Fastapi App
ccplugins/awesome-claude-code-plugins
Bootstrap a new FastAPI backend with async SQLAlchemy 2.0, asyncpg, Alembic, Pydantic v2, and no deprecated APIs.
Build and deploy a full-stack app (React frontend + Python/FastAPI backend) or a Vellum app to Vercel as a serverless demo with seeded data
$ npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install vellum-ai/vellum-assistant deploy-fullstack-vercel --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/vellum-ai/vellum-assistant.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deploy-fullstack-vercel .claude/skills/deploy-fullstack-vercel && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "deploy-fullstack-vercel" agent skill from https://github.com/vellum-ai/vellum-assistant/tree/main/skills/deploy-fullstack-vercel into .claude/skills/deploy-fullstack-vercel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-fullstack-vercel", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/vellum-ai/vellum-assistant/tree/main/skills/deploy-fullstack-vercelType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install vellum-ai/vellum-assistant deploy-fullstack-vercel --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vellum-ai/vellum-assistant.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/deploy-fullstack-vercel .agents/skills/deploy-fullstack-vercel && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deploy-fullstack-vercel" agent skill from https://github.com/vellum-ai/vellum-assistant/tree/main/skills/deploy-fullstack-vercel into .agents/skills/deploy-fullstack-vercel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-fullstack-vercel", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install vellum-ai/vellum-assistant deploy-fullstack-vercel --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vellum-ai/vellum-assistant.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/deploy-fullstack-vercel .cursor/skills/deploy-fullstack-vercel && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deploy-fullstack-vercel" agent skill from https://github.com/vellum-ai/vellum-assistant/tree/main/skills/deploy-fullstack-vercel into .cursor/skills/deploy-fullstack-vercel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-fullstack-vercel", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/vellum-ai/vellum-assistant.git --path skills/deploy-fullstack-vercel--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install vellum-ai/vellum-assistant deploy-fullstack-vercel --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vellum-ai/vellum-assistant.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/deploy-fullstack-vercel .gemini/skills/deploy-fullstack-vercel && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deploy-fullstack-vercel" agent skill from https://github.com/vellum-ai/vellum-assistant/tree/main/skills/deploy-fullstack-vercel into .gemini/skills/deploy-fullstack-vercel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-fullstack-vercel", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install vellum-ai/vellum-assistant deploy-fullstack-vercelInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/vellum-ai/vellum-assistant.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/deploy-fullstack-vercel .github/skills/deploy-fullstack-vercel && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deploy-fullstack-vercel" agent skill from https://github.com/vellum-ai/vellum-assistant/tree/main/skills/deploy-fullstack-vercel into .github/skills/deploy-fullstack-vercel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-fullstack-vercel", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install vellum-ai/vellum-assistant deploy-fullstack-vercel --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vellum-ai/vellum-assistant.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/deploy-fullstack-vercel .opencode/skills/deploy-fullstack-vercel && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deploy-fullstack-vercel" agent skill from https://github.com/vellum-ai/vellum-assistant/tree/main/skills/deploy-fullstack-vercel into .opencode/skills/deploy-fullstack-vercel/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deploy-fullstack-vercel", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
deploy-fullstack-vercelBuild and deploy a full-stack app (React frontend + Python/FastAPI backend) or a Vellum app to Vercel as a serverless demo with seeded data
Deploy Fullstack Vercel is an agent skill from vellum-ai/vellum-assistant. Build and deploy a full-stack app (React frontend + Python/FastAPI backend) or a Vellum app to Vercel as a serverless demo with seeded data
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Designed for Vellum personal assistants
It sits in Backend & APIs, covering Serverless, Deployment and Backend development. It works with Vercel, FastAPI, React and Python. The repository describes itself as: An AI Assistant that’s easy to setup, does your work 24/7, knows your preferences and gets better over time. The licence is MIT.
12 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 56dfa96. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
vercelbunbunxcurlFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use vercel, bunx and curl, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Designed for Vellum personal assistants
From compatibility in the SKILL.md frontmatter.
