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 channel-botgit 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/channel-bot)<a href="https://agentmods.dev/skills/vstorm-co/full-stack-ai-agent-template/channel-bot"><img src="https://agentmods.dev/badge/skills/vstorm-co/full-stack-ai-agent-template/channel-bot/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/channel-bot"><img src="https://agentmods.dev/badge/skills/vstorm-co/full-stack-ai-agent-template/channel-bot.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 | $0.00053 | $0.00532 |
| Opus 5.5 | $0.00021 | $0.00213 |
| Sonnet 5.5 | $0.00011 | $0.00106 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
channel-bot 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Messaging Channels (Telegram / Slack)
Channels are a thick service at backend/app/services/channels/: per-platform adapters plus a router that funnels inbound messages into the same agent pipeline as the web chat.
Layout
| File | Responsibility |
|---|---|
base.py |
Shared channel adapter interface |
telegram.py |
Telegram adapter (aiogram v3) |
slack.py |
Slack adapter (Events API + Socket Mode) |
router.py |
Maps an inbound platform message → conversation/session → agent run → reply |
chart_render.py |
Renders chart tool output as PNG for channels |
Bots are stored in the DB (channel_bot), with per-user identity (channel_identity) and per-thread session (channel_session) tables. Bot tokens are encrypted at rest with CHANNEL_ENCRYPTION_KEY (Fernet).
Register / manage a bot
uv run {{ cookiecutter.project_slug }} cmd channel ... # see `cmd channel --help`
Webhook vs polling
- Polling (dev): the adapter long-polls the platform — no public URL needed.
- Webhook (prod): the platform POSTs to
POST /api/v1/telegram/{bot_id}/webhook/ the Slack events endpoint. Verify the signature/secret (HMAC for Telegram, signing secret for Slack) before processing.
Add a new channel adapter
- Implement an adapter in
services/channels/<platform>.pyagainst thebase.pyinterface (parse inbound → normalized message; send outbound). - Wire it into
router.pyso inbound messages reach the agent and replies stream back. - Add a webhook route under
api/routes/v1/(signature-verified) and/or a polling entrypoint. - Reuse the existing conversation/session model — don't fork the agent pipeline.
Rules
- Inbound messages flow through
router.pyinto the same agent/session pipeline as web chat — don't duplicate agent logic per platform. - Always verify webhook signatures before acting on a payload.
- Store tokens encrypted (
CHANNEL_ENCRYPTION_KEY); never log or echo them. - Respect per-group/per-thread concurrency controls already in the adapters.
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 · 46 lines · 53 tokens per session scan A 7c48eeabd221
channel-bot is a skill published in the GitHub repository vstorm-co/full-stack-ai-agent-template (1,954 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 532 once invoked, about $0.0002 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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