Agent skill · research science · borghei
research-summarizer
Synthesize raw user research (interviews, surveys, tickets) into themed findings and decision-ready briefs. Use when synthesizing user interviews, building a findings brief, or communicating research to stakeholders.
Why this skill is useful
Adds 3 executable scripts for research synthesis and insight scoring that aren't in public docs.
What it needs
Requires python3 installed locally. About 4k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
Research Summarizer A skill focused on synthesizing and communicating research — the part that comes after you've collected the data. Distinct from the research collection skills which guide interview design, recruiting, and protocol. This skill assumes you have raw inputs (transcripts, notes, survey responses) and need to turn them into trustworthy insights that drive product decisions. When to use this skill Synthesizing a batch of user interviews (typically 5-30) Pulling themes from open-text survey responses Synthesizing support tickets for product-truth analysis Building a findings brief for stakeholders Separating signal from anecdote in qualitative data Auditing existing research summaries for bias and reliability Preparing a research readout for execs / cross-functional teams Inputs the advisor expects Type of research artifacts (interviews, surveys, tickets, observations, sales notes) Volume and recency Research question(s) the synthesis is answering Audience for the output (PM team / exec / engineering) Decision the output should inform Clarify First Before generating the synthesis or brief, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] The research question — what decision this synthesis answers (drives the brief's lead and which themes matter) [ ] Audience and the decision it informs — PM team, exec, or engineering (sets brief altitude, length, and format) [ ] Artifact type and volume — interviews/surveys/tickets and how many (drives confidence, sample-size adequacy, and bias checks) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Workflows Workflow 1 — Organize and theme raw research 1. Capture raw research items (one per row) with source, date, segment. 2. Run researchsynthesisorganizer.py to surface theme clusters based on tagging, computed frequencies, and segment cross-cuts. 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/research-summarizer