open-seo: Skill for Claude Code

.agents/skills/ai-visibility-audit/SKILL.md

ai-visibility-audit is a skill for Claude Code, Codex from every-app/open-seo. It costs 100 tokens per session (2,846 once invoked), scanned A, original, MIT.

An audit of how often a brand appears or is cited in answers from AI assistants about its market.

In plain words
What is it for?
Use it to review saved AI answers and produce a short list of pages to improve, third-party sites to join, or access problems to fix.
Why use it?
It shows where a brand is missing from AI answers and which pages influence those answers, so the work is based on observed results rather than guesses.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is every-app/open-seo's own configuration. It tells Claude Code and Codex how to work on open-seo itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything open-seo configures →

About the project

OpenSEO is an open-source SEO platform for keyword research, rank tracking, competitor analysis, backlink analysis, site audits, and AI visibility work. It connects SEO data to AI agents through an MCP server and reusable agent skills, while allowing users to supply their own DataForSEO API key and self-host the tool. Catalogue add-ons guide agents through OpenSEO's SEO workflows.

every-app/open-seo · 23,850 stars · on GitHub · openseo.so

Reuse

Borrowing it

Nothing to install: this file belongs to every-app/open-seo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/every-app/open-seo/main/.agents/skills/ai-visibility-audit/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/every-app/open-seo

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for ai-visibility-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/every-app/open-seo/ai-visibility-audit/github.svg)](https://agentmods.dev/skills/every-app/open-seo/ai-visibility-audit)
Your own site
<a href="https://agentmods.dev/skills/every-app/open-seo/ai-visibility-audit"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/ai-visibility-audit/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.

agentmods 80×15 button for ai-visibility-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/every-app/open-seo/ai-visibility-audit"><img src="https://agentmods.dev/badge/skills/every-app/open-seo/ai-visibility-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,846 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 9 Oct 2026
  • Snyk warn 9 Oct 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00100 $0.02846
Opus 5.5 $0.00040 $0.01138
Sonnet 5.5 $0.00020 $0.00569
Haiku 4.5 $0.00010 $0.00285

Measured 8d ago against content hash 36bae0bf1101, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-visibility-audit 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 8d 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.

.agents/skills/ai-visibility-audit/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Visibility Audit

Goal

Find the work most likely to get a brand named or cited in AI answers about its market, and explain it so a non-expert can act on it. Research broadly; recommend selectively. The report leads with one to three recommendations, each tied to the answers and cited pages that justify it.

This skill reads saved answers, adds the pages behind them, and decides what to do. For demand research alone, use ai-prompt-research.

Project context

The project-context tools are free and shared with the app and other agents.

  1. External MCP clients: resolve the project with list_projects, ask only if the match is ambiguous, then call get_project_context. In SAM, use the current project and context already injected into the conversation; SAM has no get_project_context tool and needs no project selection or connection setup.
  2. This skill needs business_overview, the website and the main competitors. If the overview is empty, infer it from the site, confirm it in one question, and save it with update_project_context.
  3. Read get_ai_visibility_tracker. Its prompts, competitors, engines, market and latest runs decide which path below applies. Check the tracker's own brand, the brands row with own: true. Its name is the project name. If that is not how people write the brand (for example a project named "Acme website"), answers that name the brand are not counted as mentions and brand questions are not treated as branded. Ask the user to rename the project in the app before collecting or interpreting answers.
  4. Reuse research-log findings under 30 days old for discovery. A claim that drives a recommendation still needs evidence from a run or a page read during this audit.
  5. On finish, write back the pages the report names with addKeyPages and append { updates: [{ appendResearchLog: { summary: "AI visibility audit: <run id>. Verdict: <conclusion>" } }] }.

OpenSEO MCP tools

  • get_ai_visibility_results, get_ai_visibility_sources, get_ai_visibility_answer: the evidence. Read one run-wide results call, one run-wide sources call with the same runId, then answers you need to verify.
  • get_ai_visibility_trend: whether visibility is moving, when the tracker has comparable history. Never compute a trend yourself from separate result calls.
  • research_ai_visibility_prompts: the questions around a prompt group and the sources ChatGPT cites for them (US English only).
  • explore_prompt: asks ChatGPT one prompt through its API and returns the answer, citations and fanOutQueries, the web searches the model ran before answering. Charged at actual usage per uncached answer; cached answers are free for seven days. Requires a paid plan in hosted mode.
  • estimate_ai_visibility_cost, save_ai_visibility_tracker, set_ai_visibility_schedule, run_ai_visibility_check, get_ai_visibility_run: only for the first-run path below.
  • get_ranked_keywords, get_serp_results, get_backlinks_overview: optional context when an owned page's search performance or authority could change a recommendation.
  • Web reading (fetch, scrape or search): robots.txt, the owned pages that should be cited, and the pages AI answers cite instead.

Read the full file on GitHub · 110 lines

Changes

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.

  1. 8d ago Changed 36bae0bf1101
  2. 10d ago First seen · 110 lines · 100 tokens per session scan A 8199e8d4eefc

Subscribe to this mod's changes

ai-visibility-audit is a skill published in the GitHub repository every-app/open-seo (23,850 stars, last pushed 3d ago), licensed MIT. It adds 100 tokens to every session and 2,846 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-07.

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