How to Build a Knowledge Base Bot with OpenClaw in Slack

Why Your Team's Knowledge Is Leaking

Every engineering and product team has the same problem: critical knowledge lives in too many places at once. An answer to a recurring bug is buried in a Linear comment from six months ago. The reasoning behind an architectural decision exists only in a Notion doc that nobody remembers the name of. A new hire spends three days asking questions that a senior engineer answered in a GitHub PR review last quarter.

The fix isn't another wiki. It's an agent that already knows where everything lives — and can retrieve, synthesize, and explain it on demand, right inside Slack where your team already works.

This guide walks you through building exactly that: a persistent, context-aware knowledge base bot using OpenClaw, connected to your real tools through SlackClaw.

What You're Actually Building

Before diving into steps, it helps to be clear on what this bot does and doesn't do. You're not building a static FAQ bot. You're building an autonomous agent with:

• Persistent memory — it remembers previous conversations, decisions, and context across sessions, so you don't re-explain your stack every time
• Live tool access — it can query Notion, search GitHub issues, pull Jira tickets, and read Gmail threads in real time
• Reasoning ability — it synthesizes information from multiple sources instead of just returning a single search result
• A dedicated execution environment — SlackClaw runs each team's agent on its own server, so your data stays isolated and the agent can run long tasks without timeouts

The result is something closer to a knowledgeable team member than a search bar.

Step 1: Connect Your Knowledge Sources

SlackClaw connects to 800+ tools via one-click OAuth, which means you don't need to write any API glue code to get started. For a knowledge base bot, you'll want to connect at least the following:

Documentation and Notes

• Notion — connect your team wiki, runbooks, and onboarding docs
• Google Drive — spec sheets, design docs, meeting notes
• Confluence — if your organization uses Atlassian's wiki

Engineering Context

• GitHub — PRs, issues, commit messages, and inline code comments are goldmines of decision context
• Linear or Jira — ticket history, bug reports, and project timelines

Communication History

• Gmail — particularly useful for customer-facing teams that need to reference vendor conversations or client decisions
• Slack history (via SlackClaw's built-in context) — the agent can reference previous Slack threads it was part of

Once you've authorized these integrations in SlackClaw's dashboard, the agent can call any of them as tools during a conversation. You don't need to pre-index anything — the agent queries sources live when it needs them.

Step 2: Define Your Agent's Core Skills

OpenClaw uses a skill system to define what your agent knows how to do beyond raw tool access. Think of skills as reusable instruction sets that shape how the agent behaves in recurring situations.

For a knowledge base bot, you'll want to define at least three custom skills:

Skill 1: Answer a Question with Source Attribution

This is the core skill. When someone asks a question, the agent should search across connected tools, synthesize what it finds, and cite where the answer came from — so people can verify and dig deeper. Learn more about our pricing page.

skill: answer_with_sources
description: >
  When asked a question, search Notion, GitHub, Linear, and Jira
  for relevant information. Synthesize a clear answer and list the
  specific documents, tickets, or PRs you referenced. If the answer
  is uncertain or the sources conflict, say so explicitly.
tools:
  - notion_search
  - github_search_issues
  - linear_search
  - jira_search
output_format: >
  Answer: [synthesized response]
  Sources: [list of links or document titles]

Skill 2: Summarize a Project or Topic

When a new team member joins a project or a stakeholder asks for a status briefing, the agent should be able to pull together a coherent summary from scattered sources. Learn more about our integrations directory.

skill: summarize_project
description: >
  Given a project name or topic, pull recent activity from Linear
  or Jira, find related Notion docs, and check GitHub for recent
  PRs or issues. Return a structured summary: current status,
  recent decisions, open questions, and key people involved.
tools:
  - linear_get_project
  - jira_get_project
  - notion_search
  - github_search_pulls

Skill 3: Capture and Store a Decision

Knowledge bases fail because people forget to update them. This skill flips the workflow — the agent captures decisions during the Slack conversation and writes them back to Notion automatically.

skill: capture_decision
description: >
  When a team reaches a decision in conversation, extract the key
  decision, the reasoning, the people involved, and any alternatives
  considered. Write this to the team's Decision Log in Notion with
  today's date and a link back to this Slack thread.
tools:
  - notion_create_page
  - notion_append_to_database

This last skill alone is worth the setup time. It turns your Slack conversations from ephemeral chat into a living record.

Step 3: Set Up Persistent Memory

One of the things that separates SlackClaw's implementation of OpenClaw from a basic chatbot is persistent memory. The agent maintains context across conversations — not just within a single thread.

In practice, this means:

• If you tell the agent "our main repo is called platform-core and we use Linear for tracking," it remembers that in every future conversation
• If the agent helped you debug an issue last week, it can reference that context when a related question comes up today
• Onboarding information you give the agent once doesn't need to be repeated

To make the most of this, spend five minutes during setup giving the agent a structured briefing in Slack:

"You are our team's knowledge assistant. Our main product is [X]. We track engineering work in Linear under the team called [Y]. Our Notion workspace has a top-level page called 'Engineering Wiki' that's the canonical source for internal docs. When answering questions, prefer Notion and GitHub over email. We have [Z] engineers and our on-call rotation lives in [tool]."

The agent stores this in its persistent memory layer and applies it automatically going forward. You're essentially onboarding the agent the same way you'd onboard a new team member.

Step 4: Deploy and Test in a Real Scenario

Once your integrations are connected and your skills are configured, invite the SlackClaw bot to a channel — either a dedicated #knowledge-bot channel or directly into your #engineering or #product channels.

Run through a few real test cases before announcing it to the team: For related insights, see OpenClaw for Slack: The Future of AI-Powered Team Coordination.

1. "Why did we choose [technology X] over [technology Y]?" — tests GitHub PR and Notion search with reasoning
2. "What's the current status of the [project name] migration?" — tests the summarize_project skill across Linear and GitHub
3. "We just decided to deprecate the V1 API by Q3. Can you log that decision?" — tests the capture_decision skill and Notion write access
4. "What did we talk about last week regarding authentication?" — tests persistent memory and Slack context retrieval

For each response, check that sources are cited, that the synthesis is accurate, and that write operations (like creating Notion pages) worked correctly. Adjust your skill definitions if the agent is missing context or being too verbose.

Pricing Considerations for Team Rollout

One practical advantage of SlackClaw's credit-based pricing is that you're not paying per seat. A knowledge base bot gets most of its value from being available to everyone — the new hire, the on-call engineer at 2am, the PM who needs a quick answer before a stakeholder call. Per-seat pricing punishes exactly that kind of broad adoption.

With credits, you pay for what the agent actually does. A question that requires pulling three Notion pages and two Linear tickets costs more than a simple factual recall, which is a fair tradeoff. You can monitor usage in the SlackClaw dashboard and set credit limits to avoid surprises.

For most teams, a knowledge base bot of this type runs efficiently because the agent reuses memory rather than re-fetching the same context repeatedly.

What to Expect After a Few Weeks

The first week, people will test it with low-stakes questions. By week two, someone will use it to answer a question they would have previously Slacked a senior engineer about. By week four, the captured decisions in Notion will start to feel like a genuine institutional record rather than a chore. For related insights, see Using OpenClaw with Dropbox in Slack.

The compounding effect of persistent memory means the agent genuinely gets more useful over time — not because the model improves, but because the context it carries grows richer with every conversation.

That's the real value here: not a bot that answers questions, but an agent that knows your team — and keeps knowing it, session after session, without ever asking you to repeat yourself.