Manage a CSV-backed contact database with SHA256 hashing for deduplication.
npx skills add https://github.com/athina-ai/goose-skills --skill contact-cache
Please help me install this Agent Skill. Skill: contact-cache Source: https://github.com/athina-ai/goose-skills/tree/main/skills/capabilities/contact-cache Command: npx skills add https://github.com/athina-ai/goose-skills --skill contact-cache
This Skill prevents duplicate outreach by maintaining a centralized, deduplicated database of contacts, ensuring efficient and organized lead management.
Check if the LinkedIn profile 'https://linkedin.com/in/johnsmith' is already in the contact cache.
High-intent search queries and answers about installing and using this skill.
Prevent duplicate outreach by maintaining a centralized, deduplicated CSV-backed contact database that automatically identifies and flags duplicate contacts based on their LinkedIn URL or email address. This ensures you never contact the same person twice across different campaigns.
Contact deduplication works by checking each contact's LinkedIn URL or email against the existing database, generating a stable SHA256 hash as a unique contact ID. It then tracks predefined outreach statuses so you can record and manage workflow progress for each unique lead.
Yes, you can use a CSV file as the backend database to add, check, update, export, and generate statistics for your contacts. The system tracks contact details, source strategy, and outreach status entirely through this flat file format.
The best way to check if a LinkedIn profile is already in your contact cache is to query the database by the LinkedIn URL. The system uses the URL to look up existing records and immediately flags the contact if a duplicate is found.
No, you do not need a dedicated CRM to avoid contacting the same person twice. This solution manages lead tracking and deduplication directly through a CSV file, providing a centralized contact database without the overhead of a full CRM platform.
A CSV-backed contact database for outreach tracking relies on flat file storage, meaning it lacks built-in concurrency controls and is best suited for single-user or batch processing workflows rather than real-time, multi-user collaborative CRM environments.
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