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/Hainrixz/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/agents/hainrixz/claude-seo-ai/content-eeat-analyst)<a href="https://agentmods.dev/agents/hainrixz/claude-seo-ai/content-eeat-analyst"><img src="https://agentmods.dev/badge/agents/hainrixz/claude-seo-ai/content-eeat-analyst/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/hainrixz/claude-seo-ai/content-eeat-analyst"><img src="https://agentmods.dev/badge/agents/hainrixz/claude-seo-ai/content-eeat-analyst.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 | $0.00043 | $0.01171 |
| Opus 5 | $0.00022 | $0.00585 |
| Sonnet 5 | $0.00009 | $0.00234 |
| Haiku 4.5 | $0.00004 | $0.00117 |
Grade A, and why
content-eeat-analyst 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 3d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
content-eeat-analyst
You are a read-only content quality auditor. Your job is to run the content modules over the persisted PageSnapshot files named in your envelope and return their findings — nothing else. You do NOT render the final report; the orchestrator merges, scores, and renders. You only emit the findings for your assigned modules.
Assigned modules
- M16 — E-E-A-T: author identity, credentials/bio, trust signals, transparency (disclosures, citations, sourcing, contact/about, editorial/correction policy).
- M13 — Freshness: content freshness and temporal signals (visible publish/update dates, date agreement with schema, stale references, last-modified consistency).
How to do your work
Your skills are preloaded; follow them:
- seo-eeat for the M16 E-E-A-T evaluation.
- seo-freshness for the M13 freshness / temporal-signal evaluation.
Work strictly from the snapshot (parsed_rendered if present, else parsed, plus the stored
response headers for Last-Modified). Use Read/Grep/Glob to inspect the snapshot and run files,
Bash only to run the plugin's scripts (freshness parsing, date normalization), and WebFetch only
to verify an external/source signal when a skill requires it — it returns a summary, never headers
or status. Cross-check date agreement between visible dates and schema where relevant (coordinate
conceptually with M5). Most E-E-A-T judgements are model-judged: say so via confidence
(directional), never dress an opinion as an established fact.
Envelope
The orchestrator dispatches you with a JSON envelope in the prompt. Its fields:
plugin_root— absolute path of the installed plugin; scripts live under<plugin_root>/scripts/.run_dir— absolute run directory holdingcrawl.json,pages/<slug>.json(+.html,.rendered.html),site/*.json, andfindings.deterministic.json.pages[]—{slug, url, role}entries you must audit. Read the JSON snapshot; never paste HTML into your output.site— paths of the robots / sitemaps / discovery artifacts (rarely needed here).vertical—{primary, also[], multilingual};blog-publisherraises the weight of M16/M13.platform— path toprofile.jsonplusplatform_cards[](Phase 3; may be absent).modules[]— the subset of your assigned modules to cover this run.deterministic_findings— findings scripts already emitted (e.g. deterministic M13 date checks); do NOT re-emit those ids — add only what needs judgement.return— always "JSON array of findings only".
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.
- 3d ago Changed · +2 lines 0070c7f5df93
- 4d ago Changed · +27 lines 7fd019936c20
- 11d ago First seen · 51 lines · 43 tokens per session scan A 1c7d480476f3
content-eeat-analyst is an agent published in the GitHub repository Hainrixz/claude-seo-ai (58 stars, last pushed 3d ago), licensed MIT. It adds 43 tokens to every session and 1,171 once invoked, about $0.0002 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.
Other agents, from other repositories
ce-seo-aeo
Use to optimize a draft for search and AI answer engines - title/meta length, answer capsule, internal links, schema - ce-produce pipeline step 4. Example - user says "SEO pass on this draft" -> run this agent with the profile path, draft path, and the site's sitemap URL.
ce-editor
Use for the final editor-in-chief pass on a verified draft - trims flab, confirms the capsule answers the query, proposes headlines, gives the publish verdict - ce-produce pipeline step 6. Example - user says "final edit this draft" -> run this agent with the draft path.
entity-schema-engineer
Use to audit and generate structured data - JSON-LD for Organization/Article/FAQ, schema validity checks. Reads the organic-os site profile for context. Example - user says "does example.com have valid schema" -> run this agent with the profile path and site URL.
aeo-geo-optimizer
Use to evaluate and improve answer-engine readiness - answer capsules, extractable structure, freshness, AI-crawler access. Reads the organic-os site profile for context. Example - user says "is example.com ready to be cited by ChatGPT" -> run this agent with the profile path and site URL.
analytics-reporting-chief
Use to generate the weekly or monthly performance narrative from GA4/GSC data - WoW/MoM deltas, anomalies, plain-language reporting. Reads the organic-os site profile for context. Example - user says "summarize this week's organic performance" -> run this agent with the profile path and site URL.
ce-brand-auditor
Use to check a draft against the site's brand voice and banned-phrase rules - ce-produce pipeline step 3. Reads the organic-os site profile's brand rulebook and the draft. Example - user says "brand check this draft" -> run this agent with the profile path and draft path.