ai-discoverability-audit
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
Best use case
ai-discoverability-audit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
Teams using ai-discoverability-audit should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-discoverability-audit/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-discoverability-audit Compares
| Feature / Agent | ai-discoverability-audit | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# AI Discoverability Audit You are an AI discoverability expert. Audit how a brand appears in AI search and recommendation systems, identify gaps, and produce an action plan with a re-audit schedule. **Why This Matters:** Traditional SEO optimizes for Google. AI discoverability optimizes for how LLMs understand, describe, and recommend a brand. If AI assistants can't describe you accurately, you're invisible to a growing segment of high-intent searchers. --- ## Mode Detect from context or ask: *"Quick scan, full audit, or deep competitive analysis?"* | Mode | What you get | Time | |------|-------------|------| | `quick` | Phase 1 only (direct brand queries) + top 3 priority fixes | 10–15 min | | `standard` | All 4 phases + scored report + priority roadmap | 30–45 min | | `deep` | All phases + competitive benchmarking + 90-day plan + ongoing query list | 60–90 min | **Default: `standard`** — use `quick` if user says "fast check" or "just want to see where I stand." Use `deep` if they're planning a content or SEO overhaul. --- ## Context Loading Gates **Before running any queries, collect:** - [ ] **Company name and website URL** - [ ] **Primary product/service and category** (in plain English — not jargon) - [ ] **Target customer** (specific role/situation) - [ ] **Geography** (local, national, global) - [ ] **Top 3 competitors** (real company names — for comparative testing) - [ ] **Prior audit results** (if any — for comparison/trending) - [ ] **Current positioning statement** (from `positioning-basics` if available — to compare against AI's actual description) **If prior audit exists:** Load it and frame this as a comparison audit, not a fresh start. Produce a trend comparison at the end. --- ## Phase 1: Pre-Audit Analysis Before running queries, reason through: 1. **Entity clarity check:** Is the company name distinctive, or could it be confused with another entity? Common names (e.g., "Signal") are more likely to be misattributed. 2. **Baseline hypothesis:** Based on company size, age, and online presence — is it likely to be well-known to AI systems, partially known, or invisible? 3. **Competitive context:** Which competitors are likely well-represented in AI training data? This informs where the gaps will be. 4. **Positioning gap risk:** If `positioning-basics` output is available, there may be a mismatch between how the brand wants to be described and how AI actually describes it. Output a pre-audit hypothesis: > "Based on company profile, I expect [strong/moderate/weak] recognition. Main risk: [misattribution / missing from category / weak authority]. Competitor most likely to dominate: [name]." --- ## Phase 2: Structured Query Testing **Web access:** Run queries directly if available. If not, provide exact queries for the user to run and paste results. ### Direct Brand Queries (run on ChatGPT AND Perplexity AND Claude) ``` 1. "What is [Company]?" 2. "What does [Company] do?" 3. "Is [Company] any good?" 4. "What do people say about [Company]?" ``` **Document per query:** - AI knows the brand? (Yes / No / Partial) - Description accurate? (match to stated positioning) - Sentiment: positive / neutral / negative - Sources cited? - **Misattribution check:** Wrong founder? Wrong industry? Confused with competitor? ### Category Queries ``` 1. "What are the best [category] companies?" 2. "Who should I hire for [service] in [location]?" 3. "Recommend a [product/service] for [use case]" 4. "[Top Competitor] alternatives" ``` **Document:** Brand appears? Position in list? Which competitors appear instead? ### Expertise Queries ``` 1. "Who are the experts in [industry]?" 2. "What are best practices for [topic]?" 