AI-assisted development | Slack Developer Docs guide Skip to main content Guides Reference Samples Tools Changelog Dev Program MANAGE APPS * Welcome! * Quickstart * Resources * * Slack platform concepts * AI in Slack * Overview * Build agents on Slack * Build with AI * AI-assisted development * Vercel Slack Agent Skill * OpenClaw Slack Plugin * Slack MCP and Skills Plugin * MCP * Other integration methods * APIs * App management * App manifests * Admin resources * Authentication * Block Kit * Enterprise * Enterprise Search for apps * GovSlack * Interactivity * Messaging * Slack Marketplace * Surfaces * Workflows * * Legacy * * 日本語版ページ * * AI in Slack * Build with AI Copy as markdown On this page AI-assisted development Developers using an AI coding assistant, IDE, or a chat-based LLM to assist in their Slack app building journey have several ways to give their tools better context about the Slack platform. Install the Slack MCP server ​ The Slack MCP server gives your AI tool direct access to your Slack workspace data. Once connected, your AI assistant can search channels, read messages, and use real conversation context to help you build. Set up instructions are available for Claude and Cursor , among other MCP-compatible clients. Use the Slack Skills Plugin ​ The Slack MCP and Skills Plugin bundles a set of skills that can help you develop on the Slack platform. Skills are focused sets of instructions and references that your agent can load to accomplish related tasks, such as validating Block Kit objects and scaffolding a Slack app for you. It's available for both Claude Code and Cursor. Read more on our Slack MCP and Skills Plugin page . Point your AI tool to llms.txt ​ Add a rule to your AI tool's project instructions (for example, a CLAUDE.md or .cursorrules file) telling it to start with our documentation index: For Slack platform documentation, start with https://docs.slack.dev/llms.txt This file provides a structured overview of the platform, designed for LLM consumption. A full index of every documentation page is available at docs.slack.dev/llms-sitemap.md . Ingest the docs in bulk ​ The complete docs site is concatenated into an llms-full.txt file. We also offer language-specific bulk files. File Contents llms-full.txt Everything llms-full-platform.txt Slack platform docs, reference, and language-agnostic tools (Slack CLI, GitHub Action) llms-full-js.txt Bolt for JavaScript and the Node Slack SDK llms-full-python.txt Bolt for Python and the Python Slack SDK llms-full-java.txt Bolt for Java and the Java Slack SDK llms-full-deno.txt Deno Slack SDK Pair a language file with llms-full-platform.txt for platform concepts, authentication, and API reference. These files are sized for indexing and retrieval pipelines, not for a single prompt. You can drop individual pages into prompts as described in the access pages as markdown section. Access pages as markdown ​ Every documentation page on this site is available as raw markdown by appending .md to the URL. For example: https://docs.slack.dev/quickstart → https://docs.slack.dev/quickstart.md This is useful for: * Copying page content into a prompt for context * Agents that fetch URLs dynamically (MCP fetch tool, Claude Code's WebFetch, etc.) * Building up a local reference for your project You can also use the Copy as Markdown button at the top of any page to copy its contents to your clipboard. Use the "Open in..." feature ​ Each documentation page has an "Open in..." feature within the "Copy as markdown" button drop-down that lets you send the page directly to ChatGPT, Claude, or Perplexity as context for a conversation. Use the Slack CLI ​ AI tools and agents can run Slack CLI commands directly in a terminal. The Slack CLI supports app scaffolding and local development. The slack docs command searches Slack API documentation from the command line, which is useful for agents that can execute shell commands. Tips for better results ​ When prompting an AI tool about Slack development: * Specify your framework or SDK. Mention whether you're using the Bolt framework (and which language) or one of the SDKs , since implementation details differ significantly. * Reference specific API methods. Instead of "send a message," say "use chat.postMessage " to get more accurate guidance. * Provide your app's manifest or scopes. Sharing your current app configuration helps the LLM suggest relevant next steps without guessing. * Link to the relevant docs page. Paste the .md URL of the page you're working from so the AI tool can fetch it for full context. 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