Query LanceDB email workspaces to locate and summarize email information.
npx skills add https://github.com/0xfe/ragmail --skill ragmail
Please help me install this Agent Skill. Skill: ragmail Source: https://github.com/0xfe/ragmail/tree/main/.agents/skills/ragmail Command: npx skills add https://github.com/0xfe/ragmail --skill ragmail
Ragmail enables fast, local semantic search and analytics over LanceDB email workspaces, turning large sets of email data into actionable insights such as who said what, when, and related costs, without leaving the user’s environment.
Query a Ragmail workspace (e.g., 2026) for emails matching a term and return concise results.
High-intent search queries and answers about installing and using this skill.
To search and summarize email data in LanceDB workspaces, you can query emails and email_chunks tables using Python scripts and a bundled CLI to locate information, count messages by sender or date, and extract topic summaries.
Yes, you can count emails by sender and filter by date for ad-hoc trend analysis by running Python-based queries against your LanceDB workspace, enabling fast local semantic search and analytics over your email datasets.
You need LanceDB and a stored email dataset to extract monetary references and topic summaries. The Skill relies on Python scripts that connect to LanceDB workspaces and use FTS indexing to answer cost-related inquiries.
Yes, LanceDB full-text search supports querying both full emails and email chunks. The Skill connects to LanceDB workspaces and processes both the emails and email_chunks tables with FTS indexing to locate and summarize information.
The best way to perform local semantic search over LanceDB email datasets is using embedded querying with Python scripts and a bundled CLI, turning large sets of email data into actionable insights directly within your environment.
A limitation when analyzing large email datasets in LanceDB workspaces is that the Skill relies on Python scripts and FTS indexing for queries, meaning analysis is constrained to the data structures and tables available within the local LanceDB environment.
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