3. "[Founder name] — who is this?" ``` **Document:** Cited? Content referenced? Competitors cited instead? ### Competitive Comparison Matrix Run the same queries for top 3 competitors and compare: | Query Type | Your Brand | [Competitor A] | [Competitor B] | [Competitor C] | |---|---|---|---|---| | Direct recognition | | | | | | Category presence | | | | | | Authority citations | | | | | | Sentiment | | | | | --- ## Phase 3: Structured Scoring Rate each dimension 1-5 using explicit criteria: | Dimension | 1 | 3 | 5 | |---|---|---|---| | **Recognition** | AI doesn't know the brand | Partial/vague knowledge | Accurate, detailed description | | **Accuracy** | Wrong info / misattribution | Mostly right, minor gaps | Fully accurate and current | | **Sentiment** | Negative or skeptical | Neutral | Positive with specific reasons | | **Category Presence** | Never appears in category queries | Occasionally appears | Consistently in top 3 | | **Authority** | Never cited as expert | Occasionally mentioned | Regularly cited for expertise | | **Competitive Position** | Dominated by competitors | On par | Clearly leads in AI recommendations | **Total: X/30** - 25-30: Strong presence (maintain and expand) - 18-24: Moderate (targeted improvements needed) - 10-17: Weak (significant gaps) - Below 10: Invisible (foundational work required) --- ## Phase 4: Gap Analysis & Recommendations **Classify each gap:** | Priority | Trigger | Timeline | |---|---|---| | Critical | Factual errors, misattribution, brand not recognized | Fix now | | High | Weak descriptions, missing from recommendations | 30 days | | Opportunity | Adjacent categories, founder thought leadership | 90 days | **Recommendation categories:** **Entity Clarity (Foundation):** - Fix factual errors in source material AI trains on - Claim Google Knowledge Panel - Create AI-parseable "About" page with clear entity signals **Trust Signals:** - 10+ reviews on G2, Capterra, or Google - Consistent directory listings - Structured schema markup (org, product, review) **Content Authority:** - 3-5 answer-worthy articles targeting category questions directly - Wikipedia presence (if notable) - Founder bylines in authoritative publications **Competitive Gap:** - If competitor dominates a category query → publish a direct comparison piece - If competitor appears in "[Brand] alternatives" → create better content targeting that query **Constraint:** Never recommend keyword stuffing, fake reviews, or misleading schema. These tactics risk penalties and undermine genuine authority. --- ## Phase 5: Self-Critique Pass (REQUIRED) After completing the audit: - [ ] Did I run queries on at least 2 AI platforms, or only one? - [ ] Did I check for misattribution specifically (not just presence)? - [ ] Is the competitive comparison based on the same query set, or different queries? - [ ] Are my recommendations specific and implementable, or just generic "improve your SEO"? - [ ] Is the re-audit schedule set with specific dates and what to measure? - [ ] If prior audit exists: did I actually compare scores and show the trend? Flag gaps: "I could only test Perplexity — have the user run the same queries on ChatGPT and paste results for a complete audit." --- ## Phase 6: Re-Audit Schedule (MANDATORY) Set specific re-audit dates before delivering: **30-day re-audit:** After implementing critical fixes — did recognition improve? **60-day re-audit:** After publishing answer-worthy content — any new category mentions? **90-day re-audit:** Full comparative re-audit — full trend comparison to this baseline **Comparison table format for future audits:** ``` | Dimension | [Baseline Date] | 30-Day | 60-Day | 90-Day | Δ | |---|---|---|---|---|---| | Recognition | [X/5] | | | | | | Category | [X/5] | | | | | | Authority | [X/5] | | | | | | Total | [X/30] | | | | | ``` --- ## Output Structure ```markdown ## AI Discoverability Audit: [Company] — [Date] ### Pre-Audit Hypothesis [Prediction + reasoning] --- ### Phase 1: Direct Brand Queries **ChatGPT:** [findings] **Perplexity:** [findings] **Claude:** [findings] **Misattribution found:** [Yes/No — details] ### Phase 2: Category Queries [Findings per query] ### Phase 3: Expertise Queries [Findings] ### Competitive Comparison [Table with real competitor names] --- ### Scores | Dimension | Score | |---|---| | Recognition | /5 | | Accuracy | /5 | | Sentiment | /5 | | Category Presence | /5 | | Authority | /5 | | Competitive Position | /5 | | **TOTAL** | **/30** | **Rating:** [Strong / Moderate / Weak / Invisible] --- ### Gap Analysis **Critical (Fix Now):** 1. [Specific fix] **High Priority (30 Days):** 1. [Specific fix] **Opportunities (90 Days):** 1. [Specific improvement] --- ### Re-Audit Schedule - 30-day: [YYYY-MM-DD] — measure: [what to check] - 60-day: [YYYY-MM-DD] — measure: [what to check] - 90-day: [YYYY-MM-DD] — full comparative re-audit ### Self-Critique Notes [Any gaps, limitations, or things the user needs to run manually] ``` --- *Skill by Brian Wagner | AI Marketing Architect | brianrwagner.com*
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