Deploy Fullstack Vercel loads about 2.8k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 757 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from vellum-ai/vellum-assistant at commit 56dfa96, republished under its MIT licence (© vellum-ai). 757 words, ~2,757 tokens.
.claude/skills/deploy-fullstack-vercel/SKILL.md (or your agent's skills folder).Deploy a full-stack app with a React/Vite frontend and Python/FastAPI backend to Vercel as a serverless demo, OR deploy a Vellum-built app from the library. No auth required - meant for demos, portfolio pieces, and quick showcases.
For publishing Vellum apps from the library, use the built-in publish_page tool. This is the preferred path — it uses the stored Vercel API token (vercel/api_token) via the brokered publish flow without exposing the token to shell commands.
The stored Vercel API token is reserved for brokered publish_page and unpublish_page actions only. Do not pass it to bash, curl, Vercel CLI commands, or proxy credential injection. Do not use network_mode: "proxied" with credential_ids for Vercel deployments.
For custom projects that need Vercel deployment (not Vellum app publishing):
bun install -g vercel (not npm — npm is not available in the sandbox).vercel login to authenticate interactively (opens browser for the user).When the user asks to deploy a Vellum app from their library (from /workspace/data/apps/<app-name>/):
Check the compiled app for Vellum bridge API usage:
grep -l "window\.vellum\.\|vellum\.fetch\|vellum\.data\|vellum\.sendAction" /workspace/data/apps/<app-name>/dist/*.js /workspace/data/apps/<app-name>/dist/*.html 2>/dev/nullIf found, the app depends on the Vellum bridge and needs a shim to work standalone.
The app uses window.vellum.* APIs that are normally injected by the Vellum viewer. For standalone deployment, create a vellum-shim.js file in the app's dist/ directory that provides browser-native replacements.
Before writing the shim, read the app's compiled JavaScript (dist/main.js or equivalent) to understand exactly which window.vellum.* APIs the app calls and what data shapes it expects. The shim must match the app's actual usage — don't guess at signatures.
Common APIs to shim (implement only what the app actually uses):
| Bridge API | Standalone replacement | Notes |
|---|---|---|
vellum.data.query() | localStorage-backed store | Read the app code to determine the record shape — some apps expect {id, data: {...}} wrappers, others use flat records |
vellum.data.create(...) | localStorage insert with crypto.randomUUID() | Match the argument signature the app passes (some pass a payload, others pass {id, ...fields}) |
vellum.data.update(...) | localStorage update | Match the argument signature (typically (id, payload)) |
vellum.data.delete(...) | localStorage delete | Typically (id) |
vellum.fetch(path, opts) | console.warn + return empty success Response | Custom routes aren't available standalone |
vellum.sendAction(id, data) | No-op with console.warn | Surface actions aren't available standalone |
vellum.openLink(url) | window.open(url, '_blank') | |
vellum.widgets.toast(msg) | Create a temporary styled <div> that auto-dismisses | |
vellum.route | null | Deep-link routes aren't available standalone |
Structure: Wrap everything in an IIFE that guards against the real bridge: (function() { if (window.vellum) return; ... })();
Add a <script src="vellum-shim.js"></script> tag in dist/index.html BEFORE any <script type="module"> tags:
sed -i 's|<script type="module"|<script src="vellum-shim.js"></script>\n<script type="module"|' dist/index.htmlcd /workspace/data/apps/<app-name>/distCreate a vercel.json in the dist directory:
{
"rewrites": [
{
"source": "/((?!main\\.js|main\\.css|vellum-shim\\.js|assets/).*)",
"destination": "/index.html"
}
]
}Then deploy using the publish_page tool (preferred). For Vellum apps, use the built-in app publish flow rather than raw Vercel API calls from shell.
cd <project>/frontend
bun install
bunx vite buildThis produces static files in frontend/dist/.
<project>/vercel-deploy/
├── api/
│ ├── index.py ← FastAPI app wrapper (entry point)
│ ├── database.py ← DB config (use /tmp for SQLite)
│ ├── models.py
│ ├── schemas.py
│ ├── seed_data.py ← Must seed ALL required data (users, etc.)
