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SkaleAgents

SkaleAgents is an AI review tool for infrastructure and pre-deployment scenarios. It can connect to the AI clients you already use, inspect application and infrastructure configurations, and provide suggested changes. It supports integration via MCP (Model Context Protocol) with editors or chat clients such as Cursor, Claude Code, Claude Desktop, ChatGPT, and Codex, allowing review results to return to the same conversation. Users can also browse public bots and a prompt library to bring expert prompts into their own assistants; or submit IaC text as an audit task, view findings in a Watchtower session, and set up scheduled audits by cron expression and time zone, with optional webhooks to receive completion or failure notifications. The website provides interactive examples that require no account, for experiencing how sample checks are updated; the examples do not represent real audit results.

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About this tool

Purpose

SkaleAgents is aimed at "the work behind deployment," helping developers inspect applications and infrastructure before submitting code or infrastructure changes and get hints on what needs to be changed. The official description is: connect to the AI you already use, and it will inspect your application and infrastructure, then tell you what needs to change.

Main Capabilities

  1. Integration with existing AI clients: supports clients such as Cursor, Claude Code, Claude Desktop, ChatGPT, and Codex, with review results returned in the same conversation.
  2. Based on MCP (Model Context Protocol): compatible AI clients can call SkaleAgents tools; the officially provided hosted server address is https://skaleagents.com/mcp。
  3. Architecture review: have the assistant call review_architecture with the relevant source code or infrastructure content, and optionally choose focus areas such as security, reliability, cost, or general.
  4. Built-in check reference: public reference pages show what each built-in check looks for, what input it requires, and what the results cannot prove, viewable without an account.
  5. Bot directory: search by task, filter by specialty, and after reading a bot's description, bring the prompt into your own assistant and use it with project context.
  6. Prompt library: prompts are listed by title, type, and category; open one to read and copy the full description; after logging in, you can save your own reusable prompts.
  7. Creator Studio: start from security, reliability, or capacity templates, customize the description, save drafts, and choose to publish publicly or keep private.
  8. Audit tasks and Watchtower: after logging in, paste IaC, choose an expert focus such as DevSecOps, SRE, MLOps, or scaling, and attach telemetry JSON when helpful; open Watchtower from the task to view findings, share the session, and send redirection instructions.
  9. Scheduled audits: create scheduled audits using cron expressions and time zones; optional webhooks receive completion or failure notifications.

How to Use

Use via MCP in an editor

  • Configuration: on the MCP page, select your client and add the hosted server URL.
  • Authorization: connect in the client, log in if necessary, and approve MCP access; credentials are handled automatically, and the official instructions say to connect via Google OAuth.
  • Request: have the assistant call review_architecture with the relevant source code or infrastructure content, and read the findings in the same conversation.

Use prompts and bots

  • In the public directory, browse by specialty and open a bot to read its description.
  • In Actions, send the prompt to Claude, ChatGPT, Grok, Codex, or Perplexity; if using Gemini or another agent, copy the prompt.
  • Provide the relevant code or configuration, and ask for evidence and verification steps before adopting suggestions.

Submit an audit task

  • After logging in, paste IaC, choose an expert such as DevSecOps, SRE, MLOps, or scaling, and requiredOptionally attach telemetry JSON.
  • Open Watchtower from a task to review findings, share the session, and send redirection instructions.
  • When repetition is needed, create a scheduled audit using a cron expression and time zone, with an optional webhook to receive completion or failure notifications.

Experience without an account

You can modify the sample deployment settings and observe how three local rules update the sample findings. The official description states that this is an interactive example, not a real-time AI audit or a deployable checklist, and that it does not execute code or send anything to an API.

Use cases

  • Perform self-checks for security, reliability, cost, or general purposes on infrastructure content such as Terraform and Kubernetes manifests before deployment.
  • Within an editor or chat client, have an AI assistant invoke review tools using project context.
  • Teams can share audit results and view findings together in a Watchtower session.
  • Run scheduled audits at a fixed cadence and receive notifications via webhook.
  • Browse public bots and prompts to obtain expert-style review prompts.

Limitations

  • Scheduled audits only review the provided infrastructure text and do not automatically track changes in a repository.
  • Sample findings and the interactive demo do not represent real audit results; actual values depend on your workload.
  • Without an MCP setup, using the prompt-based approach means you are subject to the account and usage limits of the AI provider you use.
  • The official text does not provide specific pricing, costs, or performance guarantees.
  • Other features, platforms, or usage steps not described in the materials are not provided at this time.

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