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在 OpenClaw 中安装
/install deploy-kit
功能描述
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...
使用说明 (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
安全使用建议
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.
功能分析
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.
能力评估
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.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install deploy-kit - 安装完成后，直接呼叫该 Skill 的名称或使用
/deploy-kit触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
One-command deploys to Vercel, Railway, Supabase. Auto-detects frameworks.
元数据
常见问题
Deploy Kit 是什么？
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... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 1931 次。
如何安装 Deploy Kit？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install deploy-kit」即可一键安装，无需额外配置。
Deploy Kit 是免费的吗？
是的，Deploy Kit 完全免费（开源免费），可自由下载、安装和使用。
Deploy Kit 支持哪些平台？
Deploy Kit 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Deploy Kit？
由 HugoSbl（@hugosbl）开发并维护，当前版本 v1.0.0。
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