│ ├── routers/
│ │ ├── __init__.py
│ │ └── *.py
│ └── requirements.txt ← Python deps (fastapi, sqlalchemy, pydantic)
├── index.html ← From frontend/dist/
├── assets/ ← From frontend/dist/assets/
└── vercel.jsonKey steps:
mkdir -p <project>/vercel-deploy/api
# Copy frontend build output to deploy root
cp -r <project>/frontend/dist/* <project>/vercel-deploy/
# Copy backend files into api/
cp <project>/backend/models.py <project>/vercel-deploy/api/
cp <project>/backend/database.py <project>/vercel-deploy/api/
cp <project>/backend/schemas.py <project>/vercel-deploy/api/
cp <project>/backend/seed_data.py <project>/vercel-deploy/api/
cp -r <project>/backend/routers <project>/vercel-deploy/api/
cp <project>/backend/requirements.txt <project>/vercel-deploy/api/import sys, os
sys.path.insert(0, os.path.dirname(__file__))
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from database import engine, Base, SessionLocal
from seed_data import seed_exercises, seed_default_user # all seed functions
from routers import users, exercises, workouts, schedule, progress
# Create tables and seed on EVERY cold start
Base.metadata.create_all(bind=engine)
db = SessionLocal()
try:
seed_exercises(db)
seed_default_user(db) # IMPORTANT: seed all required data
finally:
db.close()
app = FastAPI(title="MyApp")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(users.router)
# ... other routers
@app.get("/api/health")
def health_check():
return {"status": "ok"}Critical: Vercel serverless functions can only write to /tmp. Update the SQLite path:
SQLALCHEMY_DATABASE_URL = "sqlite:////tmp/app.db"This is the #1 gotcha. Since /tmp is ephemeral, every cold start gets a fresh database. If your frontend assumes certain data exists (like user ID 1), you MUST seed it:
def seed_default_user(db: Session):
count = db.query(UserProfile).count()
if count > 0:
return
user = UserProfile(name="Demo User", ...)
db.add(user)
db.commit(){
"rewrites": [
{ "source": "/api/(.*)", "destination": "/api/index.py" },
{ "source": "/((?!assets/).*)", "destination": "/index.html" }
]
}This routes:
/api/* → Python serverless functioncd <project>/vercel-deploy
vercel --yes --prodcurl -s <deployed-url>/api/health
# Should return: {"status":"ok"}| Issue | Solution |
|---|---|
| SQLite resets on cold start | Seed ALL required data in index.py startup |
| No persistent storage | Acceptable for demos. For production, use Vercel Postgres or Supabase |
| No auth | Fine for demos/portfolios. Add auth layer for real apps |
requirements.txt location | Must be inside api/ folder (next to index.py) |
| Module imports in routers | Use sys.path.insert(0, os.path.dirname(__file__)) in index.py |
| CORS | Set allow_origins=["*"] for demo deployments |
--name flag deprecated | Don't use --name with Vercel CLI, just deploy from the directory |
| Vellum bridge APIs | Use the vellum-shim.js to provide localStorage-backed data + no-op stubs |
| npm not available | Use bun install -g vercel to install Vercel CLI in sandbox |
bun install -g vercel # Install
vercel login # Authenticate (opens browser for user-mediated auth)
vercel --yes --prod # Deploy to production (skip prompts)
vercel logs --project <name> # Check function logs© vellum-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/deploy-fullstack-vercel of vellum-ai/vellum-assistant.
