Deploy logo

Deploy

Organization
ww-w-ai
deploy

Deployment guidance and checklist for dev, staging, and production environments. Creates a checkpoint before deploy and provides a structured pre-deploy checklist. Use proactively when user mentions deploying, releasing, or shipping to any environment. Triggers: deploy, release, ship, publish, go live, 배포, 릴리스, 출시, デプロイ, リリース, 公開, 部署, 发布, 上线, desplegar, lanzar, publicar, déployer, publier, mettre en production, bereitstellen, veröffentlichen, freigeben, distribuire, rilasciare, pubblicare Do NOT use for: development builds, local testing

Overview

Publisherww-w-ai
Repositorybkit-gemini
Skill namedeploy
Stars
66
Forks
16
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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 ww-w-ai on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/ww-w-ai/bkit-gemini.git /tmp/bkit-gemini
mkdir -p .claude/skills
cp -r /tmp/bkit-gemini/skills/deploy .claude/skills/deploy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deploy 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 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 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.

Deploy Skill

Guided deployment with pre-deploy checklist and checkpoint creation

Commands

CommandDescriptionExample
/deploy devDeploy to development/deploy dev
/deploy stagingDeploy to staging/deploy staging
/deploy prodDeploy to production (full checklist)/deploy prod

Note: Auto-deploy is not possible from the Gemini CLI Extension. This skill provides a guided checklist and ensures safety steps are taken. The actual deploy command must be run by the user or CI/CD pipeline.

How to Execute

1. Pre-Deploy Checkpoint

Before any deployment:

  1. Call bkit_checkpoint to create a restore point
  2. Record the checkpoint ID for potential rollback
  3. Note: If checkpoint fails, warn user but do not block

2. Environment-Specific Checklists

Development (/deploy dev)
  • All modified files saved
  • No syntax errors (node -c on changed files)
  • Local tests pass (if configured)
  • Checkpoint created
Staging (/deploy staging)

All of dev checklist, plus:

  • PDCA Check phase completed (match rate >= 80%)
  • No TODO or FIXME in changed files
  • Environment variables documented
  • API changes backward-compatible
Production (/deploy prod)

All of staging checklist, plus:

  • PDCA Check phase completed (match rate >= 90%)
  • Code review completed (/code-review)
  • All tests pass
  • No debug/console.log statements
  • Rollback plan documented
  • PDCA report generated (/pdca report)
  • Changelog updated

3. Deploy Command Guidance

After checklist is verified, provide the appropriate deploy command:

bash
# Development
npm run deploy:dev    # or project-specific command

# Staging
npm run deploy:staging

# Production
npm run deploy:prod

4. Post-Deploy Verification

After deployment:

  1. Verify the deployment was successful
  2. Check health endpoints (if applicable)
  3. Update PDCA status to "deployed"
  4. Log the deployment in audit trail

Output Format

markdown
## Deploy Checklist: {environment}

### Pre-Deploy
- [x] Checkpoint created (ID: chk-20260409-143000)
- [x] Syntax check passed (12 files)
- [x] PDCA match rate: 94%
- [ ] Code review: PENDING

### Deploy Command
Run: `npm run deploy:staging`

### Post-Deploy
- [ ] Verify health endpoint
- [ ] Update PDCA status

Safety Rules

  1. Production requires explicit confirmation - Never auto-deploy to prod
  2. Checkpoint is mandatory - Always create before deploy
  3. Match rate threshold - Warn if below required threshold for environment
  4. Rollback ready - Always provide rollback instructions with checkpoint ID

Frequently asked questions

What does the Deploy AI skill do?

Deployment guidance and checklist for dev, staging, and production environments. Creates a checkpoint before deploy and provides a structured pre-deploy checklist. Use proactively when user mentions deploying, releasing, or shipping to any environment. Triggers: deploy, release, ship, publish, go live, 배포, 릴리스, 출시, デプロイ, リリース, 公開, 部署, 发布, 上线, desplegar, lanzar, publicar, déployer, publier, mettre en production, bereitstellen, veröffentlichen, freigeben, distribuire, rilasciare, pubblicare Do NOT use for: development builds, local testing

Why use Deploy on TypingMind?

Because you install it once and use it with any model. Deploy 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 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ww-w-ai/bkit-gemini/tree/main/skills/deploy. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deploy?

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?

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

Is the Deploy AI skill free?

Yes. It is published on GitHub by ww-w-ai under the Apache-2.0 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.

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