Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AntonioBlago/claude-code-seo-starternpx agentmods add skills/antonioblago/claude-code-seo-starter/visibly-seo-status-quoWrote 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/antonioblago/claude-code-seo-starter/visibly-seo-status-quo)<a href="https://agentmods.dev/skills/antonioblago/claude-code-seo-starter/visibly-seo-status-quo"><img src="https://agentmods.dev/badge/skills/antonioblago/claude-code-seo-starter/visibly-seo-status-quo/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/antonioblago/claude-code-seo-starter/visibly-seo-status-quo"><img src="https://agentmods.dev/badge/skills/antonioblago/claude-code-seo-starter/visibly-seo-status-quo.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 | $0.00072 | $0.01113 |
| Opus 5 | $0.00036 | $0.00557 |
| Sonnet 5 | $0.00014 | $0.00223 |
| Haiku 4.5 | $0.00007 | $0.00111 |
Grade A, and why
visibly-seo-status-quo 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Status-Quo Analysis
Establish, with live data only, where a domain stands organically in a target market today. This is the foundation every later phase builds on — so every fact here must be verified, not assumed.
Tier note. Steps 1–2 use Visibly AI's Google tools (
query_search_console,list_projects), which need a Visibly key (pro tier — GSC/GA run at 0 credits once Google is connected). No key? Skip to Step 3: export Search Console (Performance → Queries → CSV) yourself and feed that CSV to the Python template in Step 4 — the cross-reference, classification and quick-win logic all run locally and keyless. Setup tiers:docs/setup.md§3.
Step 1 — Discover what's wired
mcp__visiblyai__list_projects— find the project matching the domain.mcp__visiblyai__get_google_connections— confirm Search Console (and GA) are connected.
If no project/connection exists, stop and tell the user what to connect first.
Step 2 — Pull live GSC performance
mcp__visiblyai__query_search_console—dimension=query, target-country filter,limit=500. This is the ground truth: clicks, impressions, CTR, average position.- Repeat with
dimension=pageto find URLs with impressions but weak clicks (underperforming pages = on-page/intent-mismatch candidates).
Step 3 — Load the client's target keywords
- Read the client's keyword file (
*.xlsx/*.csv) with pandas. These are the keywords the business cares about — often different from what it actually ranks for. That gap is where the strategy lives.
Step 4 — Cross-reference and classify
- Map each target keyword onto the live GSC row (clicks, impressions, CTR, position).
- Classify on two axes:
- Type: Brand · Generic · Competitor
- Ranking bucket: Top 3 · Page 1 (4-10) · Page 2 (11-20) · Weak (21-50) · Not Ranking (50+/none)
Don't do this join by hand. Save the GSC export (dimension=query) as CSV/XLSX next to
the client's keyword file and run the Python template — it does the cross-reference,
both classifications and the quick-win flag deterministically:
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 · 86 lines · 72 tokens per session scan A dd5aff1d5f62
visibly-seo-status-quo is a skill published in the GitHub repository AntonioBlago/claude-code-seo-starter (3 stars, last pushed 6d ago), licensed MIT. It adds 72 tokens to every session and 1,113 once invoked, about $0.0004 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.
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