Research decision room
Turn messy user research notes, interviews, support tickets, surveys, and product context into an evidence-backed decision room: a single HTML artifact with an evidence ledger, theme map, confidence heatmap, opportunity matrix, decision memo, and experiment queue. Use when teams need to move from qualitative signals to product or design decisions without fabricating certainty.From its SKILL.md
npx -y skills add nexu-io/open-design --skill research-decision-roomAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
6.1 KB, ~1.0k tokens by cl100k_base, as published. Nobody here has run it
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 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>.
What ships with it: 3 files
32.1 KB alongside SKILL.md
references/
- checklist.md2.1 KB
- evidence-model.md3.1 KB
- example.html26.9 KB
Gives 0 of the 12 instructions most research analysis skills give in ~1.0k tokens
Counted across 1,213 of the 2,113 authors here whose files we hold, read 2026-09-06
- Cite sources for every important claimin 47 of 1213, across 38 files
- Separate facts from inferences and recommendationsin 21 of 1213, across 12 files
- Write findings to a markdown filein 19 of 1213
- Label every insight with a confidence levelin 18 of 1213, across 8 files
- Read product marketing context before asking questionsin 18 of 1213, across 8 files
- Rank themes by frequency and intensityin 16 of 1213, across 6 files
- Establish research mode before proceedingin 16 of 1213, across 6 files
- Segment survey responses by customer tier or tenurein 16 of 1213, across 6 files
- Categorize support tickets before analyzingin 16 of 1213, across 6 files
- Weight research sources from the last twelve monthsin 16 of 1213, across 6 files
- Use at least five data points per segmentin 15 of 1213, across 5 files
- Extract verbatim quotes for all research findingsin 15 of 1213, across 5 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.