Blindspots logo

Blindspots

Community
Tranz007
blindspots

Find important users, contexts, constraints, dependencies, consequences, and conditions that a design discussion has not considered at all. Use when a designer asks "what am I missing?", wants edge perspectives beyond UI states, or needs a pre-review scan for overlooked experience risks.

Overview

PublisherTranz007
Repositoryux-skills
Skill nameblindspots
Stars
48
Forks
4
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 Tranz007 on GitHub. Read the source before you install it.

Installation

Install the Blindspots 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.
    https://github.com/Tranz007/ux-skills/tree/main/skills/blindspots
  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/Tranz007/ux-skills.git /tmp/ux-skills
mkdir -p .claude/skills
cp -r /tmp/ux-skills/skills/blindspots .claude/skills/blindspots
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blindspots 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 Blindspots 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 Blindspots 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.

Blindspots

Look outside the frame the team is already using.

Always

  • Context — inspect what is already known before asking the user to repeat it. Use .ux/INTENT.md when product purpose or outcome can change the answer, and load only the additional project context the task needs.
  • User — ground the work in the people affected, their goal, task, context, and available evidence. Do not invent user needs, behaviors, or personas.
  • Evidence — keep known, inferred, assumed, unknown, and conflicted information distinct when the difference matters.
  • System — prefer established product language, components, patterns, and rules before inventing new ones.
  • Clear — lead with the useful point, use the minimum structure needed, and remove generic AI filler.
  • Trust — never invent evidence, requirements, rationale, implementation status, or compliance.
  • Outcome — for substantial multi-step work, keep intent active, use a small .ux/STATE.md only when continuity needs it, prioritize the highest-impact unresolved gap before polishing, and verify the actual experience against intent before declaring completion.

Do not recite these rules to the user unless one of them materially affects the answer.

Do not introduce research questions, personas, or discovery work when the user and task are already clear or the missing information would not materially change the work.

Inspect first

Understand the current problem, intended users, evidence, system, and proposed direction. Do not produce a generic accessibility-and-edge-cases checklist detached from the product.

Search for omitted dimensions

Consider only dimensions relevant to the work, including:

  • people with different abilities, familiarity, language, age, roles, permissions, or support needs;
  • real-world context such as interruption, urgency, shared devices, travel, connectivity, time zones, or environmental constraints;
  • policy, privacy, trust, consent, safety, or financial consequences;
  • upstream and downstream services;
  • multi-user or organizational behavior;
  • lifecycle effects after the happy path ends;
  • unusual but plausible data, inventory, account, or entitlement conditions;
  • support and recovery burden;
  • conflicts with established product or design-system conventions.

Prioritize

Do not dump every imaginable edge case. Rank blind spots by likelihood, consequence, and cost of discovering them late.

Separate a true blind spot from a state already handled in the design.

Output

Lead with the few omissions most likely to change the design. Explain why each matters and what would resolve it.

Guardrails

Do not invent vulnerable populations, regulations, or product requirements. Raise them as questions when context does not establish them.

Examples

  • "What are we not thinking about?"
  • "Blindspots on this booking flow?"
  • "Who or what does this design accidentally ignore?"

Frequently asked questions

What does the Blindspots AI skill do?

Find important users, contexts, constraints, dependencies, consequences, and conditions that a design discussion has not considered at all. Use when a designer asks "what am I missing?", wants edge perspectives beyond UI states, or needs a pre-review scan for overlooked experience risks.

Why use Blindspots on TypingMind?

Because you install it once and use it with any model. Blindspots 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 Blindspots in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Tranz007/ux-skills/tree/main/skills/blindspots. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Blindspots?

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 Blindspots?

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

Is the Blindspots AI skill free?

Yes. It is published on GitHub by Tranz007 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