Full-Stack AI Agent Template generates full-stack AI applications with a FastAPI backend and Next.js frontend, including agents, retrieval-augmented generation, streaming, authentication, and integrations. It is for building AI products with features such as chat, conversation sharing, administration, and multiple agent or vector-database choices. Catalogue add-ons support the generated applications and their agent workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vstorm-co/full-stack-ai-agent-template --skill agent-toolgit clone --depth 1 https://github.com/vstorm-co/full-stack-ai-agent-templateWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vstorm-co/full-stack-ai-agent-template/agent-tool)<a href="https://agentmods.dev/skills/vstorm-co/full-stack-ai-agent-template/agent-tool"><img src="https://agentmods.dev/badge/skills/vstorm-co/full-stack-ai-agent-template/agent-tool/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vstorm-co/full-stack-ai-agent-template/agent-tool"><img src="https://agentmods.dev/badge/skills/vstorm-co/full-stack-ai-agent-template/agent-tool.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.00776 |
| Opus 5.5 | $0.00026 | $0.00310 |
| Sonnet 5.5 | $0.00013 | $0.00155 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
agent-tool scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add an Agent Tool ({{ cookiecutter.ai_framework }})
Agent tools live in backend/app/agents/tools/ and are surfaced to the model so it can call them mid-conversation. The assistant is defined in backend/app/agents/.
Steps
-
Write the tool function in
backend/app/agents/tools/<tool_name>.py:- Async, fully type-hinted, with a clear docstring — the docstring and signature are what the model sees, so make them precise.
- Pure logic: take typed args, return a JSON-serializable result. Raise on hard errors; return a structured
{"error": ...}for soft failures the model should reason about. - Keep secrets/IO behind
settingsand the service layer; don't inline credentials.
-
Export it from
backend/app/agents/tools/__init__.py(add the import and append to__all__, matching the existing feature-gated blocks). -
Register it on the agent in the assistant for the active framework: {%- if cookiecutter.use_pydantic_ai %}
app/agents/assistant.py— decorate with@agent.tool(needsRunContext[Deps]) or@agent.tool_plain(no context):@agent.tool async def my_tool(ctx: RunContext[Deps], query: str) -> dict: """One-line description the model reads to decide when to call this.""" ...
{%- elif cookiecutter.use_pydantic_deep %}
app/agents/pydantic_deep_assistant.py— add the function to the agent's tool list. {%- elif cookiecutter.use_langchain %}app/agents/langchain_assistant.py— wrap with@tooland add it to thetools=[...]passed to the agent. {%- elif cookiecutter.use_langgraph %}app/agents/langgraph_assistant.py— wrap with@tooland include it in the tools list bound to the graph. {%- elif cookiecutter.use_deepagents %}app/agents/deepagents_assistant.py— add the function to the agent's tool list. {%- endif %}
- Prompt guidance (optional but recommended): if the tool should only be used in specific situations, add a sentence to the system prompt in
app/agents/prompts.pyso the model knows when to reach for it.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 50 lines · 66 tokens per session scan A 1f0db50318bf
agent-tool is a skill published in the GitHub repository vstorm-co/full-stack-ai-agent-template (1,953 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 776 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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