Run any Skill in Manus with one click

deploy-everywhere

Deploy a Python AI agent or app to multiple platforms in one command: Railway, Fly.io, Docker Hub, AWS Lambda, and GitHub Actions CI. Generates Dockerfile, platform-specific configs, environment variable templates, and a deployment checklist. Adapted from OpenClaw's multi-platform deployment pipeline. Use when you want to ship Nanobot-powered tools to production without manual config per platform.

Run Skill in Manus

Overview

Install command

npx skills add https://github.com/mk-knight23/AI-Agent-Nanobot --skill deploy-everywhere

Copy and paste this command into Claude Code to install the skill

Source

mk-knight23/AI-Agent-Nanobot

Stars1

Forks0

UpdatedMarch 10, 2026 at 00:13

SKILL.md

readonly

name

deploy-everywhere

description

deploy-everywhere

Ship your Nanobot app to 10 platforms from one command.

Usage

@Nanobot deploy-everywhere --platforms railway,fly,lambda
@Nanobot deploy-everywhere --platforms docker --push ghcr.io/mk-knight23/nanobot
@Nanobot deploy-everywhere --dry-run      # Preview all configs, don't deploy
@Nanobot deploy-everywhere --platform github-actions --trigger push:main

Supported Platforms

Platform	Config Generated	Deploy Method
Railway	`railway.toml`	`railway up`
Fly.io	`fly.toml`	`fly deploy`
AWS Lambda	`serverless.yml`	`sls deploy`
Docker Hub	`Dockerfile`	`docker push`
GitHub Actions	`.github/workflows/deploy.yml`	git push
Render	`render.yaml`	git push
Heroku	`Procfile`	`git push heroku`

Files Created

Dockerfile                          # Multi-stage Python build, <150MB image
.dockerignore                       # Excludes .env, __pycache__, tests
railway.toml                        # Railway service + env config
fly.toml                            # Fly.io app config
.github/workflows/deploy.yml        # CI/CD pipeline
.env.template                       # All required env vars (no secrets)
DEPLOY_CHECKLIST.md                 # Pre-flight checklist before going live

What It Handles

1. Dependency Optimization

Uses uv for fast Docker layer caching
Separates production from dev deps
Pins exact versions in requirements.lock

2. Environment Variable Management

Scans code for os.environ and os.getenv calls
Generates .env.template with all required vars
Adds startup validation: fails fast if required vars are missing

3. Health Check Endpoint

Injects /health endpoint into your agent gateway
Returns: uptime, version, active provider, memory usage

4. Secrets Detection

Blocks deploy if .env file is in git staging
Warns on hardcoded strings matching secret patterns
Integrates with git-secrets if installed

Multi-Platform CI/CD

# Generated .github/workflows/deploy.yml
on:
  push:
    branches: [main]
jobs:
  deploy:
    steps:
      - uses: actions/checkout@v4
      - run: pip install uv && uv sync
      - run: python -m pytest --tb=short
      - run: railway up          # or fly deploy, etc.

Philosophy

Nanobot should ship everywhere. One Docker image, multiple runtimes. The skill handles the plumbing so you focus on the agent logic.

More from this repository

same repository

batch-processor

mk-knight23/AI-Agent-Nanobot

Parallel processing of large datasets using Python asyncio + Claude AI transforms. Accepts CSV, JSON, Parquet, or JSONL as input. Splits into configurable chunk sizes, processes each chunk concurrently using Haiku (cost-efficient), aggregates results, and writes output. Includes progress tracking, error retry with exponential backoff, and cost estimation before run. Cross-ported from ZeroClaw's Rayon-based batch processor.

2026-03-101

add-gmail

mk-knight23/AI-Agent-Nanobot

Adds Gmail monitoring to Nanobot using the Google Gmail API with OAuth2. Creates channels/gmail.py that polls INBOX for unread messages, passes them to the agent, and sends replies after approval. Use when you want Nanobot to process and respond to emails. Requires a Google Cloud project with Gmail API enabled.

