THE PAI FEATURE ROADMAP

[danielmiessler]

Here are my main ideas for the PAI Roadmap features.

1. LOCAL MODEL SUPPORT (Probably Ollama)

• I want the system to ideally be able to switch into OFFLINE or PRIVATE mode
• Either in config, or via prompting

The challenge is that Claude Code by default sends to Anthropic, so I'm not sure how easy it'll be for it to route EVERYTHING through local models only.

2. SCHEDULED / CLOUD FUNCTIONALITY

• I want the ability to run tasks that take place on a remote server and either run until they're finished or maybe run on a schedule
• In other words, I don't want to fully be tied to a particular local session
• Think like n8n type functionality where we can wire up Intelligence Pipelines and have them running as part of our system

3. REMOTE / MOBILE INTERACTION

• I want to be able to be out and about and invoke a shortcut on my iPhone and send requests to Kai
• Will need to be highly secure with multiple layers of security
• I'm imagining that the requests come into a message queue on the internet somewhere
• Kai is then running somewhere else on the internet and he reaches out like every few seconds to check the queue
• More security checks
• Then he parses and executes the command

4. COMPUTER MONITORING & PROACTIVE AGENT

Comprehensive system monitoring to enable true proactive digital assistant capabilities:

• Monitor all screens and applications (visual context awareness)
• Listen to all microphones (audio context awareness)
• Track keyboard/mouse activity and application state
• Understand user context, intentions, and workflow patterns
• Proactively suggest actions, automate tasks, and anticipate needs
• Operate continuously in background without explicit invocation

The Vision: True Digital Assistant

As described in The Real Internet of Things: Digital Assistants:

"A computer system that can monitor human context, intentions, and commands, interpret them, and then take action as well or better than a professional personal assistant."

"What's more significant is that they will do this for us continuously."

This represents the evolution from reactive command-response AI to a true digital assistant that:

• Understands context from everything happening on your computer
• Anticipates needs based on learned patterns and current activity
• Takes autonomous action on your behalf with learned preferences
• Works continuously rather than only when explicitly invoked

Implementation Challenges

This will likely be the most difficult feature to implement:

Security Considerations:

• Screen content may contain sensitive information (passwords, financial data, private communications)
• Microphone access requires careful privacy controls and user consent
• Must implement robust permission system with granular controls
• Need secure storage and processing of monitored data
• Implement "privacy modes" to exclude certain apps/windows
• Consider encryption for all monitored data at rest and in transit

Technical Complexity:

• Cross-platform screen capture (macOS, Linux, Windows)
• Real-time audio processing and transcription
• Computer vision for understanding screen content
• Application state monitoring and context building
• Performance optimization to avoid system slowdown
• Battery/resource management for continuous background operation
• Integration with existing PAI architecture

Privacy & Trust:

• User must have complete control over what's monitored
• Transparency about what data is collected and how it's used
• Clear audit trail of agent actions taken
• Ability to pause/disable monitoring at any time
• Local processing vs. cloud processing decisions

Why This Is Critical

This is the most significant step toward PAI becoming an actual Digital Assistant as envisioned in The Real Internet of Things.

Without continuous context awareness, PAI remains a reactive system - powerful but requiring explicit user commands. With computer monitoring, PAI can:

1. Proactively assist - "I notice you're working on a presentation, would you like me to gather the latest metrics for slide 7?"
2. Prevent mistakes - "You're about to commit code with an API key visible in that file"
3. Optimize workflow - "You've switched between these three apps 12 times in the last hour - should I automate this pattern?"
4. Learn preferences - Understanding how you work to better represent you in future actions
5. Continuous operation - Working for you even when you're not directly interacting with the system

This transforms PAI from an AI tool into a true digital companion that understands your work, anticipates your needs, and augments your capabilities continuously.

Related Reading

• The Real Internet of Things (Book)
• The Real Internet of Things: Digital Assistants (Blog Post)
• How My Projects Fit Together - Human 3.0

Discussion Point: Where should we draw the privacy/utility line? What monitoring capabilities are most valuable vs. most concerning?

--

Open to other ideas to add here!

Let's discuss!

