> ## Documentation Index
> Fetch the complete documentation index at: https://packmind-feat-rename-cli-packmind.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Update from External Sources

Your team's coding knowledge doesn't live only in the codebase. Valuable conventions, decisions, and best practices are scattered across pull request reviews, Slack discussions, issue trackers, and documentation wikis. Packmind can automatically mine these external sources using AI agent skills and [MCP servers](https://modelcontextprotocol.io/), classify findings for playbook relevance, and submit them as [change proposals](/playbook-maintenance/change-proposals) for your team to review.

<Tip>
  Looking to do a one-time import from a knowledge base? See [Import from
  Confluence, Notion and more](/tools/import-from-knowledge-base).
</Tip>

## Why Mine External Sources?

* **Knowledge is scattered** — PR review comments, Slack threads, Jira issues, and wiki pages all contain insights that should live in the playbook.
* **Manual updates are easy to forget** — Without automation, new conventions agreed upon in a PR review or a Slack discussion may never make it into the playbook.
* **Continuous mining keeps the playbook current** — Regularly scanning external sources ensures emerging patterns and decisions are captured as they happen.
* **Change proposals ensure human review** — Every finding goes through a [change proposal](/playbook-maintenance/change-proposals), so your team stays in control of what enters the playbook.

## How It Works

The workflow follows a three-stage pipeline:

1. **Fetch** — A source skill queries an external tool through its MCP server (e.g., fetching merged PR review comments from GitHub, or retrieving recent channel messages from Slack).
2. **Classify** — The AI agent analyzes the fetched data and identifies items relevant to coding conventions, best practices, architectural decisions, or recurring patterns.
3. **Update** — Classified findings are handed off to the `packmind-update-playbook` skill, which creates [change proposals](/playbook-maintenance/change-proposals) in Packmind — standards, commands, or skills depending on the nature of each finding.

<Info>
  This workflow is AI agent agnostic. It works with any agent that supports MCP
  — Claude Code, GitHub Copilot, Cursor, Windsurf, and others.
</Info>

## Supported Sources

| Source                       | What is mined                              |
| ---------------------------- | ------------------------------------------ |
| **GitHub PR Comments**       | Review comments from merged pull requests  |
| **Slack Conversations**      | Channel discussions and threaded messages  |
| **Jira Issues**              | Resolved issues and their comments         |
| **GitLab MR Comments**       | Review comments from merged merge requests |
| **Confluence Documentation** | Pages and spaces                           |
| **Notion Documentation**     | Pages and databases                        |

<Tip>
  These are example use cases. Any tool that exposes an MCP server can be used
  as a source — see [Adding Custom Sources](#adding-custom-sources) below.
</Tip>

## Example Skills Repository

The [demo-use-case-skills](https://github.com/PackmindHub/demo-use-case-skills) repository is a **public showcase** of example source skills. None of these skills are built into or available within the Packmind app — they are provided as open-source examples that teams can use as-is, adapt to their needs, or draw inspiration from when building their own. Inside you will find:

* **Source skills** for each integration — one skill per external source, each handling data fetching, noise filtering, and relevance classification.
* **Shared skills** — `packmind-update-playbook` (creates change proposals from findings) and `packmind-list-commands` (prevents duplicate artifacts).
* **CI workflow examples** — GitHub Actions configurations for running source skills on a schedule, so your playbook is updated automatically without manual intervention.

Each integration folder includes its own README with setup instructions, MCP server configuration, and usage examples.

## Adding Custom Sources

The architecture is extensible by design. The [example skills repository](https://github.com/PackmindHub/demo-use-case-skills) demonstrates how to build source skills, and you can follow the same pattern to create your own. To add a new external source, create a skill that fetches data from the source's MCP server, classifies findings for playbook relevance, and hands them off to `packmind-update-playbook`. Any tool with an MCP server can become a source — monitoring tools, design systems, internal APIs, and more.

## Learn More

* [Change Proposals](/playbook-maintenance/change-proposals) — How change proposals are reviewed and applied
* [Updating Your Playbook](/playbook-maintenance/updating-your-playbook) — Overview of all playbook update workflows
* [Import from Confluence, Notion and more](/tools/import-from-knowledge-base) — One-time import from knowledge bases
* [Skills Management](/concepts/skills-management) — How skills work and how to create them
* [CLI Reference](/tools/cli) — CLI installation and commands
* [Demo Use Case Skills](https://github.com/PackmindHub/demo-use-case-skills) — Example source skills repository