Deploy logo

Deploy

Community
danielvm-git
deploy

Build → verify artifact → deploy → wait → smoke deployment pipeline. Platform-agnostic (MCP or CLI), with configurable timeout, retry with exponential backoff, and integrated health-check. The deploy half of CI/CD: run after build to push to production.

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill namedeploy
Stars
206
Forks
18
Bundled files
1
LicenseMIT
Links
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by danielvm-git 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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/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

HARD GATE — Do not deploy without running tests first. Run test or your CI suite before this skill.

HARD GATE — Use this skill from a CI/CD pipeline or post-merge on main/master. Never deploy from a feature branch.

HARD GATE — The deploy skill orchestrates deployment; the smoke-test skill validates post-deploy health. Chain them: deploy → smoke-test.

Orchestrate a full build-to-deployment pipeline: build the artifact, verify it exists and is non-empty, invoke a platform deploy tool (MCP or CLI), poll until the deploy completes or times out, then run a baseline smoke test against the live URL.

Pipeline Stages

build → verify artifact → deploy → wait/retry → smoke
StageDescriptionFailure mode
BuildExecute the project's build commandNon-zero exit: report build error
VerifyCheck artifact exists and is non-emptyMissing/empty: report artifact path
DeployInvoke platform deploy tool (MCP, Vercel CLI, rsync, etc.)Non-zero exit: report deploy error
WaitPoll deploy status every 30s up to DEPLOY_TIMEOUT (default 5 min)Timeout: report exceeded
Smokecurl -sSf $DEPLOY_URL as baseline health checkNon-200: report failure

Process

1. Detect build command

Read project manifest files in order to determine the build command:

ManifestBuild command
package.jsonnpm run build (or scripts.build value)
Cargo.tomlcargo build --release
pyproject.toml / setup.pyDepends on build backend (poetry build, pip install -e ., etc.)
Makefilemake build or first target named build
AGENTS.md / CLAUDE.mdLook for build: in project commands section

If no manifest is found, prompt the user with: "No detected build command. Pass --build 'npm run build' or specify the command."

2. Build the artifact

bash
npm run build

Or the detected command from step 1. If the build fails, exit non-zero and report the build output.

3. Verify the artifact

bash
ARTIFACT_DIR="${ARTIFACT_DIR:-dist}"
if [ ! -d "$ARTIFACT_DIR" ] || [ -z "$(ls -A "$ARTIFACT_DIR" 2>/dev/null)" ]; then
  echo "FAIL: build artifact not found at $ARTIFACT_DIR"
  exit 1
fi

Configurable via $ARTIFACT_DIR environment variable (default: dist/).

4. Deploy to platform

Platform-agnostic — supports multiple deployment targets via environment variables:

PlatformEnv varExample
VercelVERCEL_TOKEN, VERCEL_PROJECT_IDvercel deploy --prod --token $VERCEL_TOKEN
NetlifyNETLIFY_AUTH_TOKEN, NETLIFY_SITE_IDnetlify deploy --prod --auth $NETLIFY_AUTH_TOKEN --dir $ARTIFACT_DIR
Platform MCPMCP tool callmcp deploy via your platform MCP server
rsync/SSHDEPLOY_SSH_USER, DEPLOY_SSH_HOST, DEPLOY_SSH_PATHrsync -avz $ARTIFACT_DIR/ $DEPLOY_SSH_USER@$DEPLOY_SSH_HOST:$DEPLOY_SSH_PATH
CustomDEPLOY_COMMANDRun any deploy command string

The deploy tool is selected by which environment variables are set. If none are configured:

bash
echo "No deploy target configured. Set one of: VERCEL_TOKEN, NETLIFY_AUTH_TOKEN, DEPLOY_SSH_USER+DEPLOY_SSH_HOST, DEPLOY_COMMAND, or MCP deploy tool."
exit 1

5. Wait and poll status

After invoking the deploy command, poll for completion:

See REFERENCE.md

Use exponential backoff for retries on transient failures:

See REFERENCE.md

6. Baseline smoke test

See REFERENCE.md

For comprehensive health-checking, chain to the smoke-test skill:

bash
# After deploy success
bash scripts/run-smoke.sh "$DEPLOY_URL"

7. Three-independent-facts verification (e45s15)

Before declaring deploy success, verify three independent facts — build artifact, platform accept, live/registry reachability. See REFERENCE.md.

Verify

→ verify: command -v curl >/dev/null 2>&1 && test -f skills/smoke-test/SKILL.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deploy AI skill do?

Build → verify artifact → deploy → wait → smoke deployment pipeline. Platform-agnostic (MCP or CLI), with configurable timeout, retry with exponential backoff, and integrated health-check. The deploy half of CI/CD: run after build to push to production.

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/danielvm-git/bigpowers/tree/main/skills/deploy. TypingMind reads its SKILL.md and bundles its files 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 danielvm-git 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

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