Index Slack, Telegram, Discord, Linear, Gmail, and WhatsApp messages locally for unified search.
npx skills add https://github.com/dandaka/traul --skill traul
Please help me install this Agent Skill. Skill: traul Source: https://github.com/dandaka/traul/tree/main Command: npx skills add https://github.com/dandaka/traul --skill traul
Traul indexes and makes searchable your multi-source conversations locally, enabling fast recall, cross-channel search, and proactive signals without leaving your device.
Sync data with Traul by running traul sync, then perform a sample search such as traul search 'deployment'.
High-intent search queries and answers about installing and using this skill.
You can search across Slack, Telegram, and Discord conversations locally by syncing them into a unified local index. This enables fast cross-channel recall and hybrid vector + keyword search directly on your device.
Indexing WhatsApp and Gmail messages for offline search is best handled by a local-first memory tool that uses SQLite with FTS5 for text search. This setup allows you to run ad-hoc SQL-like queries and retrieve full threads without relying on cloud services.
Yes, you can use SQLite and sqlite-vec for hybrid vector and keyword search. This architecture uses SQLite as the core database with FTS5 for text search and sqlite-vec for embeddings, optionally leveraging Ollama for local embeddings.
To set up proactive signals and briefings from multiple messaging sources, you need a signals workflow that tracks and indexes sources like Linear and Claude Code sessions locally. This surfaces proactive insights and filters by channel and source automatically.
Local conversation search does work with Claude Code sessions and Markdown files. The system indexes these sources alongside communication platforms, allowing you to browse channels, get full threads, and perform unified search.
ChegginConfigure Telegram, Discord, and Slack notifications through interactive setup flows.
shalommaExecute SQL queries on a SQLite database of Israeli tech nano-influencers.
arunodaSearch extracted facts locally using Ollama embeddings and sqlite-vec.
steipeteIndex local files and search them with BM25, vector, and hybrid queries.
