Proof Of Work logo

Proof Of Work

Organization
MadAppGang
proof-of-work

Proof artifact generation patterns for task validation. Covers screenshots, test results, deployments, and confidence scoring.

Overview

PublisherMadAppGang
Repositoryclaude-code
Skill nameproof-of-work
Stars
281
Forks
26
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 MadAppGang on GitHub. Read the source before you install it.

Installation

Install the Proof Of Work 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/MadAppGang/claude-code.git /tmp/claude-code
mkdir -p .claude/skills
cp -r /tmp/claude-code/plugins/autopilot/skills/proof-of-work .claude/skills/proof-of-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proof Of Work 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 Proof Of Work 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 Proof Of Work 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.

plugin: autopilot updated: 2026-01-20

Proof-of-Work

Version: 0.1.0 Purpose: Generate validation artifacts for autonomous task completion Status: Phase 1

When to Use

Use this skill when you need to:

  • Generate proof artifacts after task completion
  • Capture screenshots for UI verification
  • Parse and report test results
  • Calculate confidence scores for task validation
  • Determine if a task can be auto-approved

Overview

Proof-of-work is the mechanism that validates task completion. Every finished task must include verifiable artifacts that demonstrate the work was done correctly.

Proof Types by Task

Bug Fix Proof

ArtifactRequiredPurpose
Git diffYesShow minimal, focused changes
Test resultsYesAll tests passing
Regression testYesSpecific test for the bug
Error log (before/after)OptionalVisual evidence

Feature Proof

ArtifactRequiredPurpose
ScreenshotsYesVisual verification
Test resultsYesFunctionality works
Coverage reportYes>= 80% coverage
Build outputYesBuilds successfully
Deployment URLOptionalLive demo

UI Change Proof

ArtifactRequiredPurpose
Desktop screenshotYes1920x1080 view
Mobile screenshotYes375x667 view
Tablet screenshotYes768x1024 view
Accessibility scoreYes>= 80 Lighthouse
Visual regressionOptionalBackstopJS diff

Screenshot Capture

Playwright Pattern:

typescript
import { chromium } from 'playwright';

async function captureScreenshots(url: string, outputDir: string) {
  const browser = await chromium.launch({ headless: true });
  const context = await browser.newContext();
  const page = await context.newPage();

  // Desktop
  await page.setViewportSize({ width: 1920, height: 1080 });
  await page.goto(url);
  await page.waitForLoadState('networkidle');
  await page.screenshot({
    path: `${outputDir}/desktop.png`,
    fullPage: true,
  });

  // Mobile
  await page.setViewportSize({ width: 375, height: 667 });
  await page.goto(url);
  await page.waitForLoadState('networkidle');
  await page.screenshot({
    path: `${outputDir}/mobile.png`,
    fullPage: true,
  });

  // Tablet
  await page.setViewportSize({ width: 768, height: 1024 });
  await page.goto(url);
  await page.waitForLoadState('networkidle');
  await page.screenshot({
    path: `${outputDir}/tablet.png`,
    fullPage: true,
  });

  await browser.close();
}

Confidence Scoring

Algorithm:

typescript
interface ProofArtifacts {
  testResults?: { passed: number; total: number };
  buildSuccessful?: boolean;
  lintErrors?: number;
  screenshots?: string[];
  testCoverage?: number;
  performanceScore?: number;
}

function calculateConfidence(artifacts: ProofArtifacts): number {
  let score = 0;

  // Tests (40 points)
  if (artifacts.testResults) {
    if (artifacts.testResults.passed === artifacts.testResults.total) {
      score += 40;
    }
  }

  // Build (20 points)
  if (artifacts.buildSuccessful) {
    score += 20;
  }

  // Coverage (20 points)
  if (artifacts.testCoverage) {
    if (artifacts.testCoverage >= 80) score += 20;
    else if (artifacts.testCoverage >= 60) score += 15;
    else if (artifacts.testCoverage >= 40) score += 10;
    else score += 5;
  }

  // Screenshots (10 points)
  if (artifacts.screenshots) {
    if (artifacts.screenshots.length >= 3) score += 10;
    else if (artifacts.screenshots.length >= 1) score += 5;
  }

  // Lint (10 points)
  if (artifacts.lintErrors === 0) {
    score += 10;
  }

  return score;
}

Confidence Thresholds

ConfidenceAction
>= 95%Auto-approve (In Review -> Done)
80-94%Manual review required
< 80%Validation failed, iterate

Proof Summary Template

markdown
# Proof of Work

**Task**: {issue_id}
**Type**: {task_type}
**Confidence**: {score}%

## Test Results
- Total: {total}
- Passed: {passed}
- Failed: {failed}
- Coverage: {coverage}%

## Build
- Status: {status}
- Duration: {duration}

## Screenshots
- Desktop: proof/desktop.png
- Mobile: proof/mobile.png
- Tablet: proof/tablet.png

## Artifacts
- test-results.txt
- coverage.json
- build-output.txt

Examples

Example 1: Feature Proof Generation

typescript
const proof = {
  testResults: { passed: 15, total: 15 },
  buildSuccessful: true,
  lintErrors: 0,
  screenshots: ['desktop.png', 'mobile.png', 'tablet.png'],
  testCoverage: 85,
};

const confidence = calculateConfidence(proof);
// 40 (tests) + 20 (build) + 20 (coverage) + 10 (screenshots) + 10 (lint) = 100%

Example 2: Partial Proof

typescript
const proof = {
  testResults: { passed: 12, total: 15 },  // Some failing
  buildSuccessful: true,
  lintErrors: 2,
  screenshots: ['desktop.png'],
  testCoverage: 65,
};

const confidence = calculateConfidence(proof);
// 0 (tests fail) + 20 (build) + 15 (coverage) + 5 (1 screenshot) + 0 (lint errors) = 40%
// Result: Validation failed, must iterate

Best Practices

  • Always capture screenshots for UI work
  • Run full test suite, not just affected tests
  • Include coverage report for features
  • Build must pass before any proof is valid
  • Store proofs in session directory for debugging
  • Generate proof summary in markdown for Linear comments

Frequently asked questions

What does the Proof Of Work AI skill do?

Proof artifact generation patterns for task validation. Covers screenshots, test results, deployments, and confidence scoring.

Why use Proof Of Work on TypingMind?

Because you install it once and use it with any model. Proof Of Work 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 Proof Of Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MadAppGang/claude-code/tree/main/plugins/autopilot/skills/proof-of-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Proof Of Work?

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 Proof Of Work?

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

Is the Proof Of Work AI skill free?

Yes. It is published on GitHub by MadAppGang 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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