Open the folder on GitHubat commit 56dfa96
Deploy Fullstack Vercel next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deploy Fullstack Vercel this skillvellum-ai/vellum-assistant | 1.4k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Fastapi Appccplugins/awesome-claude-code-plugins | 967 | — | ~1.1k | Automated safety check: Notes | Apache-2.0 | |
| Vercel Experttheneoai/awesome-skills | 183 | — | ~3.2k | Automated safety check: Pass | MIT | |
| Azure Realtime Podcast Generationmicrosoft/skills | 3.1k | 1 repos | ~947 | Automated safety check: Pass | MIT | |
| Airflow Pluginsastronomer/agents | 450 | — | ~6k | Automated safety check: Notes | Apache-2.0 | |
| Fastcrudbenavlabs/fastcrud | 1.6k | — | ~5k | Automated safety check: Pass | MIT |
ccplugins/awesome-claude-code-plugins
Bootstrap a new FastAPI backend with async SQLAlchemy 2.0, asyncpg, Alembic, Pydantic v2, and no deprecated APIs.
theneoai/awesome-skills
Vercel expert: Frontend deployment, Serverless Functions, environment configuration, preview deployments, edge functions, and performance optimization.
microsoft/skills
Builds podcast-style audio narration from text with Azure OpenAI's GPT Realtime Mini over WebSocket, from a Python FastAPI backend to a React player.
astronomer/agents
Builds Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI.
benavlabs/fastcrud
A skill your agent uses when building or modifying CRUD endpoints with FastCRUD (the fastcrud PyPI package) in a FastAPI project — covers FastCRUD, crudrouter, EndpointCreator, FilterConfig…
Arize-ai/phoenix
Backend development guide for the Phoenix AI observability platform (Strawberry GraphQL, SQLAlchemy async, FastAPI).
vellum-ai/vellum-assistant
Create and configure a GitHub App so the assistant can push commits, open PRs, and comment under its own bot identity.
vellum-ai/vellum-assistant
Connect a Discord bot to the assistant via the Discord Gateway with guided application creation and intent configuration
vellum-ai/vellum-assistant
Create and configure a Sentry internal integration so the assistant can manage issues, alerts, and releases under its own identity
vellum-ai/vellum-assistant
Ingest a large dataset into memory as a skimmed map. An agent skill from vellum-ai/vellum-assistant.
vellum-ai/vellum-assistant
A skill your agent uses when the user wants to build, scaffold, ship, or edit a Vellum plugin that bundles multiple surfaces (hooks, tools, skills, and more) into one installable package.
vellum-ai/vellum-assistant
Connect a Slack app to the Vellum Assistant via Socket Mode.
Categories
Build and deploy a full-stack app (React frontend + Python/FastAPI backend) or a Vellum app to Vercel as a serverless demo with seeded data. Deploy Fullstack Vercel is an agent skill from vellum-ai/vellum-assistant.
Deploy Fullstack Vercel fits situations like: tasks that involve Serverless; tasks that involve Deployment; tasks that involve Backend development.
Run `npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a claude-code`. Or copy the skill folder (skills/deploy-fullstack-vercel in vellum-ai/vellum-assistant) into .claude/skills/deploy-fullstack-vercel in your project. Claude Code loads it when a task matches its description.
Run `npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a codex`. Or copy the skill folder (skills/deploy-fullstack-vercel in vellum-ai/vellum-assistant) into .agents/skills/deploy-fullstack-vercel in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vellum-ai/vellum-assistant --skill deploy-fullstack-vercel -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deploy-fullstack-vercel, .gemini/skills/deploy-fullstack-vercel, .github/skills/deploy-fullstack-vercel and .opencode/skills/deploy-fullstack-vercel in your project.
Going by SKILL.md and its folder, Deploy Fullstack Vercel needs the command-line tools its instructions call (vercel, bun, bunx and curl). Our summary lists: Python 3. Compatibility (from SKILL.md): Designed for Vellum personal assistants.
SKILL.md contains no URLs. Its commands use curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Deploy Fullstack Vercel is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Deploy Fullstack Vercel: Fastapi App (ccplugins/awesome-claude-code-plugins, 967 stars), Vercel Expert (theneoai/awesome-skills, 183 stars), Azure Realtime Podcast Generation (microsoft/skills, 3.1k stars) and Airflow Plugins (astronomer/agents, 450 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
vellum-ai (a GitHub organization) maintains it in vellum-ai/vellum-assistant, which has 1,392 GitHub stars. The repository holds 108 skills in this directory. The repository was last updated on October 4, 2026.
Source: vellum-ai/vellum-assistant on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.