Deploy Gen

Q: Why use Deploy Gen on TypingMind?

Because you install it once and use it with any model. Deploy Gen is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

Q: How do I install Deploy Gen in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/deploy-gen. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Q: Which AI models can use Deploy Gen?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

Q: How many AI models can I use with Deploy Gen?

As many as you like. As long as a model supports skills, you can use Deploy Gen with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Q: Is the Deploy Gen AI skill free?

Yes. It is published on GitHub by jmagly under the MIT license. You only pay your own AI provider for the tokens you use.

Q: What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

Q: How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Community

jmagly

deploy-gen

Generate deployment configurations (Docker, Kubernetes) for the current project

Overview

Publisher	jmagly
Repository	`aiwg`
Skill name	`deploy-gen`
Stars	225
Forks	34
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by jmagly on GitHub. Read the source before you install it.

Installation

Install the Deploy Gen AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/deploy-gen
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/deploy-gen

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/aiwg-utils/skills/deploy-gen .claude/skills/deploy-gen

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deploy Gen in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deploy Gen on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deploy Gen is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill access pattern (post-kernel-pivot, 2026.5+)

Skill names referenced in this document are AIWG skills, not slash commands. Most are not kernel-listed and cannot be invoked as /skill-name by the platform. Reach them via:
bash
aiwg discover "<capability>"
aiwg show skill <name> --body
Only kernel-listed skills (aiwg-doctor, aiwg-refresh, aiwg-status, aiwg-help, use, steward) are directly invokable as slash commands. See skill-discovery rule.

Deploy Generator

Generate production-ready deployment configurations based on project analysis.

Research Foundation

REF-001: BP-8 - Containerized Deployment

"Production-grade agentic workflows require containerized deployment with proper isolation, resource management, and orchestration."

Usage

bash
/deploy-gen docker [options]
/deploy-gen k8s [options]
/deploy-gen compose [options]

Arguments

Argument	Required	Description
type	Yes	Deployment type: docker, k8s, compose

Options

Option	Default	Description
--output	./deploy/	Output directory for generated files
--app-name	(from package.json)	Application name
--port	3000	Application port
--multi-stage	true	Use multi-stage Dockerfile
--health-check	true	Include health check endpoints

Process

1. Project Analysis

Detect project characteristics:


Analyzing project...
- Runtime: [node/python/go/java]
- Package manager: [npm/yarn/pip/go mod]
- Entry point: [detected or ask]
- Dependencies: [count]
- Build required: [yes/no]

2. Template Selection

Choose appropriate templates based on analysis:

Runtime	Template
Node.js	`templates/deploy/docker/node.Dockerfile`
Python	`templates/deploy/docker/python.Dockerfile`
Go	`templates/deploy/docker/go.Dockerfile`

3. Configuration Generation

Generate deployment files with project-specific values.

Output: Docker


deploy/
├── Dockerfile           # Multi-stage build
├── .dockerignore        # Exclude dev files
└── docker-build.sh      # Build helper script

Dockerfile Features

Multi-stage build (build → production)
Non-root user
Health check
Proper signal handling
Layer caching optimization

Output: Kubernetes


deploy/k8s/
├── deployment.yaml      # Pod specification
├── service.yaml         # Service exposure
├── configmap.yaml       # Environment configuration
├── hpa.yaml            # Horizontal Pod Autoscaler
└── kustomization.yaml  # Kustomize base

Kubernetes Features

Resource limits and requests
Liveness and readiness probes
ConfigMap for environment
HPA for auto-scaling
Kustomize for environment overlays

Output: Docker Compose


deploy/
├── docker-compose.yml       # Service definition
├── docker-compose.dev.yml   # Development overrides
└── .env.example             # Environment template

Examples

bash
# Generate Dockerfile for Node.js project
/deploy-gen docker

# Generate full Kubernetes manifests
/deploy-gen k8s --app-name my-api --port 8080

# Generate Docker Compose for local development
/deploy-gen compose --output ./

# Generate all deployment types
/deploy-gen docker && /deploy-gen k8s && /deploy-gen compose

Template Variables

Templates use these variables:

Variable	Source
`{{APP_NAME}}`	--app-name or package.json
`{{PORT}}`	--port option
`{{NODE_VERSION}}`	.nvmrc or latest LTS
`{{PYTHON_VERSION}}`	.python-version or 3.11
`{{ENTRY_POINT}}`	Detected from project

CLI Equivalent

bash
aiwg deploy-gen <type> [options]

Related Commands

project-health-check - Analyze project before deployment
security-audit - Security review before production
flow-deploy-to-production - Full deployment workflow

Success Metrics

From Unified Plan:

Metric	Target
Zero to containerized	<2 minutes
Generated configs	Production-ready
Security baseline	Non-root, minimal image

Generate deployment for: $ARGUMENTS

References

@$AIWG_ROOT/agentic/code/addons/aiwg-utils/README.md — aiwg-utils addon overview
@$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/README.md — SDLC framework context for deployment workflows
@$AIWG_ROOT/docs/cli-reference.md — CLI reference including deploy-related commands
@$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/research-before-decision.md — Analyze project before generating configs

Frequently asked questions

What does the Deploy Gen AI skill do?

Generate deployment configurations (Docker, Kubernetes) for the current project

Why use Deploy Gen on TypingMind?

Because you install it once and use it with any model. Deploy Gen is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deploy Gen in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/deploy-gen. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deploy Gen?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deploy Gen?

As many as you like. As long as a model supports skills, you can use Deploy Gen with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deploy Gen AI skill free?

Yes. It is published on GitHub by jmagly under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Claude Opus 4 5 Migration

anthropics

OrganizationPopular

Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string…

Deploy Release Test

Validate a commit-specific Next.js preview package and manually trigger the entire Next.js deployment test suite through the test_e2e_deploy_release.yml GitHub Actions workflow. Use only when asked to…

Insight Error Page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new `errors/<slug>.mdx` page, auditing an existing one, or checking that a page matches the framework fix…

Mcp Apps Builder

**MANDATORY for ALL MCP server work** - mcp-use framework best practices and patterns. **READ THIS FIRST** before any MCP server work, including: - Creating new MCP servers - Modifying existing MCP…

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Deploy Generator

Research Foundation

Usage

Arguments

Options

Process

1. Project Analysis

2. Template Selection

3. Configuration Generation

Output: Docker

Dockerfile Features

Output: Kubernetes

Kubernetes Features

Output: Docker Compose

Examples

Template Variables

CLI Equivalent

Related Commands

Success Metrics

References

Frequently asked questions

Related AI Skills

Set up your own AI workspace now