[Andrew-DataDecode]

I think a combination of these 3 task is the next big step in integrating and using llms in general. A local model with a Claude code like interface that I can set up local or remote (and run that remote from a phone). Basically an open source combination of claude code and cusor. Claude code is why I use Claude as my default.

In my mind, the models themselves are commodities for the vast majority of tasks. I don't need high powered model for 90% of what I use PAI to do (with the incredible foundation you provided). A lot of the work arounds I have built into my PAI are about collecting info and thoughts when I'm out and about.

In other words the "next steps" feel like a separate project that PAI then sits on top of as a personalization layer.

[parnoldx]

IMO a great feature would be a setup command as Claude cmd to setup everything. It interviews the user what he want and then wires everything so that it works. Something like https://github.com/heyitsnoah/claudesidian/blob/main/.claude/commands/init-bootstrap.md

[danielmiessler]

Noted, thank you

[smolcompute]

I haven't tried, but I've seen many youtube videos on running claude code with non-Anthtropic models like GLM, Kimi, etc.

There's this: https://github.com/musistudio/claude-code-router
and this: https://code.claude.com/docs/en/llm-gateway

[danielmiessler]

Those look pretty gnarly in terms of how they mess with the install, but I will take a look.

[smolcompute]

This roadmap hits all the right targets I think.
Figuring out how to seamlessly continue to use PAI on mobile will be a major unlock !

I haven't tried yet to use PAI with the new Claude Code Cloud which is accessible in the mobile app. But that necessitates syncing your entire PAI to github which is not ideal privacy-wise.

I'm really no expert but maybe there's a way via Twingate or Tailscale ?

[smolcompute]

Another area for the roadmap could be Ambient PAI and reducing friction to user input. It would be amazing to be able to invoke PAI with voice, or a single click on any of your devices, à la "Hey Siri". Ambient PAI, i.e., PAI always on ready to help.

[danielmiessler]

Yeah, that's included in the one of the numbers up there

…

On Mon, Nov 10, 2025 at 08:48, smol compute < ***@***.*** > wrote: Another area for the roadmap could be *Ambient PAI* and reducing friction to user input. It would be amazing to be able to invoke PAI with voice, or a single click on any of your devices, à la "Hey Siri". Ambient PAI, i.e., PAI always on ready to help. — Reply to this email directly, view it on GitHub ( #79 (comment) ) , or unsubscribe ( https://github.com/notifications/unsubscribe-auth/AAAMLXSZ55SA3YE3O3WA3I334C6UJAVCNFSM6AAAAACLP427WKVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTIOJSGY4TCNA ). You are receiving this because you authored the thread. Message ID: <danielmiessler/Personal_AI_Infrastructure/repo-discussions/79/comments/14926914 @ github. com>

[HolgerGelhausen]

Great approach with PAI. I've now added lots of tools and agents.
What would help me is a kind of consistency checker that checks whether the commands, agents, and skills are correctly referenced with commands in the system.

I've built about 14 YouTube to knowledge agents. Video to argumentation or video to Mermaid, etc.
Then I sometimes forget the keywords, so I tried to build a master video agent/command that first asks what I actually want.
These are my everyday challenges.
Furthermore, and this is certainly my fault, I don't always manage to get the agents to stick to the output directories.
As I said, these are my issues, which depend on the fact that I haven't understood everything 100% yet.
Thank you for the great system.

[smolcompute]

Hi. Would you share how you get Claude to fetch the youtube transcript ?

[HolgerGelhausen]

Here is the full Command, generates Mermaid Diagramms of Videos:

#!/usr/bin/env bun
/**

• Video Argument Analyzer
• Analysiert YouTube-Videos und erstellt strukturierte Dokumentation mit:
• • Detailliertem Leitfaden (Argumentationsstruktur, Kernthesen, etc.)
• • 9+ Mermaid-Diagrammen (automatisch an Video-Typ angepasst)
• • Hochauflösenden PNG-Bildern
• • Bildergalerie mit Vorschau
• STRUCTURE:
• • Command: commands/analyze-video-arguments.md (this file)
• • Skill: ~/.claude/skills/analyze-video/ (UFC structure)
• • Resources: ~/.claude/skills/analyze-video/resources/{prompts,templates}
• @Version 1.1.0
• @author PAI System
• DEPENDENCIES:
• • youtube-transcript-api (Python): uv pip install youtube-transcript-api
• • yt-dlp (for metadata): brew install yt-dlp
• • mermaid-cli: npm install -g @mermaid-js/mermaid-cli
• • Anthropic Claude API
• FEATURES:
• • Multi-Mode: argument, tutorial, vortrag, interview, auto
• • Detailgrad konfigurierbar: kurz, mittel, ausführlich
• • Auflösung anpassbar: niedrig, mittel, hoch
• • Immer auf Deutsch
• • Automatische Mermaid → PNG Konvertierung
• USAGE:
• bun commands/analyze-video-arguments.md "VIDEO_URL"
• bun commands/analyze-video-arguments.md "VIDEO_URL" --mode argument --detail ausführlich
• bun commands/analyze-video-arguments.md "VIDEO_URL" --mode tutorial --resolution hoch
• OPTIONS:
• --mode MODE Analysis mode: argument|tutorial|vortrag|interview|auto (default: auto)
• --detail LEVEL Detail level: kurz|mittel|ausführlich (default: mittel)
• --resolution RES PNG resolution: niedrig|mittel|hoch (default: mittel)
• --lang LANG Transcript language (default: de)
• --help Show this help
• EXAMPLES:

• Automatische Modus-Erkennung

• bun commands/analyze-video-arguments.md "https://youtube.com/watch?v=..."

• Expliziter Argument-Modus mit hoher Auflösung

• bun commands/analyze-video-arguments.md "https://youtube.com/watch?v=..." \

• --mode argument --resolution hoch
  

• Tutorial-Analyse ausführlich

• bun commands/analyze-video-arguments.md "https://youtube.com/watch?v=..." \

• --mode tutorial --detail ausführlich
  

• OUTPUT STRUCTURE:
• Projects/Documentation/HHMM_DD-MM-YYYY_video-titel/
• ├── README.md # Projekt-Übersicht
• ├── LEITFADEN.md # Hauptdokumentation
• ├── struktur.md # Mermaid-Quellcode
• ├── BILDER-UEBERSICHT.md # Bildergalerie
• ├── extract-mermaid.sh # Re-generierungs-Script
• ├── metadata.json # Video-Metadaten + Config
• └── images/ # PNG-Diagramme

•   ├── 01-*.png
  

•   └── ...
  

*/

import { exec } from 'child_process';
import { promisify } from 'util';
import { mkdir, writeFile, readFile } from 'fs/promises';
import { join, dirname } from 'path';
import { fileURLToPath } from 'url';
import { existsSync } from 'fs';

const execAsync = promisify(exec);

// Get PAI directory
const PAI_DIR = process.env.PAI_DIR || join(dirname(fileURLToPath(import.meta.url)), '..');

// Get Skill resources directory (UFC structure)
const SKILL_RESOURCES = join(process.env.HOME, '.claude', 'skills', 'analyze-video', 'resources');

// Parse arguments
const args = process.argv.slice(2);

// Show help
if (args.includes('--help') || args.length === 0) {
console.log(`
🎥 Video Argument Analyzer

USAGE:
bun commands/analyze-video-arguments.md "VIDEO_URL" [OPTIONS]

OPTIONS:
--mode MODE Analysis mode: argument|tutorial|vortrag|interview|auto (default: auto)
--detail LEVEL Detail level: kurz|mittel|ausführlich (default: mittel)
--resolution RES PNG resolution: niedrig|mittel|hoch (default: mittel)
--lang LANG Transcript language (default: de)
--help Show this help

EXAMPLES:

Automatische Erkennung

bun commands/analyze-video-arguments.md "https://youtube.com/watch?v=..."

