seo-freshness

seo-freshness is a skill for Claude Code from Hainrixz/claude-seo-ai. It costs 57 tokens per session (1,346 once invoked), scanned A, original, MIT.

A website audit and repair guide for freshness signals, such as published and updated dates shown on a page and in its structured data. Structured data is machine-readable information that helps search engines understand a page.

In plain words
What is it for?
Use it to compare visible dates with schema dates, assess content age, flag freshness problems, and add an honest update date when appropriate.
Why use it?
It finds mismatched, missing, or misleading dates and checks whether content appears stale for its subject.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the claude-seo-ai plugin — 34 skills, 5 agents, 1 hook shipped together

Good fit Use it to compare visible dates with schema dates, assess content age, flag freshness problems, and add an honest update date when appropriate.

Compare 6 skills from other repositories ↓
Install

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.

Claude Code
/plugin marketplace add Hainrixz/claude-seo-ai
Claude Code
/plugin install claude-seo-ai

Made for: Claude Code.

Or install claude-seo-ai, the plugin that ships this one along with the rest of its 34 skills, 5 agents, 1 hook.

Wrote 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.

agentmods badge for seo-freshness

README.md
[![agentmods](https://agentmods.dev/badge/skills/hainrixz/claude-seo-ai/seo-freshness/github.svg)](https://agentmods.dev/skills/hainrixz/claude-seo-ai/seo-freshness)
Your own site
<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.

agentmods 80×15 button for seo-freshness

Your own site · 80×15
<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>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,346 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash dca17a52659e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/seo-freshness/SKILL.md · 44 lines

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):

  1. Visible dates: detect on-page "Published" / "Updated on" / "Last reviewed" patterns and their values (ISO or human-readable).
  2. Schema dates: parse datePublished/dateModified from JSON-LD Article/BlogPosting/NewsArticle.
  3. Agreement: visible date and schema date must match; flag mismatches and schema dates with no visible counterpart (AI engines distrust hidden-only dates).
  4. 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.
  5. 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 missing dateModified into existing Article schema as an additive diff for fix. Never backdate to a false date — use the verifiable last-change date (e.g. Last-Modified header / repo mtime / today) or leave a clearly-marked TODO placeholder 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.

Read the full file on GitHub · 44 lines

Changes

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.

  1. 4d ago Changed · +2 lines dca17a52659e
  2. 5d ago Changed · +3 lines f0f4b5e5e193
  3. 12d ago First seen · 39 lines · 57 tokens per session scan A 7d47d6239dae

Subscribe to this mod's changes

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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