tim/SlipItIn
1
0
Fork 0
Code Pull Requests 1 Actions 1 Packages Releases Wiki Activity

Neue Skills, Referenzen & OpenWiki-Doku integriert

Browse Source 
Umfangreiche Erweiterung der Skill-Bibliothek: Neue Skills für Humanisierung (Englisch/PT-BR), Design-Validierung, AI-SEO und Coolify-Deployment inkl. Regelwerke, Presets, Pattern-Referenzen, Testfälle und Automatisierungsskripte. Zusätzliche Skills für Revenue-Centric Design, Pier Cloud, OKF, Lebenslauf- und LinkedIn-Optimierung sowie zahlreiche Referenzdateien, Checklisten und YAML/JSON/Markdown-Templates. Einführung einer vollständigen OpenWiki-Dokumentation mit Architektur-, Domain- und Workflow-Beschreibungen, zentralem Index und automatisierten Updates. Modularer Aufbau, restriktive Lizenzen und umfassende Qualitäts- und Evaluationsmechanismen für alle neuen Inhalte.
This commit is contained in:
Tim Krampitz
2026-07-26 14:00:58 +02:00
parent 070727d5cd
commit 01046b01e4
202 changed files with 31290 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
.github/skills/geo-optimization/references/guiding-principles.md vendored Normal file
View File

@@ -0,0 +1,16 @@
# GEO Guiding Principles
Core principles that guide all GEO optimization decisions.
## Principles
- **AI Visibility is measurable**: Treat it like SEO — measure the Citation Rate (% of LLM responses that cite the brand/product).
- **Terminology drives retrieval**: Align titles and headings with the natural language users use when querying LLMs, not just Google keyword patterns.
- **Authority beats volume**: One high-signal, well-structured page outperforms dozens of thin or weak pages.
- **Authentic community is the new gold**: Real reviews and discussions on Product Hunt, Reddit, and Quora are the strongest trust signals for LLMs.
- **Models are volatile**: Model updates change citation patterns — monitor continuously and adjust.
- **Traditional SEO + Structured Data are still the foundation**: JSON-LD schemas (FAQPage, Product) remain essential for LLM retrieval.
## Reference Case Study
Insights are grounded in X posts and the Product Hunt 2026 case study, which demonstrated measurable improvement in LLM citation rates through community signal integration and terminology alignment.