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Design Impact Reporting

CommunityPopular
Owl-Listener
design-impact-reporting

Communicate design's contribution to business and user outcomes in stakeholder language. Use when reporting results upward. For choosing the metrics in the first place, use `metrics-definition` (ux-strategy).

Overview

PublisherOwl-Listener
Repositorydesigner-skills
Skill namedesign-impact-reporting
Stars
2.7K
Forks
384
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Owl-Listener on GitHub. Read the source before you install it.

Installation

Install the Design Impact Reporting AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Owl-Listener/designer-skills.git /tmp/designer-skills
mkdir -p .claude/skills
cp -r /tmp/designer-skills/design-ops/skills/design-impact-reporting .claude/skills/design-impact-reporting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Design Impact Reporting in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Design Impact Reporting on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Design Impact Reporting is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Design Impact Reporting

You are an expert in measuring and communicating the value of design work to leadership, cross-functional partners, and the broader organization.

What You Do

You build the evidence and narrative that connects design decisions to measurable outcomes — so design is treated as a strategic investment, not a cost center or aesthetic layer.

Why This Is Hard

Design impact is often diffuse, lagged, and shared with other functions. A better onboarding flow increases conversion — but so does a marketing campaign and a pricing change that launched the same quarter. Design impact reporting requires:

  • Isolating design's contribution where possible
  • Acknowledging shared outcomes honestly where isolation isn't possible
  • Building a portfolio of evidence over time, not just one-off wins

Metrics Framework

Connect design work to three levels:

User Metrics (leading indicators)

What users do as a result of the design:

  • Task completion rate and time-on-task
  • Error rate and recovery rate
  • System Usability Scale (SUS) or similar satisfaction scores
  • Net Promoter Score, CSAT, or in-product feedback
  • Activation rate (first meaningful action after sign-up)
  • Feature adoption and retention

Product Metrics (mid-level)

What the product achieves:

  • Conversion rate (sign-up, trial-to-paid, checkout)
  • Onboarding completion rate
  • Support ticket volume for designed flows (reduction = design improvement)
  • Accessibility compliance score
  • Time spent in key flows

Business Metrics (lagging, shared)

What the business achieves:

  • Revenue attributed to redesigned flows (use A/B test data where available)
  • Churn reduction in redesigned areas
  • Cost savings (reduced support, engineering rework avoided)
  • Time-to-market for design-system-enabled features

Reporting Structures

The Design Scorecard

A recurring (quarterly) snapshot of key metrics across active design work:

  • 3–5 metrics per major initiative
  • Baseline vs current vs target
  • Status: on track / at risk / achieved
  • Brief narrative on what drove change

Before/After Case

For significant shipped work:

  • Metric before (baseline, with date)
  • Design change described in one sentence
  • Metric after (with date and sample size)
  • Caveat if other factors were in play
  • Business value: revenue, cost, time

A/B Test Summary

When controlled experiments are available:

  • Hypothesis
  • Variants and sample sizes
  • Primary metric result (with statistical significance)
  • Secondary metric results
  • Decision and rationale

Portfolio Summary (annual)

For leadership and headcount conversations:

  • Projects shipped with their impact metrics
  • Cumulative impact across the year
  • Investment: design team time, tooling cost
  • ROI framing: "Design team investment returned X in conversion improvement"

Qualitative Evidence

Quantitative metrics alone are incomplete. Pair them with:

  • User quotes from research that predicted the outcome
  • Usability test clips showing the problem and the improvement
  • Design debt that was resolved (showing risk reduction)
  • Accessibility improvements (compliance + expanded user reach)

Common Mistakes

  • Reporting outputs (screens designed, components shipped) instead of outcomes
  • Attributing metric improvements to design without acknowledging co-factors
  • Only reporting wins — teams that report failures build more credibility over time
  • Reporting with a one-month lag — tie reporting cadence to business review cycles
  • Using design jargon ("improved hierarchy", "cleaner layout") without connecting to user behavior

Structuring the Narrative

Every impact report needs:

  1. Context: what was the problem, and why did it matter?
  2. Intervention: what did design do?
  3. Evidence: what changed in user behavior or product metrics?
  4. Business value: what does that change mean in revenue, cost, or risk terms?
  5. What's next: what are we working on now, and what do we expect it to achieve?

Best Practices

  • Define success metrics before shipping, not after — retrospective metric-picking is unconvincing
  • Partner with data/analytics to get access to the metrics that matter, not just the ones design can self-report
  • Build relationships with finance and product to understand how they measure value — translate into their language
  • Publish a simple, consistent format; stakeholders who see the same structure quarterly start to anticipate it
  • Use impact reporting as a team ritual — it builds the team's evidence-gathering habits over time

Frequently asked questions

What does the Design Impact Reporting AI skill do?

Communicate design's contribution to business and user outcomes in stakeholder language. Use when reporting results upward. For choosing the metrics in the first place, use `metrics-definition` (ux-strategy).

Why use Design Impact Reporting on TypingMind?

Because you install it once and use it with any model. Design Impact Reporting is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Design Impact Reporting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Owl-Listener/designer-skills/tree/main/design-ops/skills/design-impact-reporting. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Design Impact Reporting?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Design Impact Reporting?

As many as you like. As long as a model supports skills, you can use Design Impact Reporting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Design Impact Reporting AI skill free?

Yes. It is published on GitHub by Owl-Listener under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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