GEO SEO Claude Code Skill Collection for AI Search Audits

The GEO SEO Claude Code skill collection gives SEO consultants and marketing teams evidence-led checks for crawler access, citability, schema, platform readiness, and a prioritized audit report from a site URL. It is a repository of reusable Claude Code capabilities, not a claim that one score or one command can prove AI-search performance.

Skills Overview

geo-seo-claude is an MIT-licensed Claude Code bundle with one geo orchestrator, 14 specialist skills, five parallel audit agents, six Python helpers, and six JSON-LD templates. Its documented /geo commands cover full and quick audits, citation readiness, AI crawler access, llms.txt, brand mentions, platform checks, schema, technical SEO, content quality, reporting, prospects, proposals, and monthly comparison.

Where This Skill Collection Fits

This is an AI Skills asset because it gives Claude Code users a reusable operating capability: inspect a real website, collect evidence, and produce a bounded audit artifact. It sits between a one-off SEO checklist and a human implementation plan. It can support a GEO consultant or an in-house content lead, but it does not replace access to analytics, a crawl review, editorial judgment, or approval for live changes.

The most useful route is narrow-first: run a focused command for the question already known, then use the full audit when a site needs cross-functional prioritization. That avoids treating every crawl, schema, or content concern as a reason to produce a broad report.

Who It Helps

• SEO consultants: create repeatable discovery and evidence bundles before recommending technical or editorial work.
• Marketing and content leads: check whether pages are extractable, self-contained, and accessible to the relevant crawlers before commissioning rewrites.
• Technical SEO practitioners: inspect robots directives, rendering assumptions, structured data, and headers with a GEO-specific lens.
• GEO agencies: turn a URL review into a client-facing Markdown or PDF report while keeping the underlying checks visible.

Access / Install Links

• Repository: zubair-trabzada/geo-seo-claude
• Install guide: follow the README Quick Start for macOS/Linux or Git Bash on Windows.
• Command reference: review commands-reference.md before choosing a command.
• Architecture and inventory: inspect skills-and-agents.md.
• License: the repository is published under the MIT License.

The documented prerequisites are Claude Code CLI, Git, Python 3.8+ and, on Debian or Ubuntu, python3-venv. The installer creates a dedicated virtual environment under the Claude skill directory; optional uv can speed dependency setup and optional Playwright supports screenshots. Review the installer and dependencies in your own environment before running it, especially when a client URL or local prospect data is involved.

Skills Included

The collection has 15 installable entry points when the geo orchestrator is counted with its 14 specialist skills. Use the matrix to choose a narrow job first; a full audit is for coordinating evidence that already spans several roles.

Selection Logic

• 1. Start with audit evidence: choose geo, geo-audit, geo-citability, or geo-content when the decision is about what a page or site actually needs.
• 2. Check technical discoverability: choose crawlers, llms.txt, schema, or technical review when access, markup, rendering, or site foundations are the concern.
• 3. Assess external visibility: use brand mentions or platform optimization when the question is how an entity appears across specific AI-search surfaces.
• 4. Package confirmed evidence: use report, PDF, or comparison skills only after the source findings have been checked by a human owner.
• 5. Manage agency operations separately: use prospect, proposal, and update capabilities only when permissions, local data handling, and commercial review are already in place.

Setup Steps

1. Read the repository’s install guidance and confirm that Claude Code CLI, Git, Python, and the environment constraints are acceptable.
2. Install using the documented script or manual clone path, then open Claude Code in the target project or audit workspace.
3. Start with a public, authorized URL and run a narrow command such as /geo quick https://example.com.
4. Read the generated evidence before selecting a deeper audit, an implementation task, or a client report.
5. Keep a human approval step before changing robots rules, schema, site copy, reporting claims, or prospect records.

Example Inputs

Replace each bracketed variable with a URL you own or are authorized to review. Start with the narrowest command that answers the decision you need to make.

/geo quick [target URL]

/geo crawlers [target URL]

/geo citability [page URL]

/geo audit [target URL]

Expected Outputs

Depending on the selected command, expect a terminal snapshot or a named Markdown artifact such as an audit report, crawler map, llms.txt analysis, schema report, content analysis, or client report. The full audit is documented to coordinate five parallel agents and consolidate their results into a composite score, severity list, and staged action plan. These outputs are decision support: they should lead to an evidence review, not automatic deployment.

Use Cases

• Content refresh triage: identify pages whose answer structure or cited evidence needs editorial review before a rewrite sprint.
• AI crawler policy review: map current directives and rendering constraints for a technical SEO handoff.
• Schema implementation brief: inspect live markup and create a human-reviewed template shortlist for developers.
• Agency discovery: turn an authorized prospect domain into a transparent audit scope and a report that separates observed facts from recommended actions.

Limitations

• The repository is designed for Claude Code; it is not a drop-in install for every coding agent.
• Scores, correlations, and platform checklists do not promise rankings, citations, traffic, or revenue.
• Public fetching can miss login-gated content, client-side behavior, analytics context, or business constraints.
• CRM and report features can handle sensitive client information, so teams should review local storage, permissions, and retention before use.
• Generated recommendations require human validation before production edits, especially for robots, schema, content claims, and client proposals.

Common Mistakes & Fixes

• Starting with the full audit for a narrow question: run the focused crawler, schema, or citability command first and broaden only if the findings require it.
• Treating a generated template as verified site data: compare every recommended robots rule, schema field, and business fact with the live site and policy owner.
• Turning a report into an automatic implementation queue: assign an owner, required evidence, and approval step before any production change.

Related Tools / Prompts

Pair this collection with an editorial workflow only after it identifies a concrete gap. For example, a content lead can use a website-audit prompt pack to frame a redesign review, while a development team can use a Figma-to-React workflow after the audit identifies implementation and accessibility priorities. The skill supplies inspection and prioritization; those adjacent assets help execute a specific next step.

Common Questions

• Q: What does the GEO SEO Claude Code skill collection do?
  A: It provides Claude Code commands and specialist skills for reviewing AI-search visibility signals such as citation readiness, crawler access, structured data, platform readiness, and reporting.
• Q: Is a full audit the right first command?
  A: Not necessarily. Use a focused check when the question is known, and reserve the full audit for a cross-functional review that needs one prioritized action plan.
• Q: Does it make website changes automatically?
  A: The documented commands produce analysis, templates, and reports. Treat implementation as a separate, reviewed task with the right site access and approvals.
• Q: What should a team check before installing it?
  A: Confirm Claude Code, Git, Python requirements, local data handling, the installer behavior, and authorization for every URL or client record used in an audit.

Install or adapt this skill collection for an authorized audit workflow? Share what you build in the comments.

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Big Prompt Hub Review

This collection is most useful when a team needs a repeatable way to separate evidence gathering from implementation decisions. Its modular commands give an audit a clearer scope than a generic GEO checklist, while the reports can make technical and editorial findings easier to hand off. The limits matter: source access, business policy, privacy, and human verification still determine whether a recommended change should be made.