Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add JakeLabate/Claude-SEO-Skills --skill image-seo-auditgit clone --depth 1 https://github.com/JakeLabate/Claude-SEO-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jakelabate/claude-seo-skills/image-seo-audit)<a href="https://agentmods.dev/skills/jakelabate/claude-seo-skills/image-seo-audit"><img src="https://agentmods.dev/badge/skills/jakelabate/claude-seo-skills/image-seo-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jakelabate/claude-seo-skills/image-seo-audit"><img src="https://agentmods.dev/badge/skills/jakelabate/claude-seo-skills/image-seo-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00103 | $0.01669 |
| Opus 5 | $0.00051 | $0.00834 |
| Sonnet 5 | $0.00021 | $0.00334 |
| Haiku 4.5 | $0.00010 | $0.00167 |
Grade A, and why
image-seo-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image SEO Audit
Audit the images of a website and produce an actionable SEO and performance report.
When to use this skill
Use this skill when the user asks to:
- Audit or analyze the images on a website for SEO or accessibility
- Find images with missing, empty, or poor
alttext - Find images that cause layout shift (missing
width/height) or load slowly (no lazy loading, oversized files) - Identify images served in legacy formats instead of WebP/AVIF
- Find broken images or generic, uninformative filenames
Inputs to collect
Before starting, confirm with the user:
- Site URL or local files — a live site root URL (e.g.,
https://example.com), a sitemap URL, a list of URLs, or a local folder of HTML files. - Scope — full site, a specific section (e.g.,
/blog/), or a list of URLs. - Crawl limits — max pages (default 500) for live crawls.
- File checks — whether to fetch each image to measure byte size and detect broken images (the
--check-filesflag; slower but enables the oversized-file and broken-image checks). - Size budget — the byte threshold above which an image is "oversized" (default 200 KB).
Workflow
Step 1: Gather the page set and extract images
- If a sitemap is available (
/sitemap.xml), it is used to get the canonical page list. - Otherwise, the crawler starts from the homepage following only same-host links.
- For local HTML folders, all
.htmlfiles are enumerated.
Use scripts/extract_images.py to build an image inventory:
python3 scripts/extract_images.py https://example.com --max-pages 500 --check-files --output image_inventory.json
# already crawled once (e.g. in a full SEO audit)? skip the crawl and reuse the shared cache:
# python3 scripts/fetch_pages.py https://example.com --output page_cache.json
# python3 scripts/extract_images.py --from-cache page_cache.json --check-files --output image_inventory.json
For every <img> the inventory records: absolute src, alt (null if the attribute is absent vs. empty string if alt=""), width/height attributes, loading, whether it has a srcset, whether it sits inside a <picture>, and its position in the page. With --check-files, each unique image URL is HEAD-requested to record HTTP status, content type, and byte size.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 131 lines · 103 tokens per session scan A 199048b47a84
image-seo-audit is a skill published in the GitHub repository JakeLabate/Claude-SEO-Skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 103 tokens to every session and 1,669 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
seo-audit
A checklist-based SEO review for a website. SEO, or search engine optimization, is the work of improving a site so search engines can understand and rank it.
fire-your-seo-agency
A procedure for improving how a website appears in search engines and how AI answer systems find and cite it. It covers search, answer-engine, generative-AI, and Naver visibility.
geo-loop
Run one bounded eGEOagents loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, append one Timeline entry and one LOG line. Use for loop mode, /geo:loop, scheduled GEO runs, or continuous monitoring.
content-scoring
Score content against the 10 GEO criteria with evidence and prioritized fixes. Use when users ask to score, rate, evaluate, or estimate ranking strength.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
validation-doctor
Check Brave Search and Chrome DevTools MCP availability and provide exact setup snippets. Use when validation dependencies are missing or uncertain.