Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Hainrixz/claude-seo-ai/plugin install claude-seo-aiWrote 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/hainrixz/claude-seo-ai/seo-freshness)<a href="https://agentmods.dev/skills/hainrixz/claude-seo-ai/seo-freshness"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-seo-ai/seo-freshness/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/hainrixz/claude-seo-ai/seo-freshness"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-seo-ai/seo-freshness.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 | $0.00057 | $0.01346 |
| Opus 5 | $0.00028 | $0.00673 |
| Sonnet 5 | $0.00011 | $0.00269 |
| Haiku 4.5 | $0.00006 | $0.00135 |
Grade A, and why
seo-freshness scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
seo-freshness (M13)
Freshness is a recency signal both classic ranking systems (Query Deserves Freshness) and AI answer engines weigh — Perplexity in particular favours recently-updated sources when citing. Date fields tie directly to Article schema (cross-check M5); see references/schema-tier1.md for the date rules.
Inputs
Work from the PageSnapshot named in your dispatch envelope: read parsed from <run_dir>/pages/<slug>.json (jsonld[], metas[] for article:published_time/modified_time) plus headers.last-modified; Grep pages/<slug>.html for verbatim evidence; site artifacts live in <run_dir>/site/{robots.json,sitemaps.json,discovery.json}. Deterministic findings already emitted by audit.mjs are listed in <run_dir>/findings.deterministic.json — do not re-emit those ids; add model-judged findings only. If invoked directly with a URL/path and no snapshot exists, first run node "${CLAUDE_PLUGIN_ROOT}/scripts/snapshot.mjs" <target> --out "${CLAUDE_PLUGIN_DATA}/runs" and use the printed snapshot path.
Audits
Working from the PageSnapshot (parsed_rendered when render.used is not none, else parsed):
- Visible dates: detect on-page "Published" / "Updated on" / "Last reviewed" patterns and their values (ISO or human-readable).
- Schema dates: parse
datePublished/dateModifiedfrom JSON-LDArticle/BlogPosting/NewsArticle. - Agreement: visible date and schema date must match; flag mismatches and schema dates with no visible counterpart (AI engines distrust hidden-only dates).
- Staleness: estimate content age (most recent reliable date) vs topic volatility — fast-moving topics (prices, tooling, "best X 2026", regulations) decay faster than evergreen reference content. Report stale, not just old.
- Pattern hygiene: "updated on" with no substantive content change is a freshness anti-pattern — note it, never recommend it.
Fixes
- AUTO (
fixable: auto): inject a missingdateModifiedinto existing Article schema as an additive diff forfix. Never backdate to a false date — use the verifiable last-change date (e.g. Last-Modified header / repo mtime / today) or leave a clearly-markedTODOplaceholder the user confirms. - PROPOSED (
fixable: proposed): surface visible-vs-schema date mismatches with the corrected value as a draft requiring per-item accept; never auto-rewrite a date the user must verify. - ADVISORY (
fixable: advisory): recommend a genuine content refresh for stale-on-volatile pages — the tool never writes editorial content. Never fabricate dates or invent an update that did not happen.
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.
- 4d ago Changed · +2 lines dca17a52659e
- 5d ago Changed · +3 lines f0f4b5e5e193
- 12d ago First seen · 39 lines · 57 tokens per session scan A 7d47d6239dae
seo-freshness is a skill published in the GitHub repository Hainrixz/claude-seo-ai (59 stars, last pushed 5d ago), licensed MIT. It adds 57 tokens to every session and 1,346 once invoked, about $0.0003 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 2026-08-30.
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