Colin is a context engine that keeps agent skills fresh by referencing live sources like GitHub, Linear, Notion, and HTTP endpoints. It benefits operations teams by automating skill updates and ensuring agents have the most current information. Colin integrates with Python-based workflows and supports Claude agents.
git clone https://github.com/PrefectHQ/colin.gitColin is a context engine designed to keep AI agent skills current by pulling live data from sources like GitHub, Linear, Notion, and HTTP endpoints. It uses template-based skill definitions with automatic dependency tracking and caching, so only stale content gets recompiled—saving compute resources and ensuring agents always have the latest information. Colin integrates directly with Claude Code, writing compiled skills to the skills folder for immediate availability. The engine tracks source versions and LLM call results separately, intelligently reusing cached outputs when source data hasn't changed. Operations teams benefit by automating the labor-intensive task of keeping agent knowledge synchronized with live systems.
[{"step":"Identify the source type and target. Determine whether you need updates from GitHub (e.g., a repository), Linear (e.g., a project), Notion (e.g., a database), or an HTTP endpoint (e.g., a REST API).","tip":"Use the exact identifier (e.g., repository name, project ID, or URL) to ensure Colin fetches the correct data."},{"step":"Specify the focus area. Tell Colin what to prioritize, such as bugs, new features, documentation, or specific keywords.","tip":"Narrowing the focus (e.g., 'only critical bugs') reduces noise and speeds up the summary process."},{"step":"Run the prompt in a Python environment with Colin installed. Example:","tip":"Ensure your Python environment has the `colin` package installed (`pip install colin`) and the necessary API keys/tokens for the source (e.g., GitHub PAT, Linear API key)."},{"step":"Review the output and take action. Use the summarized updates to inform your next steps, such as updating documentation, testing fixes, or adjusting project plans.","tip":"Cross-reference Colin’s output with your internal tools (e.g., Jira, Slack) to validate the impact of changes."},{"step":"Automate the process. Set up a cron job or scheduled task to run Colin periodically (e.g., daily) and push updates to your team’s communication channels (e.g., Slack, email).","tip":"Use tools like `cron` (Linux/macOS) or Task Scheduler (Windows) to automate updates. For Claude agents, integrate Colin into your workflow via the `colin` skill."}]
Maintain real-time team status dashboards by syncing Linear issues and GitHub commits into agent skills
Automatically extract and compile blockers and priorities from weekly notes using LLM processing
Keep knowledge bases fresh without manual updates by referencing live Notion documents
Build time-sensitive skills with configurable cache expiration to ensure currency
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/PrefectHQ/colinCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Use Colin to fetch the latest updates from [SOURCE_TYPE: GitHub/Linear/Notion/HTTP_ENDPOINT] for [REPOSITORY/PROJECT/DOCUMENT/URL]. Extract key changes, new issues, or recent updates and summarize them in bullet points. Focus on [SPECIFIC_FOCUS: bugs, new features, documentation changes, etc.]. Provide a brief analysis of how these updates might impact [RELATED_TASK/PROJECT].
Here’s the latest update from the **Acme Corp Engineering** GitHub repository (as of 2024-05-20): **Recent Changes:** - **Bug Fixes:** Resolved a critical memory leak in the `data_processor.py` module (PR #456, merged 2024-05-19). The issue was causing crashes in high-volume data pipelines. - **New Features:** Added a `real_time_monitoring` flag to the `api_client.py` class (PR #457, merged 2024-05-18). This enables real-time error tracking for the `/v2/analytics` endpoint. - **Documentation:** Updated the `README.md` to include setup instructions for the new `docker-compose.yml` file (PR #455, merged 2024-05-17). **Impact on Current Project:** The memory leak fix is critical for the upcoming Q2 data migration. The `real_time_monitoring` feature could reduce debugging time by 30% for the analytics team. The documentation update ensures new team members can onboard faster. **Next Steps:** - Test the `data_processor.py` fix in the staging environment. - Explore integrating the `real_time_monitoring` flag into the existing logging system. - Update internal wikis to reflect the new Docker setup instructions.
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