Argument-Modus, hohe Auflösung

bun commands/analyze-video-arguments.md "URL" --mode argument --resolution hoch

Tutorial-Analyse ausführlich

bun commands/analyze-video-arguments.md "URL" --mode tutorial --detail ausführlich

DEPENDENCIES:
uv pip install youtube-transcript-api
brew install yt-dlp
npm install -g @mermaid-js/mermaid-cli

MODES:
argument - Für argumentative/politische Videos (Thesen, Beweise, Schlussfolgerungen)
tutorial - Für Anleitungen/Tutorials (Schritte, Werkzeuge, Troubleshooting)
vortrag - Für akademische Vorträge (Konzepte, Chronologie, Belege)
interview - Für Interviews/Gespräche (Positionen, Themen, Konsens/Dissens)
auto - Automatische Erkennung basierend auf Inhalt
`);
process.exit(0);
}

// Get URL (first non-flag argument)
const url = args.find(arg => !arg.startsWith('--'));

if (!url) {
console.error('❌ Error: No URL provided');
console.log('Usage: bun commands/analyze-video-arguments.md "VIDEO_URL"');
process.exit(1);
}

// Parse options
const getOption = (flag: string, defaultValue: string) => {
const index = args.indexOf(flag);
return index !== -1 && args[index + 1] ? args[index + 1] : defaultValue;
};

const mode = getOption('--mode', 'auto');
const detail = getOption('--detail', 'mittel');
const resolution = getOption('--resolution', 'mittel');
const lang = getOption('--lang', 'de');

// Validate options
const validModes = ['argument', 'tutorial', 'vortrag', 'interview', 'auto'];
const validDetails = ['kurz', 'mittel', 'ausführlich'];
const validResolutions = ['niedrig', 'mittel', 'hoch'];

if (!validModes.includes(mode)) {
console.error(❌ Invalid mode: ${mode});
console.log(Valid modes: ${validModes.join(', ')});
process.exit(1);
}

if (!validDetails.includes(detail)) {
console.error(❌ Invalid detail level: ${detail});
console.log(Valid levels: ${validDetails.join(', ')});
process.exit(1);
}

if (!validResolutions.includes(resolution)) {
console.error(❌ Invalid resolution: ${resolution});
console.log(Valid resolutions: ${validResolutions.join(', ')});
process.exit(1);
}

// Resolution mapping
const resolutionMap = {
'niedrig': { width: 1600, height: 1200 },
'mittel': { width: 2400, height: 1800 },
'hoch': { width: 3200, height: 2400 }
};

// Extract video ID from URL
function extractVideoId(url: string): string {
const match = url.match(/(?:v=|/)([\w-]{11})(?:?|&|$)/);
return match ? match[1] : 'unknown';
}

// Create timestamp for directory name
function getTimestamp(): string {
const now = new Date();
return ${String(now.getHours()).padStart(2,'0')}${String(now.getMinutes()).padStart(2,'0')}_${String(now.getDate()).padStart(2,'0')}-${String(now.getMonth()+1).padStart(2,'0')}-${now.getFullYear()};
}

// Download transcript using youtube-transcript.md command
async function downloadTranscript(url: string, lang: string): Promise<{ transcriptPath: string, metadataPath: string, outputDir: string }> {
console.log('\n📥 Downloading transcript...');

try {
const transcriptCommand = join(PAI_DIR, 'commands', 'youtube-transcript.md');
const { stdout, stderr } = await execAsync(
bun "${transcriptCommand}" "${url}" --lang ${lang}
);

// Extract output directory from stdout
const match = stdout.match(/Output directory: (.+)/);
if (!match) {
  throw new Error('Could not find transcript output directory');
}

const outputDir = match[1].trim();
const transcriptPath = join(outputDir, 'transcript.txt');
const metadataPath = join(outputDir, 'metadata.json');

if (!existsSync(transcriptPath)) {
  throw new Error(`Transcript not found at: ${transcriptPath}`);
}

console.log(`✅ Transcript downloaded to: ${transcriptPath}`);
return { transcriptPath, metadataPath, outputDir };

} catch (error: any) {
console.error('❌ Error downloading transcript:', error.message);
throw error;
}
}

// Detect mode from transcript and metadata
async function detectMode(transcriptPath: string, metadataPath: string): Promise {
console.log('\n🔍 Detecting video mode...');

const transcript = await readFile(transcriptPath, 'utf-8');
const metadata = JSON.parse(await readFile(metadataPath, 'utf-8'));

const title = metadata.title.toLowerCase();
const description = (metadata.description || '').toLowerCase();
const text = (transcript.substring(0, 2000)).toLowerCase();

