Video: OpenClaw Use Cases that are actually helpful... Prompts: mberman84/065631c62d6d8f30ecb14748c00fc6d9
| # | Feature | Was genau | Nützlich | Machbar |
|---|---|---|---|---|
| 1 | Personal CRM | Gmail/Calendar auto-scannen, AI-Filtering (Spam/Newsletter raus), Contact Scoring, Embeddings für Semantic Search ("Wer war das von Firma X?") | ⭐⭐⭐⭐⭐ | ✅ Easy — SQLite + Gmail API + Cron |
| 2 | Knowledge Base (RAG) | URLs/PDFs/YouTube/Tweets speichern, Chunking (800 chars, 200 overlap), Embeddings (Gemini free), Cosine Similarity Retrieval, Fallback-Chain für Content Extraction | ⭐⭐⭐⭐ | 🟡 Medium — Embedding-Provider + Chunking-Logic |
| 3 | Content Idea Pipeline | Research → Semantic Dedupe (40% Similarity-Gate!) → Brief → Task erstellen. Ideen-DB mit Embeddings verhindert Doppel-Pitches | ⭐⭐⭐⭐ (Content Creator) | 🟡 Medium — Embedding + Task-API |
| 4 | Twitter/X Research | Tiered API: FxTwitter (free) → TwitterAPI.io (~$0.15/1K) → Official X API (teuer). Caching 1h TTL, Sentiment-Analyse, Thread-Expansion | ⭐⭐⭐⭐ | 🟡 Medium — API-Kosten/Limits |
| 5 | YouTube Analytics | Daily Metrics (Views, Watch Time, CTR, Subs), Competitor Tracking, 7-Day Moving Average, Chart-Generation (matplotlib, dark theme) | ⭐⭐⭐⭐ (YouTuber) | 🟡 Medium — YouTube Data API + Analytics API |
| 6 | Nightly Business Briefing | Signal Collection aus allen Tools → 3-Phasen AI Council: Draft → 4 Reviewer-Personas → Consensus. Gewichtetes Priority-Scoring | ⭐⭐⭐⭐⭐ | 🔴 Hard + teuer (~$2-5/Nacht, 5× Frontier-Model) |
| 7 | CRM NL Interface | HubSpot/Salesforce per Chat: Contacts/Companies/Deals CRUD, Associations, Intent Classification, Schema Inspection | ⭐⭐⭐ (nur mit Enterprise CRM) | 🟡 Medium — API + OAuth |
| 8 | AI Humanizer | AI-Tells erkennen ("delve", "landscape", "leverage"), Satzlänge variieren, Channel-Tuning (Twitter/LinkedIn/Blog/Email), Kontraktionen + Fragmente | ⭐⭐⭐ | ✅ Easy — nur guter Prompt |
| 9 | Image Gen + Iterative Editing | Describe → Generate 1-3 Variants → Feedback Loop → img2img/Inpainting. Context-Tracking über Session | ⭐⭐⭐⭐ | ✅ Easy — DALL-E/Replicate |
| 10 | Task Management | Meeting-Transkripte → LLM extrahiert Action Items → Approval Flow (nicht auto-create!) → Todoist/Asana API. CRM Cross-Reference | ⭐⭐⭐⭐ | 🟡 Medium — Transcript + Task API |
| 11 | AI Usage/Cost Tracker | Jeder API-Call als JSONL geloggt, Pricing-Tabelle, Reports by model/task/day, Routing-Suggestions (Frontier→Cheap downgrade) | ⭐⭐⭐⭐⭐ | 🟡 Medium — Hooks in API-Calls |
- Stage 1: Hard filters (noreply@, info@, team@, Marketing-Domains)
- Stage 2: AI classification (Gemini Flash) — nur echte Kontakte mit 2-Way-Interaction
- Scoring: Base 50, +5/Exchange, +3/Meeting, +25 wenn in Email UND Calendar
- Twitter: FxTwitter API → X API → Web Scraping
- YouTube: yt-dlp Transcript
- Articles: Readability → Firecrawl → Playwright → Raw HTTP
- Quality Gate: min 500 chars, max 200K, Error-Page-Detection
- Phase 1: LeadAnalyst draftet 5-10 Empfehlungen
- Phase 2: 4 Personas reviewen parallel (Promise.all):
- GrowthStrategist (Skalierung)
- RevenueGuardian (Cashflow)
- SkepticalOperator (Realitäts-Check)
- TeamDynamicsArchitect (Team-Health)
- Phase 3: CouncilModerator → Consensus
- Priority = (Impact × 0.4) + (Confidence × 0.35) + ((100-Effort) × 0.25)
- Anthropic: Opus $15/$75, Sonnet $3/$15, Haiku $0.80/$4
- OpenAI: GPT-4 $30/$60, GPT-4 Turbo $10/$30, o1 $15/$60
- Google: Gemini Pro $10/$30, Flash $0.30/$1.20
- Flags: >25% Spend auf einen Task = Optimierungskandidat; Frontier-Model für simple Tasks = Downgrade-Vorschlag
| # | Feature | Status | Details |
|---|---|---|---|
| 1 | Personal CRM | ✅ Manuell | USER.md mit ~15 Kontakten, automatische Anreicherung nach jeder Interaktion, Heartbeat-Check |
