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Reality Skill

Community
lcsxu
reality-skill

Converts demo UIs into believable, end-to-end product workflows. Use when building, reviewing, or finishing apps, dashboards, CRUD flows, forms, onboarding, checkout, settings, auth-like surfaces, data tools, AI tools, integrations, or any UI that must behave like a real app instead of a static template. Composes after no-slop/perfect-design to make the interface operational, truthful, stateful, validated, persistent, and verifiable.

Overview

Publisherlcsxu
Repositoryzero-slop
Skill namereality-skill
Stars
13
Forks
1
Bundled files
15
LicenseApache-2.0
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.

  • 15 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lcsxu on GitHub. Read the source before you install it.

Installation

Install the Reality Skill 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/lcsxu/zero-slop/tree/main/skills/reality-skill
  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/lcsxu/zero-slop.git /tmp/zero-slop
mkdir -p .claude/skills
cp -r /tmp/zero-slop/skills/reality-skill .claude/skills/reality-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reality Skill 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 Reality Skill 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 Reality Skill 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.

Reality Skill

Reality Skill turns attractive screens into usable product workflows. It does not ask "does this look like an app?" It asks "can the intended user complete the job, recover from problems, and trust what the UI claims?"

Use this skill aggressively whenever a request says app, tool, dashboard, portal, editor, workflow, CRUD, form, checkout, onboarding, settings, upload, export, invite, generate, sync, connect, save, publish, deploy, analyze, or review.

Command Interface

Use references/command-interface.md.

Primary commands:

bash
reality --audit [target]
reality --fix [target]
reality --build [brief-or-target]
reality --harden [target]
reality --judge [target]
reality --verify [target]
reality -e --audit [target]

Short flags:

  • -a, --audit: find dead actions, fake data, missing states, weak workflows.
  • -f, --fix: make the smallest workflow-real implementation.
  • -b, --build: build the real workflow model while creating a new app surface.
  • --harden: add durability, recovery, validation, accessibility, and edge-state coverage.
  • -j, --judge: score the app against the reality gates.
  • --verify: prove the primary workflow by tests, build checks, and browser checks when possible.
  • -e, --economy: skip live judge agents and use deterministic checks plus local review.

Default mode:

  • Existing UI target: --fix.
  • New app brief: --build.
  • Review-only request: --audit.

First Action

Load steps/step-00-command-router.md.

Step 00 parses the command, discovers project shape, loads only the needed references, and routes to audit, fix, build, harden, judge, or verify.

Core References

Load only what the task needs:

  • references/command-interface.md: grammar, mode precedence, output contracts.
  • references/reality-gates.md: the twelve gates that define "not demoware."
  • references/app-archetypes.md: workflow expectations for dashboards, CRUD, editors, checkout, AI tools, integrations, onboarding, settings, and auth-like surfaces.
  • references/implementation-playbook.md: concrete patterns for data models, actions, state machines, validation, persistence, optimistic updates, undo, and honest mocks.
  • references/truth-boundaries.md: how to represent sample data, unavailable services, auth, payments, exports, AI, analytics, and integrations without lying.
  • references/verification-playbook.md: build, test, browser, persistence, accessibility, responsive, and smoke-test proof.
  • references/judge-system.md: reality judge protocol and scoring gates.

Non-Negotiables

  • Do not stop at a static UI when the user asked for an app, dashboard, tool, form, editor, portal, or workflow.
  • Do not ship dead buttons, href="#", placeholder handlers, console-only submits, fake success toasts, decorative charts, fake metrics, fake users, fake payments, fake auth, fake AI, fake integrations, or fake exports.
  • Do not claim production capability unless the code actually connects to the production capability with real credentials/configuration already present in the project.
  • Prefer real project services, schemas, stores, routes, and design-system primitives over invented abstractions.
  • When real backend/service access is absent, create an honest local boundary: local storage, in-memory state, fixture mode, disabled integration, explicit sample data, or documented adapter seam in code and UI.
  • A primary workflow must include entry, intent, input, validation, submit/action, progress, success, failure, retry/recovery, persistence, and return/edit/delete paths where relevant.
  • User-created or edited data must survive the immediate workflow. Use server/database/API first, existing store second, local storage/URL state third, and clearly scoped memory only for throwaway prototypes.
  • Every primary action must either work, navigate to a real next step, or be disabled with a specific reason.
  • Forms must preserve user input after errors and show actionable inline messages near the relevant fields.
  • Data shown as real must be real or user-created. Sample data must be labeled as sample and must not pretend to be customers, revenue, uptime, compliance, security, or live analytics.
  • Verification is part of the implementation, not an afterthought. For frontend work, run the app when feasible and inspect the workflow in a browser.
  • Compose with no-slop and perfect-design when available: those skills improve design quality; Reality Skill makes the result operational and truthful.

Required State

Maintain this state throughout the workflow:

text
command:
target:
mode:
economy_mode:
project_type:
primary_user:
primary_job:
workflow_contract:
data_contract:
truth_boundaries:
reality_score_before:
blocking_gates:
files_changed:
implementation_strategy:
verification:
reality_score_after:
remaining_fake_boundaries:
ship_decision:

Reality Contract

Before editing, write or infer a compact contract:

text
User:
Job:
Starting point:
Completion condition:
Data needed:
Actions needed:
Persistence needed:
Unavailable real services:
Honest mock/local boundary:
States that must exist:
Verification path:

If the contract is unclear, inspect the app and infer the most likely primary job. Ask the user only when multiple high-impact jobs conflict and a wrong choice would waste the implementation.

Local CLI Helper

For deterministic prechecks:

bash
python skills/reality-skill/scripts/reality_cli.py --audit <target>
python skills/reality-skill/scripts/reality_cli.py --json --audit <target>
python skills/reality-skill/scripts/reality_cli.py --strict --audit <target>

The CLI catches common demoware signals. It is not a replacement for reading code, implementing the workflow, browser verification, or the reality judge.

Done Means

  • The primary job can be completed end-to-end.
  • Primary actions are real or honestly unavailable.
  • User input is validated and preserved.
  • Relevant loading, empty, error, success, disabled, long-content, and small-screen states exist.
  • User-created data persists through the expected scope.
  • Navigation includes cancel, back, retry, return, edit, delete/recover paths where relevant.
  • Fake capabilities are implemented, disabled, or clearly labeled as local/sample/mock boundaries.
  • Verification ran, or the exact blocker is stated with the unverified risk.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Reality Skill AI skill do?

Converts demo UIs into believable, end-to-end product workflows. Use when building, reviewing, or finishing apps, dashboards, CRUD flows, forms, onboarding, checkout, settings, auth-like surfaces, data tools, AI tools, integrations, or any UI that must behave like a real app instead of a static template. Composes after no-slop/perfect-design to make the interface operational, truthful, stateful, validated, persistent, and verifiable.

Why use Reality Skill on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lcsxu/zero-slop/tree/main/skills/reality-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Reality Skill?

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 Reality Skill?

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

Is the Reality Skill AI skill free?

Yes. It is published on GitHub by lcsxu under the Apache-2.0 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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