user-research
Skill152 starsChanged 8 months agoMITView on GitHub
Key Findings (ranked by evidence strength) For each: - Finding (1 sentence) - Evidence (how many participants, quotes) - Confidence (High/Medium/Low) - Product implication Themes | Theme | Frequency | Representative Quote | Implication | Surprises - What contradicted our assumptions Gaps - Questions not answered, segments not covered Recommended Actions - Prioritized list with supporting evidence Bad synthesis (no evidence, no confidence levels): Good synthesis:
17 other projects use a version of this.
Adopted by
Most recently taken up by
- nisarth/claude-skills-storetheir own version, first seen Nov 8, 2026
- AxlZero21/Skillstheir own version, first seen Nov 8, 2026
- toribryan/design-skillstheir own version, first seen Oct 25, 2026
- strikersam/autonomous-ai-agencytheir own version, first seen Oct 19, 2026
- sankar2code/Claude-Skill_SubAgent-Builder-Collectionstheir own version, first seen Oct 13, 2026
- shayan-m81/layerwrighttheir own version, first seen Oct 13, 2026
Used with
Projects with it also use git-workflow and frontend-design.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
Microsoft AzureSkillgit-workflow
Squad branching model: dev-first workflow with insiders preview channel
Also in 3 projects that use this
BlockSkillfrontend-design
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
Also in 4 projects that use this
Other versions17 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 17
- anthropics/knowledge-work-plugins★ 35ksame name
- nimadorostkar/Claude-Skills-collection★ 33same name
- strikersam/autonomous-ai-agency★ 11same name
- shayan-m81/layerwright★ 8same name
- kevinaimonster/skill-hub★ 4same name
- sankar2code/Claude-Skill_SubAgent-Builder-Collections★ 2same name
- AxlZero21/Skills★ 1same name
- huangqianqian120/skillslibrary★ 1same name
- NinjaSln-labs/agent-skills★ 1same name
- nisarth/claude-skills-store★ 1same name
7 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- competitive-analysisSkill
- launch-checklistSkill
- metrics-definerSkill
- prd-writerSkill
- sprint-plannerSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 10 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 76
Use actual quotes, never paraphrases.
- Distinctive: line 79
Every finding requires a confidence level AND a product implication.
“Common” is measured: kept by at least 6 in 10 of the 10 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
User Research Synthesizer
Trigger
Activate on "synthesize research", "analyze interviews", "research findings", "interview synthesis".
Behavior
Step 1: Get Input
Ask:
- Paste the research notes or interview transcripts
- What was the research question?
- How many participants?
Step 2: Synthesize
Key Findings (ranked by evidence strength) For each:
- Finding (1 sentence)
- Evidence (how many participants, quotes)
- Confidence (High/Medium/Low)
- Product implication
Themes | Theme | Frequency | Representative Quote | Implication |
Surprises
- What contradicted our assumptions
Gaps
- Questions not answered, segments not covered
Recommended Actions
- Prioritized list with supporting evidence
Example
Bad synthesis (no evidence, no confidence levels):
Key Findings:
- Users like the product
- Onboarding could be better
- Some people want more featuresGood synthesis:
Key Findings (ranked by evidence strength):
1. Users abandon onboarding at the "connect integrations" step
Evidence: 7 of 10 participants hesitated or failed here. 4 said
variants of "I don't want to give access to my data yet."
Confidence: HIGH
Implication: Move integrations to post-activation. Let users see
value before asking for trust.
2. Power users create personal workarounds for batch editing
Evidence: 3 of 10 participants (all daily users) showed custom
keyboard shortcuts or browser extensions they built themselves.
Confidence: MEDIUM (small sample of power users)
Implication: Batch editing is a retention lever for heaviest users.
Worth exploring, but validate with usage data first.
Surprises:
- 6 of 10 participants didn't know the search feature existed. It's
behind a keyboard shortcut (Cmd+K) with no visible UI entry point.
This contradicts our assumption that search is well-adopted.
Gaps:
- No participants from enterprise segment (>500 employees). Findings
may not generalize to that tier.
- Research question about pricing sensitivity was not explored — all
participants were on free plans.Rules
- Use actual quotes, never paraphrases. Quotes are evidence. Paraphrases are interpretation.
- Findings from 1-2 participants are signals, not conclusions. Label them accordingly.
- Distinguish said vs. did. Behavior always outranks stated preference.
- Every finding requires a confidence level AND a product implication. A finding without "so what" is trivia.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

