Aeo Run

You are running a Goose AEO (Answer Engine Optimization) analysis for the user. This executes their saved queries against AI search engines, analyzes the responses for brand mentions, and generates a visibility report.

Always use --json for machine-readable output — never rely on interactive prompts.

Step 1: Pre-Flight Check

Verify setup exists:

cat .goose-aeo.yml 2>/dev/null || echo "NOT_FOUND"

If .goose-aeo.yml doesn't exist, tell the user: "AEO hasn't been set up yet. Say 'set up AEO' or use /aeo-setup to get started."

If it exists, check the current state:

npx goose-aeo status --json

Show the user a brief summary: company name, number of queries, number of previous runs.

Step 2: Cost Estimate

Run a dry-run to estimate cost:

npx goose-aeo run --dry-run --json

Parse the JSON and tell the user:

• Number of queries that will be run
• Which providers will be queried
• Total API calls
• Estimated cost in USD

Ask for confirmation: "This will cost approximately $X.XX. Want to proceed?"

Do NOT proceed without explicit user confirmation.

Step 3: Execute Run

npx goose-aeo run --confirm --json

This may take several minutes. Tell the user it's running and to hang tight.

Parse the result and show:

• Run ID
• Duration
• Actual cost
• Any errors (if some provider calls failed)

Step 4: Analyze Results

npx goose-aeo analyze --json

Show brief progress: "Analyzing responses for brand mentions..."

Parse the result and note:

• How many responses were analyzed
• Analysis cost
• Any alerts triggered (metric drops from previous run)

Step 5: Generate Report

npx goose-aeo report --json

Parse the JSON report and present a conversational summary to the user. Do NOT just dump raw numbers. Structure it like this:

Summary Format

Overall Visibility:

• "Your brand was mentioned in X% of AI search queries (visibility rate)"
• "Average prominence score: X/1.0 (how prominently you're featured when mentioned)"
• "Share of voice: X (your ranking position vs competitors)"

By Provider (only include providers that were run):

• "ChatGPT mentioned you in X% of queries"
• "Perplexity mentioned you in X% of queries"
• etc.

Key Insights:

• Highlight the best-performing provider
• Highlight the worst-performing provider
• If share of voice data is available, mention top competitors
• If any alerts were triggered, explain what dropped and by how much

Actionable Recommendations: Based on the results, suggest 2-3 things the user could do:

• If visibility is low: "Consider creating content that directly answers these types of queries"
• If one provider is much lower: "You may want to focus on optimizing for [provider]"
• If competitors are outranking: "Your top competitors [X, Y] are mentioned more frequently — look at what content they have that you don't"

Step 6: Next Steps

Offer the user these options:

1. "See the dashboard" — Run npx goose-aeo dashboard to open a visual dashboard.
2. "Compare with a previous run" — If there are 2+ runs, offer to run a diff
3. "Set up a schedule" — Ask if they want weekly or daily automated runs
4. "Refine queries" — If results suggest some queries aren't relevant, offer to adjust

Error Handling

• If the run fails partway through, some provider responses may still have been saved. Check the error count and tell the user which providers failed.
• If analysis fails, it usually means the OpenAI API key is missing (used for the analysis model). Tell the user.
• If the report shows all zeros, the brand name/domain may not match what AI engines use. Suggest checking the company name and description in .goose-aeo.yml.
• Common issue: if visibility is 0% across the board, explain that this means AI engines aren't mentioning the brand yet — this is the baseline, and the goal is to improve from here.