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Install in OpenClaw
/install deploy-kit
Description
Simplifie le déploiement d'apps web sur Vercel, Railway et Supabase en détectant le projet, vérifiant les CLI, recommandant la plateforme et exécutant le dép...
README (SKILL.md)
Deploy Kit — Skill de Déploiement Web
Simplifie le déploiement d'apps web sur Vercel, Railway et Supabase via leurs CLIs.
Quand utiliser ce skill
L'utilisateur demande de déployer une app, créer une base de données, configurer un hébergement, ou gérer des variables d'environnement sur ces plateformes.
Workflow principal
1. Détecter le projet
python3 skills/deploy-kit/scripts/deploy.py detect \x3Cchemin>
Retourne : framework, langage, fichiers clés trouvés.
2. Vérifier les CLIs disponibles
python3 skills/deploy-kit/scripts/deploy.py check
Si un CLI manque, guide l'installation (voir références).
3. Recommander une plateforme
python3 skills/deploy-kit/scripts/deploy.py recommend \x3Cchemin>
| Type de projet | Plateforme recommandée |
|---|---|
| Frontend statique / SSR (Next, Astro, Vite, Svelte, Nuxt) | Vercel |
| Backend / API (Express, Flask, FastAPI, Django) | Railway |
| App full-stack avec BDD | Railway + Supabase |
| BDD / Auth / Storage / Edge Functions | Supabase |
4. Déployer
python3 skills/deploy-kit/scripts/deploy.py deploy \x3Cchemin> --platform \x3Cvercel|railway|supabase>
⚠️ TOUJOURS demander confirmation avant de déployer. Le script demande aussi une confirmation interactive.
Détection de projet — Règles
| Fichier trouvé | Framework détecté |
|---|---|
next.config.* |
Next.js |
astro.config.* |
Astro |
vite.config.* |
Vite |
svelte.config.* |
SvelteKit |
nuxt.config.* |
Nuxt |
remix.config.* / app/root.tsx |
Remix |
angular.json |
Angular |
requirements.txt / Pipfile |
Python |
manage.py |
Django |
package.json → scripts.start |
Node.js app |
Dockerfile |
Docker (Railway) |
supabase/config.toml |
Supabase project |
Variables d'environnement
- Vercel :
vercel env add NOM_VARou via dashboard - Railway :
railway variables set NOM=VALEUR - Supabase : secrets via
supabase secrets set NOM=VALEUR
Toujours vérifier .env / .env.local pour les vars existantes avant déploiement.
Domaines custom
- Vercel :
vercel domains add mondomaine.com - Railway :
railway domain(génère un sous-domaine), custom via dashboard
Références détaillées
Charger à la demande selon la plateforme :
skills/deploy-kit/references/vercel.md— Vercel CLI completskills/deploy-kit/references/railway.md— Railway CLI completskills/deploy-kit/references/supabase.md— Supabase CLI complet
Commandes rapides
| Action | Commande |
|---|---|
| Deploy preview Vercel | vercel |
| Deploy prod Vercel | vercel --prod |
| Deploy Railway | railway up |
| Push DB Supabase | supabase db push |
| Deploy edge function | supabase functions deploy \x3Cnom> |
| Voir les logs | vercel logs \x3Curl> / railway logs |
| Lister les projets | vercel ls / railway list |
Règles pour l'agent
- Ne jamais déployer sans confirmation explicite de l'utilisateur
- Toujours détecter le projet avant de recommander
- Vérifier que le CLI est installé et authentifié
- Charger la référence détaillée de la plateforme si besoin de commandes avancées
- Proposer un déploiement preview avant production
- Mentionner les coûts potentiels si projet hors free tier
Usage Guidance
This skill appears to do what it says: detect projects and run Vercel/Railway/Supabase CLIs to deploy. Before using it, verify you trust the project being deployed (build steps can run arbitrary code), confirm the skill asks you before executing deploy commands (it does), and avoid pasting secret tokens into chat. If you follow reference install commands, prefer package manager installs (brew/npm) from official sources and be cautious about running curl | sh. Run deployments first in a staging environment and inspect the repository and any referenced scripts before deploying to production.
Capability Analysis
Type: OpenClaw Skill
Name: deploy-kit
Version: 1.0.0
The skill is designed to assist with web application deployment using Vercel, Railway, and Supabase CLIs. The `SKILL.md` instructions for the AI agent explicitly mandate user confirmation before any deployment action, and the `scripts/deploy.py` script enforces this interactive confirmation. The Python script uses `subprocess.run` with a list of arguments and `cwd` to execute platform-specific CLI commands, which is a safer approach than using `shell=True` with unsanitized input. There is no evidence of data exfiltration, persistence, or malicious prompt injection attempts against the agent. The `curl | sh` installation method mentioned in `references/railway.md` is presented as user-facing documentation, not as a command for the agent to execute.
Capability Assessment
Purpose & Capability
Name/behavior align: the SKILL.md and scripts/deploy.py focus on detecting project type, checking CLI availability, recommending a platform, and running platform CLIs to deploy. No unrelated credentials, binaries, or system paths are requested.
Instruction Scope
SKILL.md instructs the agent to detect projects, verify CLIs, and always ask for confirmation before deploying. The script runs subprocesses to invoke platform CLIs (vercel, railway, supabase) which is expected for a deploy helper — but those CLIs (and the project's build steps they trigger) can execute arbitrary code from the repository during build/runtime, so the agent/user should be aware and confirm deployments before running in sensitive environments.
Install Mechanism
There is no install spec in the skill (instruction-only + a helper script) which is low risk. The bundled reference docs include example install commands (npm global installs and a curl | sh line for Railway in the reference) — these are not executed by the skill but are potentially risky if blindly run by a user.
Credentials
The skill does not request environment variables or credentials. References mention CLI authentication (interactive login or tokens) which is appropriate for deployment CLIs; nothing indicates unnecessary access to unrelated secrets or system config.
Persistence & Privilege
always is false and the skill does not request persistent elevated privileges or attempt to modify other skills or system-wide settings. Model invocation is allowed (default) which is normal for a user-invocable skill.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install deploy-kit - After installation, invoke the skill by name or use
/deploy-kit - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
One-command deploys to Vercel, Railway, Supabase. Auto-detects frameworks.
Metadata
Frequently Asked Questions
What is Deploy Kit?
Simplifie le déploiement d'apps web sur Vercel, Railway et Supabase en détectant le projet, vérifiant les CLI, recommandant la plateforme et exécutant le dép... It is an AI Agent Skill for Claude Code / OpenClaw, with 1931 downloads so far.
How do I install Deploy Kit?
Run "/install deploy-kit" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Deploy Kit free?
Yes, Deploy Kit is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Deploy Kit support?
Deploy Kit is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Deploy Kit?
It is built and maintained by HugoSbl (@hugosbl); the current version is v1.0.0.
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