
Research Decision Room
FreeTransform messy research into actionable decisions.
Free · Opens the source repo
What Research Decision Room does
The Research Decision Room skill is designed to help product and design teams synthesize various forms of user research into a coherent, actionable format. By taking inputs such as interview notes, usability test observations, support tickets, and survey responses, this skill creates a single-page HTML artifact that serves as a decision-making tool rather than a traditional research report. This artifact includes an evidence ledger, theme map, confidence heatmap, opportunity matrix, decision memo, and experiment queue, all of which are essential for guiding teams through complex product decisions.
To utilize the skill, teams start by establishing a decision frame that outlines the scope and context of the decision at hand. This step is crucial as it guides the subsequent phases of evidence collection and synthesis. The evidence ledger is built by normalizing various signals into a structured format, ensuring that every piece of evidence is accounted for and assessed for its strength and limitations. This meticulous approach helps in identifying themes and tensions from the collected data, which can then be used to score potential opportunities based on user pain, business leverage, and implementation risk.
The final output is a well-organized HTML document that not only presents the findings but also facilitates ongoing discussions among stakeholders. The interactive nature of the document allows users to filter evidence and switch views, making it a practical tool for real-time decision-making. This skill is particularly beneficial for teams that need to make informed decisions quickly and effectively without getting bogged down by lengthy reports.
Overall, the Research Decision Room skill is an invaluable resource for teams looking to translate qualitative research into concrete actions, ensuring that all relevant evidence is visible and considered in the decision-making process.
When to use it
Use this skill when you have diverse user research data and need to make informed product or design decisions based on evidence.
When not to use it
This skill is not suitable for tasks focused solely on visual inspiration or creative ideation without a decision-making component.
What you can build with it
Synthesize User Feedback
A team collects feedback from various sources and uses this skill to create a structured decision document that highlights key themes and recommended actions.
Prioritize Product Features
Using the opportunity matrix, a product team evaluates different feature requests based on user pain and business impact, leading to informed prioritization.
Communicate Findings to Stakeholders
The HTML artifact generated by this skill allows teams to present their findings succinctly to stakeholders who may not read lengthy reports.
How to install Research Decision Room
View source1. Install with the skills CLI
npx skills add nexu-io/open-design/research-decision-room --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by nexu-ioResearch 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 asI-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:
- Recommended move.
- Why now.
- What evidence supports it.
- What could be wrong.
- What to measure next.
- 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:
- Header with decision question, confidence, and last-updated label.
- Executive readout with recommendation, risk, and next experiment.
- Evidence ledger with filter chips.
- Theme map with evidence ids and confidence.
- Opportunity matrix.
- Decision memo.
- Experiment queue with owner, metric, and success threshold.
- 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>.
Frequently asked questions about Research Decision Room
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