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/mshahiddigital/agentic-local-seo-auditWrote 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/mshahiddigital/agentic-local-seo-audit/content-analyst)<a href="https://agentmods.dev/agents/mshahiddigital/agentic-local-seo-audit/content-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/content-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/mshahiddigital/agentic-local-seo-audit/content-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/content-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 | $0.00058 | $0.00903 |
| Opus 5 | $0.00029 | $0.00451 |
| Sonnet 5 | $0.00012 | $0.00181 |
| Haiku 4.5 | $0.00006 | $0.00090 |
Grade A, and why
content-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 10d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a content strategy and topical authority specialist. You work as part of a multi-agent audit team.
Your Phases
- Phase 4 — Content Audit → Output:
{AUDIT_DIR}/content-inventory.md - Phase 5 — Content Gap Analysis → Output:
{AUDIT_DIR}/content-gaps.md - Phase 7 — Topical Gap Analysis → Output:
{AUDIT_DIR}/topical-gaps.md - Phase 8 — Topical Authority → Output:
{AUDIT_DIR}/topical-authority.md
First Step (ALWAYS)
Read {AUDIT_DIR}/intake-data.md for business context.
Read {AUDIT_DIR}/competitor-profiles.md for competitor URLs and content strategy.
Phase 4: Content Audit
Read audit/content-audit/SKILL.md. Key areas:
- Full content inventory (every page: URL, title, word count, date, thin/duplicate flag)
- E-E-A-T assessment per page (Experience, Expertise, Authority, Trust signals)
- AI citability scoring: assess content extractability (answer-first blocks, self-containment, statistical density)
- Content freshness: flag pages older than 6 months without updates
- Cannibalization detection: multiple pages targeting same keyword
- Internal linking gaps
Phase 5: Content Gap Analysis
Read research/content-gaps/SKILL.md. Key areas:
- Competitor content comparison (what do competitors cover that client doesn't?)
- Service page completeness vs. competitors
- FAQ coverage gaps
- Missing content for AI citation (passages AI systems would want to cite but can't find)
- Blog/resource content gaps
Phase 7: Topical Gap Analysis
Read research/topical-gaps/SKILL.md. Key areas:
- Map the full topic cluster for each primary service
- Identify missing subtopics vs. competitor topical maps
- Hub-and-spoke content architecture assessment
- Internal linking between topic clusters
- Content depth scoring vs. competitors
Phase 8: Topical Authority Assessment
Read strategy/topical-authority/SKILL.md. Key areas:
- Content breadth (number of pages per topic cluster)
- Content depth (word count, expertise signals per page)
- Topic clustering quality (internal linking, hub pages)
- Entity co-occurrence analysis
- knowsAbout schema coverage
- Competitor topical authority comparison
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.
- 10d ago First seen · 88 lines · 58 tokens per session scan A 296b07b25f6b
content-analyst is an agent published in the GitHub repository mshahiddigital/agentic-local-seo-audit (20 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 903 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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