> For the complete documentation index, see [llms.txt](https://docs.lleverage.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lleverage.ai/run-operations/agent/agent-in-slack.md). # Agent in Slack Work with the Lleverage Agent from Slack when your organisation has connected the Channel. Slack gives you another place to work with the same Lleverage Agent. Use it for everyday Agent work when staying in Slack is more convenient than opening the web app. ## Start and continue work Use a direct message or a supported Slack conversation where the Lleverage app is available. Keep one thread focused on one piece of work. Start a new conversation for an unrelated outcome. ## Project scope A Slack conversation can be pinned to one Project or run without a fixed Project. Pin it when the work clearly belongs to one Project or when you want its Skills, context and connected systems to be unambiguous. An unpinned conversation can use eligible capabilities from Projects the current speaker is authorised to access. ## Files and approvals Slack can carry supported files into and out of Agent conversations. If the Agent is waiting on a write-access approval, you can answer the pending decision with a typed reply. The reply answers the approval; it does not bypass it. For detailed output review or deeper execution inspection, open the work in the web product. Need Slack configured? See [Set up Slack](/administer-and-govern/control/channels/set-up-slack.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.lleverage.ai/run-operations/agent/agent-in-slack.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.