Query, store, and loop on persistent research knowledge with YAML frontmatter.
npx skills add https://github.com/Cheggin/request-for-startups --skill research-cheggin
Please help me install this Agent Skill. Skill: research Source: https://github.com/Cheggin/request-for-startups/tree/main/skills/research Command: npx skills add https://github.com/Cheggin/request-for-startups --skill research-cheggin
Stores and organizes research findings so teams avoid duplicating effort and can loop back to prior experiments.
Start by querying existing research with research_query, then add findings with research_add, and run a loop with research_loop to begin.
High-intent search queries and answers about installing and using this skill.
To avoid duplicating experiments, query existing research findings before building, then add new results with structured YAML metadata to an append-only ledger for persistent institutional memory.
Maintain institutional memory by logging experiment results into a central ledger and reading back wiki pages by category, ensuring prior coding, design, and growth findings remain accessible.
Run research loops by querying web or internal sources to generate briefs and ideas, then automatically logging the experiment results into a central append-only ledger for future reference.
No, the research knowledge search does not use vector embeddings; it relies entirely on keyword and tag matching to surface existing findings from the structured YAML frontmatter.
Yes, you can check if an experiment has already been tried by querying the persistent research ledger before building, which surfaces prior work and prevents redundant efforts across categories.
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