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Agent skill

Research Summarizer

by borghei in borghei/Claude-Skills

Synthesize raw user research (interviews, surveys, tickets) into themed findings and decision-ready briefs.

MITAuto-check passedProduct & Project Management

Install Research Summarizer

skills CLI
$ npx skills add borghei/Claude-Skills --skill research-summarizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills research-summarizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/product-team/research-summarizer .claude/skills/research-summarizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
research-summarizer
GitHub stars
881
Token cost
~1.9k tokens
SKILL.md length
874 words
Files
7 (incl. scripts, references)
Skills in repo
349
Repo updated
First seen
Licence
MIT

At a glance

Synthesize raw user research (interviews, surveys, tickets) into themed findings and decision-ready briefs.

  • Works in 3 steps: Capture raw research items (one per row)… → Run research_synthesis_organizer.py to… → Refine themes manually; promote to…
  • Synthesizing user interviews
  • SKILL.md covers When to use this skill, Inputs the advisor expects, Clarify First and Workflows, plus 5 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Research Summarizer is an agent skill from borghei/Claude-Skills. 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.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/communicating-research-findings.md`, `references/insight-quality-and-bias.md` and `references/research-synthesis-frameworks.md`).

It sits in Product & Project Management, covering User research. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Synthesizing user interviews
  • Building a findings brief
  • Communicating research to stakeholders

Example prompts

  • “/research-summarizer”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Capture raw research items (one per row) with source, date, segment.
  2. Run research_synthesis_organizer.py to surface theme clusters
  3. Refine themes manually; promote to insights.

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Research Summarizer loads about 1.9k tokens when it runs, and up to ~7k if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 874 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 874 words, ~1,928 tokens.

Download SKILL.mdSave it as .claude/skills/research-summarizer/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
research-summarizer
description
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.
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
product-team
metadata.domain
user-research
metadata.updated
2026-05-27
metadata.tags
user-research, synthesis, insights, qualitative, ux, communication

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 research_synthesis_organizer.py to surface theme clusters based on tagging, computed frequencies, and segment cross-cuts.
  3. Refine themes manually; promote to insights.
bash
python3 research-summarizer/scripts/research_synthesis_organizer.py \
  --input research_items.json --format markdown
Workflow 2 — Score insight quality
  1. List proposed insights with supporting evidence count + segment coverage.
  2. Run insight_quality_scorer.py to grade each insight on Confidence, Specificity, Action-readiness, and Bias risk.
  3. Keep High / Medium insights; demote Low to "questions for further research."
bash
python3 research-summarizer/scripts/insight_quality_scorer.py \
  --input insights.json --format markdown
Workflow 3 — Generate a findings brief
  1. Capture the question, top insights, supporting evidence, decisions.
  2. Run findings_brief_generator.py to produce the structured brief.
bash
python3 research-summarizer/scripts/findings_brief_generator.py \
  --input findings.json --format markdown

Decision frameworks

Observation → Pattern → Insight → Recommendation

A clean synthesis ladder:

  1. Observation — direct quote or behavior ("user X said Y")
  2. Pattern — repeats across users ("4 of 7 users said Y")
  3. Insight — interpreted explanation ("users avoid Y because Z")
  4. Recommendation — action implied ("redesign Z to address Y avoidance")

Each level requires more confidence than the last. Don't skip from observation directly to recommendation.

Insight quality dimensions
  • Confidence: how many independent sources support it
  • Specificity: is the insight specific enough to action?
  • Bias risk: is the sample / interpretation biased?
  • Decision impact: does this insight change anything?

A high-quality insight scores well on all four. Most rough notes are strong on confidence but weak on specificity (or vice versa).

Sample size for qualitative research

A rough heuristic for how many interviews are enough:

GoalSuggested N
Discover the space (early product)5-8
Validate hypotheses8-12
Persona definition12-20
Detect quantitative signal in qual20-30+
Validate cross-segment5-8 per segment

Diminishing returns after the patterns repeat 2-3 times. If you keep hearing new things, you're not done.

Show full SKILL.md (349 more words)Show less
When qualitative data lies (common biases)
  • Confirmation bias — interviewers pull the quotes they expected to hear
  • Acquiescence bias — participants agree to be polite
  • Recall bias — what users remember vs what they did
  • Selection bias — who agreed to interview is not representative
  • Recency bias — recent interviews carry disproportionate weight
  • Anchor bias — first interview shapes interpretation of later ones
  • Demand characteristics — participants guess what you want to hear

Counter: use a second coder, structure your guide, sample diversely, and report negative evidence.

