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Reality Verification

Community
tzachbon
reality-verification

This skill should be used when the user asks to "verify a fix", "reproduce failure", "diagnose issue", "check BEFORE/AFTER state", "VF task", "reality check", "check test quality", "mock-only tests", or needs guidance on verifying fixes by reproducing failures before and after implementation, or detecting mock-heavy test anti-patterns.

Overview

Publishertzachbon
Repositorysmart-ralph
Skill namereality-verification
Stars
559
Forks
50
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Reality Verification 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/tzachbon/smart-ralph/tree/main/plugins/ralph-specum/skills/reality-verification
  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/tzachbon/smart-ralph.git /tmp/smart-ralph
mkdir -p .claude/skills
cp -r /tmp/smart-ralph/plugins/ralph-specum/skills/reality-verification .claude/skills/reality-verification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reality Verification 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 Verification 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 Verification 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 Verification

For fix goals: reproduce the failure BEFORE work, verify resolution AFTER.

Goal Detection

Classify user goals to determine if diagnosis is needed. See references/goal-detection-patterns.md for detailed patterns.

Quick reference:

  • Fix indicators: fix, repair, resolve, debug, patch, broken, failing, error, bug
  • Add indicators: add, create, build, implement, new
  • Conflict resolution: If both present, treat as Fix

Command Mapping

Goal KeywordsReproduction Command
CI, pipelinegh run view --log-failed
test, testsproject test command
type, typescriptpnpm check-types or tsc --noEmit
lintpnpm lint
buildpnpm build
E2E, UIPlaywright MCP browser tools
API, endpointWebFetch tool

For E2E/deployment verification, use MCP tools (Playwright MCP browser tools for UI, WebFetch tool for APIs).

BEFORE/AFTER Documentation

BEFORE State (Diagnosis)

Document in .progress.md under ## Reality Check (BEFORE):

markdown
## Reality Check (BEFORE)

**Goal type**: Fix
**Reproduction command**: `pnpm test`
**Failure observed**: Yes
**Output**:

FAIL src/auth.test.ts Expected: 200 Received: 401

**Timestamp**: 2026-01-16T10:30:00Z

AFTER State (Verification)

Document in .progress.md under ## Reality Check (AFTER):

markdown
## Reality Check (AFTER)

**Command**: `pnpm test`
**Result**: PASS
**Output**:

PASS src/auth.test.ts All tests passed

**Comparison**: BEFORE failed with 401, AFTER passes
**Verified**: Issue resolved

VF Task Format

Add as task 4.3 (after PR creation) for fix-type specs:

markdown
- [ ] 4.3 VF: Verify original issue resolved
  - **Do**:
    1. Read BEFORE state from .progress.md
    2. Re-run reproduction command: `<command>`
    3. Compare output with BEFORE state
    4. Document AFTER state in .progress.md
  - **Verify**: `grep -q "Verified: Issue resolved" ./specs/<name>/.progress.md`
  - **Done when**: AFTER shows issue resolved, documented in .progress.md
  - **Commit**: `chore(<name>): verify fix resolves original issue`

Test Quality Checks

When verifying test-related fixes, check for mock-only test anti-patterns. See references/mock-quality-checks.md for detailed patterns.

Quick reference red flags:

  • Mock declarations > 3x real assertions
  • Missing import of actual module under test
  • All assertions are mock interaction checks (toHaveBeenCalled)
  • No integration tests
  • Missing mock cleanup (afterEach)

Why This Matters

WithoutWith
"Fix CI" spec completes but CI still redCI verified green before merge
Tests "fixed" but original failure unknownBefore/after comparison proves fix
Silent regressionsExplicit failure reproduction
Manual verification requiredAutomated verification in workflow
Tests pass but only test mocksTests verify real behavior, not mock behavior
False sense of security from green testsConfidence that tests catch real bugs

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 Verification AI skill do?

This skill should be used when the user asks to "verify a fix", "reproduce failure", "diagnose issue", "check BEFORE/AFTER state", "VF task", "reality check", "check test quality", "mock-only tests", or needs guidance on verifying fixes by reproducing failures before and after implementation, or detecting mock-heavy test anti-patterns.

Why use Reality Verification on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tzachbon/smart-ralph/tree/main/plugins/ralph-specum/skills/reality-verification. 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 Verification?

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 Verification?

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

Is the Reality Verification AI skill free?

Yes. It is published on GitHub by tzachbon 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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