nexu-io/open-design

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.

86Collecting
See how to use itView GitHub source
npx skills add https://github.com/nexu-io/open-design --skill "skills/research-decision-room"
Automated source guideDesignDeep source

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Source-grounded design guide: research-decision-room

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 vi…

npx skills add https://github.com/nexu-io/open-design --skill "skills/research-decision-room"
Check the pinned source

The pinned source contains enough sections and task detail for a source-grounded deep guide; automated content is still not an independent test.

672 source words · 10 usable sections

Best fit

  • Interview notes, usability-test observations, support tickets, sales call notes,
  • A decision that needs evidence: "Should we build X?", "Which onboarding path
  • A need to share findings with stakeholders who will not read a long research

Design process

Read research-decision-room through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

Workflow

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.

SKILL.md · Workflow
Decision question.Audience or segment.Time horizon.
02

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.

SKILL.md · Step 1 - Establish the decision frame
Decision question.Audience or segment.Time horizon.
03

Step 2 - Build the evidence ledger

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

SKILL.md · Step 2 - Build the evidence ledger
id: short stable id, such as I-03, T-14, M-02.sourcetype: interview, usability, support, survey, analytics, sales, fieldsegment: user type or "unknown".
04

Step 3 - Synthesize themes and tensions

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

SKILL.md · Step 3 - Synthesize themes and tensions
Name the theme in plain human language.List the evidence ids that support it.Explain the behavior behind it, not just the UI complaint.
05

Step 4 - Score opportunities

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

SKILL.md · Step 4 - Score opportunities
Evidence strength.User pain.Business leverage.

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Source-grounded prompt

Use for a design task while explicitly checking the source sections.

Use research-decision-room for this design task: [task]. Inputs and constraints: [details]. Work through these pinned SKILL.md sections: “Workflow”, “Step 1 - Establish the decision frame”, “Step 2 - Build the evidence ledger”, “Step 3 - Synthesize themes and tensions”, “Step 4 - Score opportunities”. Cite the concrete requirements that shape each step, do not invent capabilities absent from the source, and verify the result against: [acceptance criteria].

Design checklist

Verify each item before delivery

The source section “Workflow” has been checked.

The source section “Step 1 - Establish the decision frame” has been checked.

The source section “Step 2 - Build the evidence ledger” has been checked.

The source section “Step 3 - Synthesize themes and tensions” has been checked.

Choose a different workflow

When another Skill is the better fit

design-review

Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.

A separate implementation from event4u-app/agent-config; compare its source, maintenance signals, and permission requirements.

Open source detail

neuropixels-analysis

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.

Open source detail

scientific-brainstorming

Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.

A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.

Open source detail

FAQ

What does the research-decision-room source document cover?

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 vi…

How do I install research-decision-room?

The source record exposes this install command: npx skills add https://github.com/nexu-io/open-design --skill "skills/research-decision-room". Inspect the command and pinned source before running it.

Repository stars
82,073
Repository forks
9,485
Quality
86/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

86/100
Documentation28/30
Specificity21/25
Maintenance20/20
Trust signals17/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

design-review by event4u-app

Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.

neuropixels-analysis by k-dense-ai

Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

scientific-brainstorming by k-dense-ai

Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.

citation-management by k-dense-ai

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

doc-and-modernize by github

Two related workflows for a locally-cloned codebase, in one skill. Documentation mode produces a single, comprehensive, verifiable architecture document primarily by reading files on disk (local-first) — use it whenever the user wants to understand, map, document, research, or onboard onto a codebase ("research this repo", "write up the architecture", "do an architecture deep dive", "document how this codebase works", "map the system design", "create an onboarding doc"). Modernization mode gener

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 4 min

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>.

Skill path
skills/research-decision-room/SKILL.md
Commit SHA
89d6d4ef21ba
Repository license
Apache-2.0
Data collected