lighthouse-ai-organization/lighthouse
Connects the Lighthouse MCP server and adds skills for building metrics, investigating alerts, fixing failing metrics, exploring data, tuning alert noise, connecting sources and managing members.
Help a Lighthouse organization admin manage members, invitations, roles, business groups and organization settings, confirming every change before making it. Use when the user asks who is in their Lighthouse organization, or to invite, remove or change the role of a member, manage business groups, or change organization settings.
Build a new Lighthouse metric (a scheduled check on the user's data that alerts in Slack or email when a threshold is met) from a plain-language goal, test it, backtest it and take it live. Use when the user asks to monitor, track, watch or alert on something in their data with Lighthouse.
Connect a data source (Postgres, Snowflake, BigQuery, SQL Server, HTTP connector, Mixpanel), a Slack or email notification destination, or an integration action source to Lighthouse, or reconnect one with new credentials, through a secure Lighthouse link so credentials never pass through the chat. Use when the user wants to connect, add, reconnect or fix the credentials of a source in Lighthouse.
Explore the tables and columns in a Lighthouse data source, run narrow read-only queries to understand the data, and create or adjust Lighthouse data sets that metrics are built on. Use when the user asks what data Lighthouse can see, what could be monitored, or to model a table as a data set.
Diagnose and fix a Lighthouse metric whose runs fail or that Lighthouse marked INVALID, using its execution errors and change history, then test the fix and resume it with the user's agreement. Use when a metric is failing, erroring, INVALID, stopped running, or shows no new results.
Investigate a Lighthouse alert, find out what fired, whether it is still firing, whether it is real and what likely caused it, then acknowledge, resolve or mark it as a hit when the user agrees. Use when the user mentions a Lighthouse alert, a metric that fired, a Slack alert from Lighthouse, or asks why a metric is alerting.
Make a noisy or silent Lighthouse metric alert at the right rate by measuring its history, backtesting candidate thresholds or filters, and applying the best one with the user's agreement. Use when the user says a metric alerts too often, too rarely, is noisy, has false positives, or asks to tune or adjust a threshold.