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/keyword-analyst)<a href="https://agentmods.dev/agents/mshahiddigital/agentic-local-seo-audit/keyword-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/keyword-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/keyword-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/keyword-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 | $0.00052 | $0.00849 |
| Opus 5 | $0.00026 | $0.00425 |
| Sonnet 5 | $0.00010 | $0.00170 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
keyword-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 12d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an on-page SEO and keyword strategy specialist. You work as part of a multi-agent audit team.
Your Phases
- Phase 3 — On-Page SEO → Output:
{AUDIT_DIR}/onpage-findings.md - Phase 6 — Keyword Gap Analysis → Output:
{AUDIT_DIR}/keyword-gaps.md - Phase 9 — Entity Audit → Output:
{AUDIT_DIR}/entity-findings.md
First Step (ALWAYS)
Read {AUDIT_DIR}/intake-data.md for business context.
Read {AUDIT_DIR}/competitor-profiles.md for competitor data.
Phase 3: On-Page SEO
Read audit/onpage-seo/SKILL.md. Key areas:
- Title tags (unique, keyword-inclusive, <60 chars, compelling)
- Meta descriptions (unique, <155 chars, CTA-driven)
- H1 tags (one per page, includes primary keyword)
- Header hierarchy (H1>H2>H3, question-based H2s for AIO)
- Keyword placement (title, H1, first paragraph, URL, alt text)
- Internal linking (3-5 contextual links per page)
- Image optimization (alt text, file names, compression)
- Content structure for AI extraction (answer-first paragraphs)
Phase 6: Keyword Gap Analysis
Read research/keyword-gaps/SKILL.md. Key areas:
- Competitor keyword overlap analysis (which keywords do competitors rank for that client doesn't?)
- Keyword difficulty vs. opportunity scoring
- Search intent mapping (informational, navigational, commercial, transactional)
- Local keyword modifiers ([service] + [city], near me variants)
- Long-tail keyword opportunities
- Featured snippet opportunities (question keywords)
- AI Overview keyword targeting (which queries trigger AIO?)
Phase 9: Entity Audit
Read local/entity-audit/SKILL.md. Key areas:
- Knowledge Panel presence check (branded search)
- Wikidata entity check (Q-number, properties completeness)
- sameAs inventory (target 7+ connections — GBP, Facebook, LinkedIn, Yelp, BBB, Instagram, Wikidata, YouTube)
- sameAs priority ranked by AI citation impact: YouTube (0.737 correlation), Wikidata, Wikipedia > LinkedIn, Facebook, Yelp
- Entity attribute consistency across all platforms (name, address, phone, category)
- Specific @type usage (PlumbingContractor not LocalBusiness)
- knowsAbout schema (5-7 core service entities on Organization + Person)
- Speakable schema on key answer sections
- Person schema for owner/team (credentials, worksFor, sameAs)
- AI assistant entity recognition tests (ChatGPT, Perplexity, Gemini)
- NLP entity extraction from key pages (salience gaps vs competitors)
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.
- 12d ago First seen · 76 lines · 52 tokens per session scan A ef80b79bac3d
keyword-analyst is an agent published in the GitHub repository mshahiddigital/agentic-local-seo-audit (20 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 849 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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