Can it split multiple topics from one conversation?

Yes, the internal sub-agent can identify multiple distinct research topics within a single conversation and create separate entries for each.

Which database does it use?

It is specifically designed to store data in the research_items table of a configured Supabase project.

How does this differ from the memory-save skill?

While memory-save focuses on high-level long-term memory and design decisions, research-save is built to capture detailed investigation results, facts, and raw content for specific research topics.

Will I be asked to confirm every save operation?

To maintain workflow speed, this skill only asks for confirmation if the research target is ambiguous. If the context is clear, it proceeds with structuring and saving immediately.

Does it prevent duplicate entries?

Yes, the skill generates an MD5 content hash for every entry and uses an ON CONFLICT DO NOTHING SQL clause to ensure the same information isn't saved twice.

Research Data Capturer

Name: Research Data Capturer
Author: iketomo

byiketomo

0•

Database Management

Captures and structures conversation-based research and discussions into a Supabase database for long-term knowledge management.

The research-save skill automates the extraction of valuable insights, facts, and summaries from your AI conversations, storing them in a structured format within a Supabase database. By delegating the heavy lifting of content analysis and SQL execution to a specialized sub-agent, it preserves the main agent's context window while ensuring research data is meticulously organized into titles, summaries, and key takeaways. This skill is essential for users building a searchable knowledge base from their iterative brainstorming and investigation sessions.

Key Features

01Uses a specialized sub-agent to minimize main agent context consumption

02Seamless Supabase integration with built-in deduplication using MD5 hashing

03Automated structuring of chat logs into title, summary, facts, and insights

04Optimized for speed with minimal user confirmation for clear research targets

05Traceability support with metadata for various AI source tools like Claude or ChatGPT

060 GitHub stars

Use Cases

01Recording technical architectural discussions and decisions during design phases

02Archiving market research or competitive analysis findings gathered during a session

03Building a structured knowledge repository from complex AI-assisted troubleshooting

Key Features

01Uses a specialized sub-agent to minimize main agent context consumption

02Seamless Supabase integration with built-in deduplication using MD5 hashing

03Automated structuring of chat logs into title, summary, facts, and insights

04Optimized for speed with minimal user confirmation for clear research targets

05Traceability support with metadata for various AI source tools like Claude or ChatGPT

060 GitHub stars

Use Cases

01Recording technical architectural discussions and decisions during design phases

02Archiving market research or competitive analysis findings gathered during a session

03Building a structured knowledge repository from complex AI-assisted troubleshooting