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research

continuous-research

Runs recurring research cycles in OpenAI, DeepSeek, Perplexity, or Tavily and maintains a scored knowledge base.

Run recurring research cycles against configurable domain configs. Each cycle discovers new entries, scores them through a weighted quality gate, compares against the previous knowledge base, and writes an updated KB with a change delta.

When to run
"Run a research cycle on procurement-ai"
Time saved
8 hours manual research → 15 minutes automated
How often
Weekly per domain (cron scheduled)
Compounds
Each cycle builds on previous — trends emerge over time

How it runs

  1. Load domain config + previous KB
  2. nlm-skill → deep research (50-70 sources)
  3. Import & query NotebookLM notebook
  4. Map entries → score with calibrated rubric
  5. Delta detection vs previous cycle
  6. Review findings, adjust weights
  7. Write KB + changelog + briefing

Why it matters

  • 32× faster
  • Change detection between cycles
  • Weighted scoring surfaces what matters
Difficulty
advanced
Input
conversation
Output
document
Needs
api-key
Used by
Head of Marketing, Product Manager

What it needs

  • domain-id — loaded from your vault context
  • domain-id-changelog — loaded from your vault context
  • domain-id-kb-latest — loaded from your vault context

What you get

  • domain-config (markdown)
  • kb-latest (markdown)
  • quick-scan-results (markdown)
  • reranked-results (markdown)
  • research-all-report (markdown)
  • research-cycle-report (markdown)
  • research-report-pdf (pdf)

Ask it like this

  • Run a research cycle for the procurement-ai domain
  • Set up a new research domain to monitor competitor AI tools in logistics
  • What's new in the procurement-ai knowledge base since last cycle?
  • Run all overdue research domains
  • Generate a PDF report from the latest procurement-ai KB