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CF geo-optimizer

Generative Engine Optimization (GEO) — make content rank in AI search answers from ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Audits existing content, rewrites for AI citation, and produces per-engine strategy. Use when asked to "optimize for AI search", "rank in ChatGPT", "GEO audit", "improve AI citations", "rank in Perplexity", "AI Overview optimization", "AI Overview ranking", "LLM SEO", "answer engine optimization", "AEO", "get cited by AI", "GEO", "generative engine optimization", "show up in ChatGPT", "appear in AI answers", "be cited by Perplexity", "SGE optimization", "Search Generative Experience", or "make my content show up in AI answers". Distinct from regular SEO — this targets generative engines, not traditional Google rankings.

nowork-studio/notfair-plugin Claude Code author: nowork-studio MIT 1 file body ≈ 62 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generative Engine Optimization (GEO) — make content rank in AI search answers from ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.

As a process F 20/100 · No process to follow — References files that are not bundled: ../../seo/geo-optimizer/WORKFLOW.md

GeneratorMarketingAI and agentsResearchtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
57
Run on models
none yet
Process rating
F
20/100
No process to follow
References files that are not bundled: ../../seo/geo-optimizer/WORKFLOW.md
Tools and files w 18
0
Steps w 15
0
Result and completion w 14
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ../../seo/geo-optimizer/WORKFLOW.md

Process rating: all ten parameters 20/100

Will not run. References files that are not bundled: ../../seo/geo-optimizer/WORKFLOW.md
  • 0Tools and files. 1 referenced file(s) missing: ../../seo/geo-optimizer/WORKFLOW.md
  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 62 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +4Description does not say when NOT to use the skill (false activations)
  • +4Structure: 1 headings, hard to scan
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -15SKILL.md body under 300 characters: nearly empty
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 17 example trigger phrases
  • +3Description length 772: enough signal without eating the budget

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.