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 lhitches/claude-seo-skills --skill topical-authority-mapgit clone --depth 1 https://github.com/lhitches/claude-seo-skillsWrote 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/lhitches/claude-seo-skills/topical-authority-map)<a href="https://agentmods.dev/skills/lhitches/claude-seo-skills/topical-authority-map"><img src="https://agentmods.dev/badge/skills/lhitches/claude-seo-skills/topical-authority-map/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/lhitches/claude-seo-skills/topical-authority-map"><img src="https://agentmods.dev/badge/skills/lhitches/claude-seo-skills/topical-authority-map.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 | $0.00110 | $0.01052 |
| Opus 5 | $0.00055 | $0.00526 |
| Sonnet 5 | $0.00022 | $0.00210 |
| Haiku 4.5 | $0.00011 | $0.00105 |
Grade A, and why
topical-authority-map 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are building a Topical Authority Map. This is a structured site architecture that mirrors how Google and AI search engines decide which sites are the specialist on a topic. It is NOT a keyword list. It is NOT a list of topics. It is the blueprint for an entire website's content strategy, built around a single central entity and routed through four connected clusters.
The four clusters:
- Core Pages (the money pages users buy from)
- AOR Pages (Areas of Relevance, the supporting authority articles)
- Linking Map (which page links to which page, with what anchor text)
- Buyer Journey (every page tagged TOFU, MOFU, or BOFU)
Plus a fifth output: a 12-week Publishing Order with content briefs that turns the map into a content plan.
Output: Two deliverables.
.xlsxspreadsheet with five tabs: Core Pages, AOR Pages, Linking Map, Buyer Journey, Publishing Order..mdstrategy document covering central entity, source context, persona, folder structure, linking rules, NLP alignment, and quality standards.
Mode: Interactive. Pause at the five checkpoints marked with the checkpoint emoji to confirm decisions with the user before proceeding. Do not skip checkpoints.
Why topical maps work
Topical maps work because of five mechanics, not because of E-E-A-T marketing:
- Historical user data accumulation. Every visitor adds data to Google's view of your site. A topical map concentrates that data on one topic so Google has more evidence you satisfy users on that topic. Spread the data across four unrelated topics and you lose on all four.
- Knowledge Graph alignment. Google has entities with attributes and relationships. Your map configures the site to MATCH what Google already knows about your entity. Match the structure and Google retrieves your pages cheaply.
- Lower cost of retrieval. Every title, heading, paragraph, and image should map to a real query. Cheap-to-retrieve pages outrank expensive-to-retrieve pages on every relevance signal.
- Satisfaction signal transfer. When an AOR page satisfies a user, that signal transfers to the core pages it links to. More AOR pages, more satisfaction signals, stronger core pages.
- Index construction efficiency. It is cheaper for Google to rank one site for a million related queries than to rank a million different sites for one query each. Deep architecture lets Google serve the whole topic from your domain.
Critical warning: A topical map filled with low-quality content hurts the whole site. Bad pages drag every other page down. Do not publish to fill the map. Every article must be quality work. If you cannot write a good article on a topic, leave it out.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 69 lines · 110 tokens per session scan A dec53b7c07d1
topical-authority-map is a skill published in the GitHub repository lhitches/claude-seo-skills (10 stars, last pushed 5d ago), licensed MIT. It adds 110 tokens to every session and 1,052 once invoked, about $0.0006 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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