// Detection rules
const patterns = {
argument: [
'argument', 'these', 'beweis', 'schlussfolgerung', 'kritik', 'politik',
'debatte', 'streit', 'position', 'gegner', 'demokratie'
],
tutorial: [
'tutorial', 'anleitung', 'schritt', 'lernen', 'wie man', 'how to',
'installation', 'setup', 'konfiguration', 'beispiel'
],
vortrag: [
'vortrag', 'präsentation', 'forschung', 'studie', 'wissenschaft',
'universität', 'konferenz', 'lecture', 'talk'
],
interview: [
'interview', 'gespräch', 'frage', 'antwort', 'diskussion',
'gast', 'moderator', 'talk show'
]
};

// Count matches for each mode
const scores: Record<string, number> = {
argument: 0,
tutorial: 0,
vortrag: 0,
interview: 0
};

for (const [modeName, keywords] of Object.entries(patterns)) {
for (const keyword of keywords) {
if (title.includes(keyword)) scores[modeName] += 3;
if (description.includes(keyword)) scores[modeName] += 2;
if (text.includes(keyword)) scores[modeName] += 1;
}
}

// Find mode with highest score
const detectedMode = Object.entries(scores)
.sort(([, a], [, b]) => b - a)[0][0];

console.log(✅ Detected mode: ${detectedMode});
console.log( Scores: ${JSON.stringify(scores)});

return detectedMode;
}

// Create output directory
async function createOutputDir(metadata: any): Promise {
const timestamp = getTimestamp();
const videoId = metadata.id || 'unknown';

// Create safe filename from title
const safeTitle = metadata.title
.toLowerCase()
.replace(/[^a-z0-9äöüß\s-]/g, '')
.replace(/\s+/g, '-')
.substring(0, 50);

const dirName = ${timestamp}_${safeTitle};
const outputDir = join(PAI_DIR, 'Projects', 'Documentation', dirName);

await mkdir(outputDir, { recursive: true });
await mkdir(join(outputDir, 'images'), { recursive: true });

console.log(\n📁 Output directory: ${outputDir});
return outputDir;
}

// Save prompt to file for manual analysis
async function savePromptForManualAnalysis(
outputDir: string,
transcript: string,
metadata: any,
mode: string,
detail: string
): Promise {

console.log(\n📝 Preparing prompt for Claude Code analysis...);

const prompt = await generatePromptForMode(mode, detail, transcript, metadata);
const promptPath = join(outputDir, 'PROMPT.md');

await writeFile(promptPath, prompt, 'utf-8');

console.log(✅ Prompt saved to: ${promptPath});
console.log('\n📋 Next steps:');
console.log(' 1. Open PROMPT.md in Claude Code');
console.log(' 2. Claude wird die Analyse erstellen');
console.log(' 3. Speichere die Outputs in diesem Verzeichnis');

return promptPath;
}

// Get mode-specific title
function getModeTitle(mode: string): string {
const titles: Record<string, string> = {
argument: 'Argumentationsleitfaden',
tutorial: 'Tutorial-Leitfaden',
vortrag: 'Vortrags-Analyse',
interview: 'Interview-Analyse'
};
return titles[mode] || 'Analyse';
}

// Generate prompt for mode from templates
async function generatePromptForMode(
mode: string,
detail: string,
transcript: string,
metadata: any
): Promise {

// Load mode-specific prompt template
const promptPath = join(SKILL_RESOURCES, 'prompts', ${mode}.md);

if (!existsSync(promptPath)) {
throw new Error(Prompt template not found for mode: ${mode});
}

let promptTemplate = await readFile(promptPath, 'utf-8');