2026-03-061

add-obsidian

mk-knight23/AI-Agent-Nanobot

Mounts an Obsidian vault as a read-only knowledge base for Nanobot. Registers a vault_search tool (async, full-text, scores by match density) so the agent can search your notes during any task. Use when you want Nanobot to reference your personal Obsidian vault for context. Requires the vault accessible as a local directory.

2026-03-061

add-slack

mk-knight23/AI-Agent-Nanobot

Adds Slack integration to Nanobot via the Slack Bolt SDK for Python (async mode). Creates channels/slack.py with Socket Mode support, listens for app_mention events, routes through the skill dispatcher, and replies in thread. Use when you want Nanobot in a Slack workspace. Requires a Slack app with app_mentions:read and chat:write scopes and Socket Mode enabled.

2026-03-061

add-supabase

mk-knight23/AI-Agent-Nanobot

Adds Supabase as Nanobot's persistent backend using the supabase-py client. Creates tables for agent memory, skill invocations, and conversation history. Registers memory_read and memory_write as tools so skills can persist state across sessions. Requires a Supabase project URL and anon key.

2026-03-061

add-telegram

mk-knight23/AI-Agent-Nanobot

Adds a Telegram bot channel to Nanobot. Installs python-telegram-bot (v20+, async), creates channels/telegram.py with an async handler, registers it in the channel router, and wires trigger-word filtering. Use when you want Nanobot to receive and respond to Telegram messages. Requires a bot token from @BotFather.

2026-03-061

Source

mk-knight23

mk-knight23/AI-Agent-Nanobot

View GitHub Repository View Creator Repositories

Install command

Download

Run Skill in Manus

Useful forSOC

Software DevelopersComputer and Mathematical Occupations15-1252L4

name

deploy-everywhere

description

deploy-everywhere

Ship your Nanobot app to 10 platforms from one command.

Usage

@Nanobot deploy-everywhere --platforms railway,fly,lambda
@Nanobot deploy-everywhere --platforms docker --push ghcr.io/mk-knight23/nanobot
@Nanobot deploy-everywhere --dry-run      # Preview all configs, don't deploy
@Nanobot deploy-everywhere --platform github-actions --trigger push:main

Supported Platforms

Platform	Config Generated	Deploy Method
Railway	`railway.toml`	`railway up`
Fly.io	`fly.toml`	`fly deploy`
AWS Lambda	`serverless.yml`	`sls deploy`
Docker Hub	`Dockerfile`	`docker push`
GitHub Actions	`.github/workflows/deploy.yml`	git push
Render	`render.yaml`	git push
Heroku	`Procfile`	`git push heroku`

Files Created

Dockerfile                          # Multi-stage Python build, <150MB image
.dockerignore                       # Excludes .env, __pycache__, tests
railway.toml                        # Railway service + env config
fly.toml                            # Fly.io app config
.github/workflows/deploy.yml        # CI/CD pipeline
.env.template                       # All required env vars (no secrets)
DEPLOY_CHECKLIST.md                 # Pre-flight checklist before going live

What It Handles

1. Dependency Optimization

Uses uv for fast Docker layer caching
Separates production from dev deps
Pins exact versions in requirements.lock

2. Environment Variable Management

Scans code for os.environ and os.getenv calls
Generates .env.template with all required vars
Adds startup validation: fails fast if required vars are missing

3. Health Check Endpoint

Injects /health endpoint into your agent gateway
Returns: uptime, version, active provider, memory usage

4. Secrets Detection

Blocks deploy if .env file is in git staging
Warns on hardcoded strings matching secret patterns
Integrates with git-secrets if installed

Multi-Platform CI/CD

# Generated .github/workflows/deploy.yml
on:
  push:
    branches: [main]
jobs:
  deploy:
    steps:
      - uses: actions/checkout@v4
      - run: pip install uv && uv sync
      - run: python -m pytest --tb=short
      - run: railway up          # or fly deploy, etc.

Philosophy

Nanobot should ship everywhere. One Docker image, multiple runtimes. The skill handles the plumbing so you focus on the agent logic.