files auto-included in deploy_app.
Parameters:
app_type: string (required)frontend_template: string (required) - Frontend framework: 'html-static' - Simple sites, minimal framework; 'react-vite' - React SPAs, dashboards, games; 'nextjs-static' - Multi-page apps, SSGUse this when deploy_app tool call returns or when the user asks to check the deployment status of an app, or reports that the app has errors or is not working as expected. Returns deployment status (in-progress: 'deploying'/'deleting', terminal: 'ready'/'failed'/'deleted'), QA snapshot (frontend/network errors), and live frontend/backend error logs.
Parameters:
app_id: string (required) - Target app idsince: integer (optional) - Optional timestamp in epoch milliseconds to filter errors. When provided, returns only errors since that timestamp.Use this when you want to permanently delete an app. Use only on explicit user request. This is irreversible; after deletion, status checks will return not found.
Parameters:
app_id: string (required) - Target app idList deployable versions for an existing app. Requires app_id. Returns newest-first {name, version, timestamp} items. Display 'name' to users. DO NOT display the 'version' value to users. Timestamp values MUST be converted to user's local time
Parameters:
app_id: string (required) - Target app idStart deploying an existing app at a specific version. Use the 'version' value (not 'name') from get_app_versions. Returns true if accepted and deployment started; use get_app_status to observe completion.
Parameters:
app_id: string (required) - Target app idversion: string (required) - Version id to applyUse this when you need to discover files in an app's source snapshot. Returns file paths matching a glob pattern (no content). Useful for exploring project structure before reading or searching files.
Parameters:
app_id: string (required) - Target app idversion: string (optional) - Version to inspect (defaults to applied version)path: string (optional) - Directory path to search withinglob: string (optional) - Glob pattern to match files (default: **/*)include_dirs: boolean (optional) - Include directory paths in resultscontinuation_token: string (optional) - Token from previous response for paginationUse this when you need to search for patterns in an app's source code. Returns matching lines with optional context. Supports regex patterns, glob filters, and multiple output modes.
Parameters:
app_id: string (required) - Target app idversion: string (optional) - Version to search (defaults to applied version)pattern: string (required) - Regex pattern to search for (max 500 chars)path: string (optional) - Directory path to search withinglob: string (optional) - Glob pattern to filter files (e.g., '*.ts')case_insensitive: boolean (optional) - Enable case-insensitive matchingoutput_mode: string (optional) - content=matching lines, files_with_matches=file paths only, count=match count per filebefore_context: integer (optional) - Lines to show before each match (0-20)after_context: integer (optional) - Lines to show after each match (0-20)context: integer (optional) - Lines before and after (overrides before/after_context)line_numbers: boolean (optional) - Include line numbers in outputmax_file_size: integer (optional) - Max file size to scan in bytes (default 10MB)continuation_token: string (optional) - Token from previous response for paginationUse this when you need to read a specific file from an app's source snapshot. Returns file content with line-based pagination (offset/limit). Handles both text and binary files.
Parameters:
app_id: string (required) - Target app idversion: string (optional) - Version to read from (defaults to applied version)file_path: string (required) - Path to the file to readoffset: integer (optional) - Line offset to start reading from (0-indexed)limit: integer (optional) - Number of lines to return (max 2000)Use this when you need to list apps owned by the current user. Returns app details with display fields for user presentation and data fields for tool chaining.
Parameters:
continuation_token: string (optional) - Token for pagination*Generated by scripts/generate-appdeploy-skill.ts*
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 lingxling/appdeploy 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.