// Replace placeholders
const replacements: Record<string, string> = {
'{DETAIL_LEVEL}': detail,
'{VIDEO_TITLE}': metadata.title,
'{CHANNEL}': metadata.channel || 'Unbekannt',
'{DURATION}': Math.floor((metadata.duration || 0) / 60).toString(),
'{VIDEO_URL}': metadata.url || '',
'{TRANSCRIPT}': transcript
};

for (const [placeholder, value] of Object.entries(replacements)) {
promptTemplate = promptTemplate.replace(new RegExp(placeholder.replace(/[.*+?^${}()|[]\]/g, '\$&'), 'g'), value);
}

return promptTemplate;
}

// Old function kept for reference - DELETE THIS LATER
async function OLD_generatePromptForMode_DELETE_ME(
mode: string,
detail: string,
transcript: string,
metadata: any
): Promise {

const basePrompt = `Analysiere das folgende Video-Transkript im Modus "${mode}" mit Detailgrad "${detail}".

Video-Informationen:

• Titel: ${metadata.title}
• Kanal: ${metadata.channel || 'Unbekannt'}
• Dauer: ${Math.floor((metadata.duration || 0) / 60)} Minuten

Transkript:
${transcript}

Erstelle:

1. Einen detaillierten Leitfaden
2. Mermaid-Diagramme zur Visualisierung der Struktur
3. Kernthesen und Zusammenfassungen

`;

const modePrompts: Record<string, string> = {
argument: `
FOKUS: Argumentationsstruktur

• Identifiziere Thesen, Beweise, Schlussfolgerungen
• Analysiere logische Ketten und Kausali

täten

• Finde Paradoxe und rhetorische Strategien

• Erstelle flowcharts für Argumentationsfluss
  , tutorial:
  FOKUS: Lernstruktur

• Extrahiere Schritte und Abläufe

• Identifiziere Werkzeuge und Voraussetzungen

• Dokumentiere Troubleshooting-Punkte

• Erstelle sequenzielle Diagramme
  , vortrag:
  FOKUS: Konzeptuelle Struktur

• Identifiziere Hauptkonzepte und Zusammenhänge

• Analysiere chronologischen Aufbau

• Extrahiere Belege und Beispiele

• Erstelle Mindmaps für Konzepte
  , interview:
  FOKUS: Gesprächsstruktur

• Identifiziere Positionen und Standpunkte

• Analysiere Konsens und Dissens

• Extrahiere Kernaussagen pro Person

• Erstelle Sequence-Diagramme für Gesprächsfluss
  `
  };

  return basePrompt + (modePrompts[mode] || '');
  }

// Placeholder generators
function generateMermaidPlaceholder(mode: string): string {
return `# ${getModeTitle(mode)} – Mermaid Diagramme

1. Gesamtstruktur

```mermaid
graph TD
Start[Video: ${mode}]
Start --> A[Hauptthemen]
A --> B[Details]
B --> End[Zusammenfassung]
```

[More diagrams would be generated here based on mode...]
`;
}

function generateReadmePlaceholder(metadata: any, mode: string, detail: string, resolution: string): string {
return `# ${getModeTitle(mode)}: ${metadata.title}

Video: YouTube
Modus: ${mode}
Detailgrad: ${detail}
Auflösung: ${resolution}

Projektstruktur

```
├── README.md
├── ${getModeTitle(mode).toUpperCase()}.md
├── struktur.md
├── BILDER-UEBERSICHT.md
├── extract-mermaid.sh
├── metadata.json
└── images/
└── *.png
```

[More content would go here...]
`;
}

function generateBilderGaleriePlaceholder(mode: string): string {
return `# Visualisierungen: ${getModeTitle(mode)}

Übersicht aller Diagramme

[Diagrams would be listed here with embedded images...]
`;
}

// Create mermaid extraction script by copying the template
async function createExtractionScript(outputDir: string, resolution: string): Promise {
const templateScriptPath = join(SKILL_RESOURCES, 'templates', 'mermaid-to-png.sh');

if (!existsSync(templateScriptPath)) {
throw new Error(Mermaid conversion script not found at: ${templateScriptPath});
}

const scriptPath = join(outputDir, 'mermaid-to-png.sh');

