Dejan ai reverse engineering
Skill vijaychauhanseo/seo-skills-pack/skills/dejan-ai-reverse-engineering
Portable SEO skills and curated memory for Codex, Claude Code, Agent Skills-compatible tools, and MCP-backed workflows.
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Use for AI search reverse engineering, DEJAN or Dan Petrovic research, technical SEO reverse engineering, SRO, selection rate, grounding snippets, citation mining, Google AI Mode, Gemini, AI Overviews, machine-readable content extraction, and tasks where DEJAN-style analysis should guide the workflow. This skill loads a compact local knowledge pack distilled from recent dejan.ai research and applies it to AI visibility and reverse-engineering work.
SKILL.md
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DEJAN AI Reverse Engineering
Overview
Use this skill for DEJAN-style AI SEO and search-system reverse engineering. It gives a compact working model of Dan Petrovic's recent ideas around grounding, selection, machine readability, entity bias, and model-aware visibility.
When To Use
- The user mentions
dejan.ai, Dan Petrovic, DEJAN, SRO, AI Rank, Tree Walker, grounding snippets, or selection rate. - You need DEJAN-style framing for Google AI Mode, Gemini grounding, AI Overviews, citation mining, or brand-association analysis.
- The task is generally about reverse engineering AI search, grounding, snippet extraction, or answer construction even if DEJAN is not mentioned by name.
- You want a local snapshot of recent DEJAN articles without re-reading the full archive.
Workflow
- Read references/dejan-ai-notes.md first for the core models, recent studies, and default heuristics.
- Read references/dejan-ai-archive-2025-2026.md when you need article lookup, dates, or a quick timeline.
- Read references/dejan-models-and-systems.md when the task touches AI Mode internals, browser embeddings, classifiers, fan-out models, or DEJAN's build patterns.
- Read references/dejan-legacy-technical-seo.md when the task needs older DEJAN technical SEO heuristics from the pre-AI era.
- Read references/reverse-engineering-checklist.md when the task is operational and you need a step-by-step diagnostic workflow.
- Separate sourced DEJAN claims from your own inference. Cite article title and date when leaning on a DEJAN-specific concept.
- Treat older DEJAN material as historically useful heuristics, not automatic truths. Verify time-sensitive claims against current systems.
- If the task is time-sensitive or you need newer material, browse
dejan.aiagain. This pack is a snapshot captured on 2026-03-20.
Operating Lens
- Ranking is necessary but not sufficient. Ask what sentence or claim survives into grounding.
- Track
selection rate, not just clicks or rankings. - Assume grounding budgets are limited and transient. Prioritize clear, self-contained statements near the top of the page.
- Treat model output as a mix of latent bias and retrieved evidence, not retrieval alone.
- Optimize for machine readability: distilled main content, clean blocks, explicit claims, and tool-friendly structure.
- Expect hidden fan-out or implicit sub-queries behind a visible prompt.
- For brand work, inspect associations and uncertainty, not just exact-match mentions.
- For reverse engineering, prefer artifacts over theory: snippets, chunks, citation positions, prompt variants, and source-page diffs.
Core DEJAN Concepts
SRO= Selection Rate Optimization, DEJAN's AI-native successor to CTR.Selection Rate= frequency with which a grounded candidate is chosen into the answer.Primary bias= the model's latent relevance perception before grounding effects are applied.Grounding snippets/chunks= the small text units actually supplied to the model.Transient grounding= a grounded passage may shape the answer without becoming durable model memory.LLM as presentation layer= retrieval and ranking still sit underneath the model layer.Association networksandTree Walker= probes for brand adjacency, token confidence, and model worldview.