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.
npx skills add T4wroot/agentic-seo --skill programmatic-seogit clone --depth 1 https://github.com/T4wroot/agentic-seoWrote 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/t4wroot/agentic-seo/programmatic-seo)<a href="https://agentmods.dev/skills/t4wroot/agentic-seo/programmatic-seo"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/programmatic-seo/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/t4wroot/agentic-seo/programmatic-seo"><img src="https://agentmods.dev/badge/skills/t4wroot/agentic-seo/programmatic-seo.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 | $0.00134 | $0.05828 |
| Opus 5 | $0.00067 | $0.02914 |
| Sonnet 5 | $0.00027 | $0.01166 |
| Haiku 4.5 | $0.00013 | $0.00583 |
Grade A, and why
programmatic-seo 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 7d 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
100% identical to programmatic-seo — 0 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 — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO: Programmatic SEO
Guides programmatic SEO—creating large numbers of SEO-optimized pages automatically using templates and structured data, rather than writing each page manually. Classic “mail merge” pSEO (one rigid template + swapped variables) often produced low differentiation and thin-feeling URLs. With AI used responsibly on top of the same data spine, you can scale per-URL customization—intent-aligned copy, section depth, FAQs, tone, localization—while still following evidence blocks, data tiers, and QA (see Data strength hierarchy and AI-assisted generation below).
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Project context: If .claude/project-context.md or .cursor/project-context.md exists, read product/ICP sections before proposing playbooks or page types.
Definition
Programmatic SEO = Building a single template and populating it with data from a database, API, or spreadsheet to generate hundreds or thousands of unique pages. Each page targets a long-tail keyword (e.g., "best SEO tool in [city]," "[App A] + [App B] integration").
Key differences from traditional SEO: Technical (SEOs + engineers); long-tail focus; data-driven (data quality = success); automation; built for scale.
Classic limits vs AI-enhanced differentiation
| Era | What breaks | What helps |
|---|---|---|
| Rigid pSEO | One template, minimal variance → similar titles/bodies, weak E-E-A-T, “obvious mail merge” | Still needs unique evidence per URL and selective indexation |
| AI-enhanced pSEO | Same structured rows (facts, SKUs, metrics) drive the page, but models add per-URL narrative: intros, FAQ depth, persona angles, localization, internal-link suggestions—higher differentiation at scale | Facts stay in your data layer; AI shapes phrasing and structure, not invented numbers—see AI-assisted generation |
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.
- 7d ago First seen · 320 lines · 134 tokens per session scan A e52d51768401
programmatic-seo is a skill published in the GitHub repository T4wroot/agentic-seo (15 stars, last pushed 7d ago), licensed MIT. It adds 134 tokens to every session and 5,828 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to programmatic-seo, differing in 0 lines, and is treated as a copy.
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