// Copy the script
const scriptContent = await readFile(templateScriptPath, 'utf-8');
await writeFile(scriptPath, scriptContent, 'utf-8');
await execAsync(chmod +x "${scriptPath}");

console.log(✅ Copied mermaid-to-png.sh to output directory);
}

// Save metadata
async function saveMetadata(
outputDir: string,
metadata: any,
mode: string,
detail: string,
resolution: string
): Promise {
const metadataWithConfig = {
...metadata,
analysis: {
mode,
detail,
resolution,
created: new Date().toISOString(),
diagrams_count: 0, // Will be updated after generation
version: '1.0.0'
}
};

const metadataPath = join(outputDir, 'metadata.json');
await writeFile(metadataPath, JSON.stringify(metadataWithConfig, null, 2), 'utf-8');

console.log(✅ Saved metadata: ${metadataPath});
}

// Main function
async function main() {
console.log('🎥 Video Argument Analyzer');
console.log('==========================\n');

console.log(📊 Configuration:);
console.log( Mode: ${mode});
console.log( Detail: ${detail});
console.log( Resolution: ${resolution});
console.log( Language: ${lang});

try {
// Step 1: Download transcript
const { transcriptPath, metadataPath } = await downloadTranscript(url, lang);

// Step 2: Read transcript and metadata
const transcript = await readFile(transcriptPath, 'utf-8');
const metadata = JSON.parse(await readFile(metadataPath, 'utf-8'));

// Step 3: Detect mode if auto
const finalMode = mode === 'auto' ? await detectMode(transcriptPath, metadataPath) : mode;

// Step 4: Create output directory
const outputDir = await createOutputDir(metadata);

// Step 5: Save prompt for manual analysis
const promptPath = await savePromptForManualAnalysis(
  outputDir,
  transcript,
  metadata,
  finalMode,
  detail
);

// Step 6: Copy transcript
console.log('\n📝 Copying transcript...');
await writeFile(join(outputDir, 'transcript.txt'), transcript, 'utf-8');
console.log('   ✓ transcript.txt');

// Step 7: Create extraction script
await createExtractionScript(outputDir, resolution);

// Step 8: Save metadata
await saveMetadata(outputDir, metadata, finalMode, detail, resolution);

// NOTE: Analysis, structure, and images will be created manually via Claude Code
// After running PROMPT.md through Claude Code:
// 1. Save ARGUMENTATIONSLEITFADEN.md (or equivalent)
// 2. Save struktur.md with Mermaid diagrams
// 3. Run: ./mermaid-to-png.sh struktur.md images ${resolution}
// 4. Create README.md and BILDER-UEBERSICHT.md based on results

console.log('\n✅ Setup complete!');
console.log(`\n📂 Output: ${outputDir}`);
console.log('\n📋 Generated files:');
console.log('   - PROMPT.md (für Claude Code)');
console.log('   - transcript.txt');
console.log('   - mermaid-to-png.sh');
console.log('   - metadata.json');
console.log('\n🚀 Next steps:');
console.log(`   1. cd "${outputDir}"`);
console.log('   2. Open PROMPT.md in Claude Code');
console.log('   3. Lasse Claude die Analyse erstellen');
console.log('   4. Speichere Outputs (LEITFADEN.md, struktur.md)');
console.log(`   5. Run: ./mermaid-to-png.sh struktur.md images ${resolution}`);

} catch (error: any) {
console.error('\n❌ Fatal error:', error.message);
process.exit(1);
}
}

// Run
main().catch(error => {
console.error('❌ Fatal error:', error.message);
process.exit(1);
});

[danielmiessler]

fabric -y

…

On Tue, Nov 11, 2025 at 05:46, smol compute < ***@***.*** > wrote: Hi. Would you share how you get Claude to fetch the youtube transcript ? — Reply to this email directly, view it on GitHub ( #79 (reply in thread) ) , or unsubscribe ( https://github.com/notifications/unsubscribe-auth/AAAMLXX26OFTOKIGB2BFMLD34HSFHAVCNFSM6AAAAACLP427WKVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTIOJTGY4DOMA ). You are receiving this because you authored the thread. Message ID: <danielmiessler/Personal_AI_Infrastructure/repo-discussions/79/comments/14936870 @ github. com>

[sudo-eugene]

I would vote that the documentation could be updated as a first priority to cause less mass confusion

#95