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AC seo-image-gen

AI image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics. Powered by Gemini via nanobanana-mcp. Requires banana extension installed. Use when user says "generate image", "OG image", "social preview", "hero image", "blog image", "product photo", "infographic", "seo image", "create visual", "image-gen", "favicon", "schema image", "pinterest pin", "generate visual", "banner", or "thumbnail".

AgriciDaniel/claude-seo Claude Code author: AgriciDaniel MIT 2 files body ≈ 2 109 tokens Open the sourcegithub.com↗ analyzed 6 d ago

AI image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics.

As a process C 61/100 · Has gaps — References files that are not bundled: references/prompt-adaptation-safety.md

GeneratorMarketingtype 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
61/100
Has gaps
References files that are not bundled: references/prompt-adaptation-safety.md
Tools and files w 18
0
When it triggers w 12
20
Result and completion w 14
60
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: seo-image-gen (AgriciDaniel/claude-seo)

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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/prompt-adaptation-safety.md

Process rating: all ten parameters 61/100

Will not run. References files that are not bundled: references/prompt-adaptation-safety.md
  • 0Tools and files. 1 referenced file(s) missing: references/prompt-adaptation-safety.md
  • 20When it triggers. No condition that starts the skill
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 38 steps, 2 vague phrases
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2109 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (6 tags): a typed call is more reliable

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)
  • +2Single-language instructions
  • +5Description quotes 16 example trigger phrases
  • +3Description length 463: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 38 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.