Common engagements

"Help me synthesize 12 user interviews"
  1. Make sure you have transcripts (or detailed notes).
  2. Tag each interview by demographic, journey stage, key behaviors.
  3. Surface 5-10 themes from initial tagging.
  4. For each theme, count: how many users? from which segments? evidence quality?
  5. Promote 3-5 themes to insights; demote the rest to "questions for next round."
  6. Add 1-2 unexpected findings (the "we didn't expect this" insight).
"Translate the research into a one-pager for execs"
  1. Lead with the question being asked.
  2. Lead with the answer (1-2 sentences); details follow.
  3. 3-5 insights with evidence; not more.
  4. Decisions / recommendations that follow.
  5. What you don't know yet (research limits + next-step questions).
  6. Methodology one-liner (N, segments, dates).
"Our research found contradictory things"
  1. First: is one finding from a different segment? Often the contradiction is segment-based.
  2. Second: was sample biased toward one side?
  3. Third: maybe both are true and the system has tensions worth surfacing.

Anti-patterns to avoid

  • Cherry-picked quotes. Always provide the count + context.
  • Insight without evidence. "Users want X" without supporting observations.
  • Anecdotal generalization. One angry user doesn't define the population.
  • Reporting interview-by-interview. Synthesis means seeing across users.
  • Hiding the negative evidence. Disconfirming evidence is valuable.
  • Brief that's longer than needed. Briefer = better-read.
  • Mixing facts and interpretations. Be clear which is which.
  • Skipping methodology. Readers need to evaluate the trust level.

References

  • references/research-synthesis-frameworks.md — affinity, thematic analysis, frameworks
  • references/insight-quality-and-bias.md — quality dimensions, bias catalog, validation
  • references/communicating-research-findings.md — brief formats, presentation patterns
  • product-team/ux-researcher-designer — research design + collection
  • product-team/product-strategist — strategic input from insights
  • product-team/product-analytics — quant complement to qual
  • c-level-advisor/chief-customer-officer-advisor — VoC program context

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in product-team/research-summarizer of borghei/Claude-Skills.

  • SKILL.md
  • references/communicating-research-findings.md
  • references/insight-quality-and-bias.md
  • references/research-synthesis-frameworks.md
  • scripts/findings_brief_generator.py
  • scripts/insight_quality_scorer.py
  • scripts/research_synthesis_organizer.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

Research Summarizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Research Summarizer compared with similar skills
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Research Summarizer this skillborghei/Claude-Skills881—~1.9kAutomated safety check: PassMIT
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Fable DomainSahir619/fable-method2.3k—~2.6kAutomated safety check: PassMIT
Produck Feedback To Buildtryproduck/produck-skills510—~1kAutomated safety check: PassApache-2.0
MITRE Problem Framing Canvasdeanpeters/Product-Manager-Skills7.2k2 repos~4.5kAutomated safety check: PassCustom licence
Customer InterviewsRefoundAI/lenny-skills1.4k—~1.7kAutomated safety check: PassMIT

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Categories

Questions about Research Summarizer

What does Research Summarizer do?

Synthesize raw user research (interviews, surveys, tickets) into themed findings and decision-ready briefs. Research Summarizer is an agent skill from borghei/Claude-Skills. Synthesize raw user research (interviews, surveys, tickets) into themed findings and decision-ready briefs.

When should I use Research Summarizer?

Research Summarizer fits situations like: synthesizing user interviews; building a findings brief; communicating research to stakeholders.

How do I install Research Summarizer in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill research-summarizer -a claude-code`. Or copy the skill folder (product-team/research-summarizer in borghei/Claude-Skills) into .claude/skills/research-summarizer in your project. Claude Code loads it when a task matches its description.

How do I install Research Summarizer in Codex?

Run `npx skills add borghei/Claude-Skills --skill research-summarizer -a codex`. Or copy the skill folder (product-team/research-summarizer in borghei/Claude-Skills) into .agents/skills/research-summarizer in your project. Codex loads it when a task matches its description.

Can I use Research Summarizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill research-summarizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research-summarizer, .gemini/skills/research-summarizer, .github/skills/research-summarizer and .opencode/skills/research-summarizer in your project.

What does Research Summarizer need to run?

Going by SKILL.md and its folder, Research Summarizer needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Research Summarizer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Research Summarizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Research Summarizer use?

Research Summarizer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Research Summarizer use?

About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5k tokens, read only when the agent opens those files.

What are the alternatives to Research Summarizer?

Skills that share tags, products or a category with Research Summarizer: User Research Cookiy (cookiy-ai/user-research-skill, 1.6k stars), Fable Domain (Sahir619/fable-method, 2.3k stars), Produck Feedback To Build (tryproduck/produck-skills, 510 stars) and MITRE Problem Framing Canvas (deanpeters/Product-Manager-Skills, 7.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Research Summarizer?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 881 GitHub stars. The repository holds 349 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.