research-decision-room — Claude Code skill: setup and guide
Turns messy interview notes, support tickets, and surveys into a single interactive HTML decision room without fabricating certainty.
npx skills add nexu-io/open-design --skill research-decision-room -a claude-codeWhat research-decision-room does
The skill converts qualitative research inputs, such as interview notes, support tickets, survey responses, and analytics context, into a standalone index.html file. The output contains an evidence ledger with filtering chips, a theme map, an opportunity matrix, a decision memo, and a prioritized experiment queue with metrics.
Synthesis follows a strict staged flow: defining the decision scope, logging evidence rows with stable IDs, grouping findings into 4 to 6 themes, and scoring opportunities from 1 to 5. The workflow explicitly forbids fabricating quotes, dates, participant counts, or metrics; unverified fields are marked as "not provided". For execution, the agent requires the file_write capability to write the final HTML artifact.
What it helps with
- normalize qualitative interview notes and support tickets into an evidence ledger
- cluster user feedback into actionable themes with confidence ratings
- evaluate product opportunities against implementation risk and user pain
- define an experiment queue with assigned metrics and success thresholds
Example prompts for the agent
- Synthesize 8 interview notes and 24 support tickets into a research decision room on onboarding improvements.
- Run a qualitative research synthesis on these customer feedback logs and emit an index.html report.
- Build an evidence-backed decision room to decide between inline tips and an onboarding checklist.
How to install research-decision-room
1Skills CLI (Vercel)
npx skills add nexu-io/open-design --skill research-decision-room -a claude-code2Manually: put the files in place
~/.claude/skills/research-decision-room/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/research-decision-room/
1Skills CLI (Vercel)
npx skills add nexu-io/open-design --skill research-decision-room -a codex2Manually: put the files in place
.agents/skills/research-decision-room/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add nexu-io/open-design --skill research-decision-room -a cursor2Manually: put the files in place
.cursor/skills/research-decision-room/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install nexu-io/open-design research-decision-room2Manually: put the files in place
.github/skills/research-decision-room/SKILL.md1Skills CLI (Vercel)
npx skills add nexu-io/open-design --skill research-decision-room -a gemini-cli2Platform CLI
gemini skills install https://github.com/nexu-io/open-design --path skills/research-decision-roomManually: put the files in place
.devin/skills/research-decision-room/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add nexu-io/open-design --skill research-decision-room -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set106
The whole set →Similar
Questions about research-decision-room
How to install research-decision-room in Claude Code?
Run npx skills add nexu-io/open-design --skill research-decision-room -a claude-code. This installs the skill directly into your Claude Code environment.
What does research-decision-room do?
It turns qualitative research notes and feedback into a self-contained HTML artifact featuring an evidence ledger, theme map, decision memo, and experiment queue.
What agent capabilities are required to use this skill?
The agent must have the file_write capability enabled to generate and save the resulting index.html artifact.
Source text
SKILL.md · Apache-2.0
---
name: research-decision-room
description: |
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.
triggers:
- "research decision room"
- "user research synthesis"
- "research synthesis dashboard"
- "evidence-backed product decision"
- "interview synthesis"
- "opportunity solution tree"
- "usability findings dashboard"
- "qualitative research board"
od:
mode: prototype
platform: desktop
scenario: research
preview:
type: html
entry: index.html
reload: debounce-100
design_system:
requires: true
sections: [color, typography, layout, components]
craft:
requires: [typography, color, accessibility-baseline, anti-ai-slop]
inputs:
- name: research_material
type: string
required: true
description: "Interview notes, tickets, survey excerpts, analytics notes, or a product decision brief"
- name: decision_scope
type: string
required: false
description: "The product/design decision the team needs to make"
outputs:
primary: index.html
example_prompt: "Synthesize 8 interview notes, 24 support tickets, and recent activation metrics into a research decision room for whether a project-management app should add an onboarding checklist or contextual inline tips."
capabilities_required:
- file_write
---
# 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
```text
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:
```xml
<artifact identifier="research-decision-room" type="text/html" title="Research Decision Room">
<!doctype html>
<html>...</html>
</artifact>
```
Nothing after the closing `</artifact>`.
© nexu-io · Apache-2.0 · nexu-io/open-design