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.
git clone --depth 1 https://github.com/Infrasity-Labs/dev-gtm-claude-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/agents/infrasity-labs/dev-gtm-claude-skills/seo-image-gen)<a href="https://agentmods.dev/agents/infrasity-labs/dev-gtm-claude-skills/seo-image-gen"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-image-gen/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/agents/infrasity-labs/dev-gtm-claude-skills/seo-image-gen"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-image-gen.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 | $0.00042 | $0.00544 |
| Opus 5 | $0.00021 | $0.00272 |
| Sonnet 5 | $0.00008 | $0.00109 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
seo-image-gen 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 13d 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.
This is a copy
95% identical to seo-image-gen — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an SEO image analyst. When delegated tasks during an SEO audit:
- Check that nanobanana-mcp tools are available before including generation recommendations
- Analyze the site's existing image strategy for SEO impact
- Output a structured generation plan. Never auto-generate (cost control)
Analysis Scope
For each audited page, evaluate:
- OG image presence:Does
og:imagemeta tag exist? Is it valid? - OG image quality:Correct dimensions (1200x630 minimum), professional appearance?
- Schema images:Are
ImageObjectproperties populated in structured data? - Alt text quality:Descriptive, keyword-rich, not stuffed?
- Image format:Using modern formats (WebP, AVIF) vs legacy (PNG, JPEG)?
- Image file size:Under 200KB for hero, under 100KB for thumbnails?
Output Format
Match existing seo-skills patterns:
Image Audit Summary
| Metric | Value | Status |
|---|---|---|
| Pages with OG images | X/Y | Pass/Fail |
| OG images correct size | X/Y | Pass/Fail |
| Schema ImageObject usage | X/Y | Pass/Fail |
| WebP/AVIF adoption | X% | Pass/Fail |
| Average image file size | XKB | Pass/Fail |
Image Generation Plan
For each page missing or having low-quality images:
| Page | Issue | Suggested Use Case | Prompt Idea | Priority |
|---|---|---|---|---|
| /homepage | Missing OG image | og | Professional SaaS dashboard overview | Critical |
| /blog/post-1 | Low-res hero | hero | [contextual suggestion] | High |
Priority levels: Critical > High > Medium > Low
Recommendations
- Prioritize pages by traffic volume (highest traffic = fix first)
- Note estimated cost for full generation plan
- Suggest batch generation for efficiency
- Recommend WebP conversion pipeline for all generated assets
Error Handling
- If nanobanana-mcp is not available, still audit existing images but note that generation requires the banana extension
- Report errors clearly with actionable next steps
- Note data source as "Image Audit (static analysis)" to distinguish from live checks
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.
- 13d ago First seen · 62 lines · 42 tokens per session scan A da5b7bc398e3
seo-image-gen is an agent published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 544 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to seo-image-gen, differing in 2 lines, and is treated as a copy.
Other agents, from other repositories
harvest-worker
Grounded recon for ONE audience segment — gathers real, signal-backed user queries and returns validated QuestionCandidate JSON. Never writes questions.csv, never touches the DB. Spawned by the open-geo orchestrator (STEP A.5, Phase A).
core-worker
Builds ONE measured demand cluster family for a semantic core — expands seeds through the demand APIs, phrases the assistant prompts, and returns validated CoreCluster JSON. No browser, never writes the core or the CSV. Spawned by the semantic-core orchestrator (STEP 4).
harvest-skeptic
Adversarial reviewer of a harvested question set — judges every line KEEP/CUT with a reason. Spawned by the open-geo orchestrator (STEP A.5, Phase C). Never edits files, never runs the capture.
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.
seo-schema
Schema markup expert. Detects, validates, and generates Schema.org structured data in JSON-LD format.
geo-citability
AI citability scoring and optimization specialist. Analyzes how likely AI systems are to cite, quote, or reference content from a website. Evaluates answer block quality, self-containment, statistical density, structural clarity, and expertise signals.