| 2 | Knowledge Base | 🟡 Teilweise | memory_search über MEMORY.md + daily files. Kein echtes RAG mit Embeddings/Chunking |
| 3 | Content Pipeline | ❌ | Nicht relevant |
| 4 | Twitter Research | ❌ | Nicht eingerichtet |
| 5 | YouTube Analytics | ❌ | Nicht relevant |
| 6 | Business Briefing | ❌ | Zu teuer aktuell (~$2-5/Nacht für 5× Frontier-Model) |
| 7 | CRM NL Interface | ❌ | Kein Enterprise CRM vorhanden |
| 8 | AI Humanizer | ✅ Implizit | SOUL.md Persona ("Be direct, no sugarcoating") + kontaktspezifische Styles (Bayrisch für Noel, etc.) |
| 9 | Image Gen + Editing | ✅ Voll | nano-banana-pro (2K, image_input für Edits), Replicate API, Sub-Agent-Workflow |
| 10 | Task Management | ❌ | Keine Task-App angebunden. To-Dos nur in HEARTBEAT.md/Memory |
| 11 | Cost Tracker | 🟡 Minimal | Grobe Schätzung (~€2/Tag). Kein echtes Logging pro API-Call |
Zusätzlich implementiert (nicht im Video):
- 🎙️ Voice Cloning — Logge's geklonte Stimme via Replicate (minimax/speech-2.8-turbo), auto-Antwort auf Sprachnachrichten
- 🖥️ Full-Stack Deployment — Dokploy + Cloudflare + GitHub Pipeline für beliebige Web-Apps
- 🤖 Sub-Agent System — Claude Code / Codex für komplexe Coding-Tasks
- 🔒 Contact Security — Phone Guard, Outbound-Filter, DSGVO-Compliance, Social Engineering Tracking
- 🌡️ Energie-Dashboard — Wärmepumpe/Solar Tracking mit AI-OCR, Wetter + Sonnenprognose (energie.logge.top)
- 🎭 Kontakt-Personas — Individueller Kommunikationsstil pro Kontakt (Bayrisch, Roasting, Pranks)
- 🧹 Server-Maintenance — Puppeteer Zombie Cleanup Cron, Docker Container Mapping, RAM-Analyse
- Usage/Cost Tracker — Größter ROI, fliegen aktuell blind bei Kosten
- Knowledge Base RAG — Für Dev-Arbeit + Consulting-Wissensmanagement
- Nightly Business Briefing — Wenn Consultancy wächst, mega wertvoll (erstmal zu teuer)
(Consultancy: Berater → Prozessanalyse → AI-Backoffice-Automatisierung)
| # | Feature | Warum für InnovaVento? | Priorität |
|---|---|---|---|
| 1 | Personal CRM | Kundenkontakte, Berater-Zuordnung, Interaction-Tracking. Gmail/Calendar-Integration für automatisches Kunden-Touchpoint-Tracking | 🔴 Hoch |
| 6 | Business Briefing | Nightly Digest: Projekt-Status aller Kunden, offene Tasks, Pipeline-Health. Multi-Persona Review fängt blinde Flecken ab | 🔴 Hoch |
| 11 | Cost Tracker | AI-Lösungen für Kunden → Kosten tracken für Pricing, Margen, Kostenvoranschläge | 🔴 Hoch |
| 2 | Knowledge Base (RAG) | Prozess-Dokumentation pro Kunde, Best Practices, wiederverwendbare Playbooks | 🔴 Hoch |
| # | Feature | Warum für InnovaVento? | Priorität |
|---|---|---|---|
| 7 | CRM NL Interface | Wenn HubSpot/Salesforce kommt — Deals tracken, Pipeline managen per Chat | 🟡 Mittel |
| 10 | Task Management | Kunden-Meetings → Action Items → Auto-Tasks | 🟡 Mittel |
| 4 | Twitter/X Research | Markt-Screening: Pain Points finden, Lead-Generierung | 🟡 Mittel |
| 8 | AI Humanizer | Kunden-Kommunikation die nicht nach ChatGPT klingt | 🟡 Mittel |
| # | Feature | Warum weniger relevant? |
|---|---|---|
| 3 | Content Pipeline | Nur wenn Content Marketing |
| 5 | YouTube Analytics | Keine YouTube-Strategie |
| 9 | Image Gen | Bereits implementiert |
Phase 1 (Sofort, <1 Woche):
- Cost Tracker (#11) — Kostenklarheit BEVOR ihr für Kunden baut
- Knowledge Base (#2) — Playbook-System für wiederholbare Prozesse
Phase 2 (Monat 1):
- Personal CRM upgrade (#1) — Von USER.md zu echtem CRM mit Gmail/Calendar
- Task Management (#10) — Meeting → Tasks Pipeline
Phase 3 (Monat 2-3, ab 3+ Kunden):
- Business Briefing (#6) — Nightly Digest über alle Kunden-Projekte
- CRM NL Interface (#7) — Wenn Pipeline wächst
User (Telegram/WhatsApp/Discord)
↓
OpenClaw Gateway (Node.js, ~674MB RAM)
↓
Agent Session (Claude Opus/Sonnet via Anthropic API)
↓
Tools: exec, web_search, web_fetch, message, cron, browser, memory_search, ...
