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Research Decision Room

nexu-io/open-design

  • 1.2k installs
  • 83.7k repo stars
  • Updated August 5, 2026
  • nexu-io/open-design

Research Decision Room is a Claude Code and Cursor skill that converts scattered user interviews, support tickets, surveys, and product notes into one HTML decision room with evidence, themes, opportunities, and experime

About

Research Decision Room is an open-design agent skill that synthesizes messy qualitative inputs—user interviews, support tickets, surveys, and product context—into a single evidence-backed HTML artifact. The output bundles an evidence ledger, theme map, confidence heatmap, opportunity matrix, decision memo, and experiment queue so teams move from signals to product or design choices without fabricating certainty. Trigger phrases include research synthesis dashboard, opportunity solution tree, and usability findings dashboard. Product managers, designers, and engineer-researchers invoke it when qualitative research is spread across docs and needs one navigable decision surface inside Cursor or Claude Code.

  • Converts qualitative research into a single self-contained HTML decision room
  • Produces an evidence ledger, theme map, confidence heatmap, opportunity matrix, decision memo and experiment queue
  • Uses only the provided research_material and optional decision_scope as input
  • Applies typography, color, accessibility-baseline and anti-ai-slop design rules
  • Outputs index.html that can be opened instantly in any browser

Research Decision Room by the numbers

  • 1,249 all-time installs (skills.sh)
  • +80 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #418 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/nexu-io/open-design --skill research-decision-room

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Listed on Skillselion
Installs1.2k
repo stars★ 83.7k
Last updatedAugust 5, 2026
Repositorynexu-io/open-design ↗

How do you synthesize user research into product decisions?

Turn scattered user interviews, support tickets, survey data and product notes into one clear HTML decision room with evidence, themes, opportunities and next experimen

Who is it for?

Product managers, UX researchers, and designers who need evidence-backed synthesis from interviews and tickets into a single HTML decision artifact inside a coding agent.

Skip if: Developers drafting academic technical papers or implementing Spring Boot microservices where research-paper-writing or java-spring-development skills are the better fit.

When should I use this skill?

A developer or PM asks for a research synthesis dashboard, evidence-backed product decision, opportunity solution tree, or interview synthesis HTML artifact.

What you get

HTML decision room with evidence ledger, theme map, confidence heatmap, opportunity matrix, decision memo, and experiment queue.

  • html decision room
  • experiment queue
  • opportunity matrix

By the numbers

  • Output bundles 6 decision-room components
  • Defines 8 agent trigger phrases for research synthesis

Files

SKILL.mdMarkdownGitHub ↗

Research Decision Room Skill

Create a single-page HTML decision artifact that helps a product or design team turn messy evidence into a clear next move. The output is not a decorative research deck. It is a working room for debate: evidence, themes, confidence, tradeoffs, and recommended experiments stay visible together.

Resource map

research-decision-room/
├── SKILL.md
├── example.html
└── references/
    ├── checklist.md
    └── evidence-model.md

Read references/evidence-model.md before synthesis and run references/checklist.md before emitting the artifact.

When to use this skill

Use this skill when the user has any mix of:

  • Interview notes, usability-test observations, support tickets, sales call notes,

app-store reviews, NPS comments, survey open text, analytics snippets, or product-decision context.

  • A decision that needs evidence: "Should we build X?", "Which onboarding path

should we try?", "Why are users dropping off?", "What do customers actually mean by slow?"

  • A need to share findings with stakeholders who will not read a long research

report.

Do not use it for pure visual inspiration, campaign ideation, or brand moodboards.

Workflow

Step 1 - Establish the decision frame

Identify the decision scope from the user's prompt. If the user did not give a decision, derive one from the evidence and label it as inferred.

Write a short frame with:

  • Decision question.
  • Audience or segment.
  • Time horizon.
  • Known constraints.
  • What this artifact will not decide.

If key context is missing and the task is not blocked, proceed with labelled assumptions instead of asking a broad question.

Step 2 - Build the evidence ledger

Normalize every useful signal into ledger rows using the model in references/evidence-model.md.

Each ledger row must include:

  • id: short stable id, such as I-03, T-14, M-02.
  • source_type: interview, usability, support, survey, analytics, sales, field

note, or stakeholder.

  • segment: user type or "unknown".
  • signal: one-sentence observation.
  • quote_or_metric: direct quote, metric, or "not provided".
  • strength: strong, medium, or weak.
  • limitations: why this evidence may be biased or incomplete.

Never invent quotes, participant counts, dates, revenue impact, or metrics. If the user did not provide a number, use "not provided" and explain what evidence would increase confidence.

Step 3 - Synthesize themes and tensions

Cluster evidence into 4 to 6 themes. For each theme:

  • Name the theme in plain human language.
  • List the evidence ids that support it.
  • Explain the behavior behind it, not just the UI complaint.
  • Mark confidence as high, medium, or low.
  • Note contradictions or segment differences.

Prefer verbs over nouns: "Teams abandon setup when the first blank state asks for too much" is better than "Onboarding problem".

Step 4 - Score opportunities

Create an opportunity matrix with 3 to 5 options. Score each option on a 1 to 5 scale:

  • Evidence strength.
  • User pain.
  • Business leverage.
  • Implementation risk, where 5 means low risk and 1 means high risk.

Show the total score, but do not let the score replace judgment. Add one sentence on why the top recommendation wins.

Step 5 - Draft the decision memo

Write a decision memo with:

1. Recommended move. 2. Why now. 3. What evidence supports it. 4. What could be wrong. 5. What to measure next. 6. Reversible next step.

Keep the memo short enough to read in under one minute.

Step 6 - Create the HTML artifact

Produce a self-contained index.html. Use the active DESIGN.md for typography, spacing, color roles, and component tone, but keep the information architecture stable:

1. Header with decision question, confidence, and last-updated label. 2. Executive readout with recommendation, risk, and next experiment. 3. Evidence ledger with filter chips. 4. Theme map with evidence ids and confidence. 5. Opportunity matrix. 6. Decision memo. 7. Experiment queue with owner, metric, and success threshold. 8. Assumptions and limitations.

The artifact should be interactive but durable. Simple vanilla JavaScript is allowed for filtering evidence, switching views, or highlighting related ids. No framework dependency is required.

Step 7 - Self-check and emit

Run the checklist. Then emit one concise orientation sentence and one HTML artifact:

<artifact identifier="research-decision-room" type="text/html" title="Research Decision Room">
<!doctype html>
<html>...</html>
</artifact>

Nothing after the closing </artifact>.

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How it compares

Choose Research Decision Room for qualitative product synthesis HTML; pick academic writing skills when the deliverable is a technical paper rather than a product decision dashboard.

FAQ

What artifacts does Research Decision Room generate?

Research Decision Room outputs one HTML decision room containing an evidence ledger, theme map, confidence heatmap, opportunity matrix, decision memo, and experiment queue synthesized from interviews, tickets, surveys, and product notes.

What inputs does Research Decision Room accept?

Research Decision Room ingests messy user research notes, interviews, support tickets, surveys, and product context. It emphasizes evidence-backed synthesis without overstating confidence in qualitative findings.

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