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research-decision-room

Turn interviews, support tickets, surveys, and product context into a single HTML workspace for decisions. Link findings to evidence, compare improvement options, show uncertainty, and outline experiments that help teams choose what to try next.

OriginalShow the original 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.

AI rating100/ 100See how to install

Good for

  • Combining interviews and support tickets into findings
  • Comparing onboarding improvement options
  • Sharing evidence and uncertainty with stakeholders
  • Planning experiments and success measures

Overview

What it does

Organizes interview notes, support tickets, surveys, and usage notes into a single-page HTML workspace for product or design decisions. It includes an evidence ledger with source IDs, four to six themes, confidence levels, a comparison of three to five improvement options, a short decision memo, and experiments to try next. It can also support evidence filtering and highlighting of related IDs.

When it helps

Use it when your team needs to discuss what to improve based on scattered customer feedback. Stakeholders who lack time for a long research report can review recommendations alongside conflicting views and gaps in the available information.

Example requests

  • “Organize these interviews and support tickets, then compare options for improving initial setup.”
  • “Group the reasons users drop off into themes and suggest experiments and metrics to measure next.”

Notes

You must provide research materials. Quotes, participant counts, dates, revenue impact, and metrics are not invented; missing numbers are marked as not provided. If the decision scope is unspecified, the inferred scope is labeled accordingly. The output is a self-contained index.html; a framework dependency is not required.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt identifies the research inputs and the purpose of producing HTML for product or design decisions.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt specifies seven workflow steps, evidence-ledger fields, scoring scales, and the structure of index.html.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodIt explicitly requires file writing and specifies a single self-contained index.html as the output.
Easy setupDoes it need few tools or API keys, and are they documented?
AGoodIt lists required input and file-writing capability and explicitly states that no framework dependency is required.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 20, 2026. Treat it as a guide and read the original before installing.

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Files included (4)

  • SKILL.md6.1 KB
  • example.html26.9 KB
  • references/checklist.md2.1 KB
  • references/evidence-model.md3.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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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