↓
Workspace: ~/.openclaw/workspace/ (MEMORY.md, USER.md, SOUL.md, memory/*.md)
Erweiterungspunkte:
- Skills (
~/.openclaw/skills/) — Markdown-Anleitungen + Scripts - Cron Jobs — Zeitgesteuerte systemEvents oder isolated agentTurns
- Sub-Agents — sessions_spawn für parallele Arbeit
- Exec — Beliebige Shell-Commands (Python, Node, curl, etc.)
| # | Feature | Integration | Aufwand | RAM-Impact |
|---|---|---|---|---|
| 1 | Personal CRM | Skill + SQLite + Cron | 2-3 Tage | ~50MB |
| 2 | Knowledge Base | Skill + SQLite + Embeddings API | 3-5 Tage | ~100MB |
| 3 | Content Pipeline | Workspace File + Skill | 1 Tag | ~0MB |
| 4 | Twitter/X Research | Skill + API Keys | 1-2 Tage | ~20MB |
| 5 | YouTube Analytics | Skill + YouTube API + Cron | 2 Tage | ~30MB |
| 6 | Business Briefing | Cron (isolated agentTurn) | 1-2 Tage | ~0MB (aber ~$2-5/Nacht API) |
| 7 | CRM NL Interface | Skill | 1 Tag | ~0MB |
| 8 | AI Humanizer | SOUL.md Update | 10 Min | 0MB |
| 9 | Image Gen | ✅ Bereits implementiert | — | — |
| 10 | Task Management | Skill + Todoist/Linear API | 1-2 Tage | ~0MB |
| 11 | Cost Tracker | Log-Parsing oder Webhook | 1-3 Tage | ~10MB |
~/.openclaw/
├── skills/
│ ├── crm/ # Personal CRM
│ │ ├── SKILL.md
│ │ ├── ingest.py # Gmail/Calendar → SQLite
│ │ ├── search.py # Semantic Contact Search
│ │ └── requirements.txt
│ ├── knowledge-base/ # RAG Knowledge Base
│ │ ├── SKILL.md
│ │ ├── ingest.py # URL → Chunk → Embed → Store
│ │ ├── search.py # Query → Cosine Search → Answer
│ │ └── requirements.txt
│ ├── twitter-research/ # Social Media Research
│ │ ├── SKILL.md
│ │ └── research.py
│ └── cost-tracker/ # Usage & Cost Tracking
│ ├── SKILL.md
│ ├── parse_logs.py # Parse OpenClaw session logs
│ └── report.py # Generate cost reports
├── workspace/
│ ├── crm/contacts.db # CRM SQLite
│ ├── kb/knowledge.db # Knowledge Base SQLite
│ └── costs/usage.jsonl # Cost tracking log
Aktuell (7.9/11.4 GB + 7GB Swap):
Docker Daemon: 784 MB
OpenClaw Gateway: 674 MB
Dokploy: 757 MB
Andere Container: ~2.5 GB
System: ~3.2 GB
Mit allen Features (+180 MB):
+ CRM (SQLite + Python): ~50 MB
+ Knowledge Base: ~100 MB
+ Twitter Research: ~20 MB
+ Cost Tracker: ~10 MB
| Feature | API-Kosten/Monat | Anmerkung |
|---|---|---|
| CRM Ingestion | ~$0.50 | 1× täglich, Gemini Flash für Filtering |
| Knowledge Base | ~$1.00 | Embedding-Calls (Gemini free oder $0.02/1M) |
| Twitter Research | ~$5-15 | Je nach Nutzung, Tier 2 API |
| Business Briefing | ~$15 (Opus+Sonnet) | Oder ~$45 (5× Opus) pro Monat |
| Cost Tracker | ~$0 | Nur lokales Log-Parsing |
| Task Management | ~$0.50 | Gemini Flash für Extraction |
| Gesamt | ~$22-32/Monat | Ohne Business Briefing: ~$7-17 |
- AI Humanizer → SOUL.md um Rewrite-Regeln erweitern (10 Min)
- Cost Tracker v0 → Python-Script das OpenClaw Session-Logs parsed (30 Min)
- Knowledge Base v0 → Workspace-Ordner
kb/mit Markdown-Files + memory_search (15 Min)