Apify Search Console AI Overview

Search Console AI Overview: A Prioritized Rewrite Queue

Build a prioritized rewrite queue from your Google Search Console data. This Actor joins each query's Search Console position and CTR against the live AI Overview citation state, then returns a tiered queue so you rewrite the highest-leverage pages first: the ones where a competitor is cited in the AI Overview while you still rank 5 to 20.

When to use this skill

• The user wants to know which pages to rewrite because of Google AI Overviews, starting from their own Search Console data.
• They ask about "google search console ai overview" or "search console ai overview" and want an action list, not a dashboard.
• They have a Search Console Queries export (CSV) and a domain, and want it turned into a ranked to-do queue.
• They want the join that nothing else does: Search Console rank and CTR on one side, live AI Overview citation state on the other.

Not for: a plain yes or no citation check across a fixed watchlist (use the google-ai-overview-monitoring skill), organic rank tracking on its own, or content rewriting itself (this tells you which pages to rewrite, not what to write).

What you get (one row per query)

Queue and identity: result_type (labels the record), query, query_normalized, tier (A to D, or X), tier_reason (why it landed there), join_status (whether the query matched a Search Console row).

Search Console metrics carried through the join: clicks, impressions, ctr, position.

Live citation check: check_status, ai_overview_present, citation_state (one of cited, competitor_cited, no_overview, overview_no_references, or null), cited_urls, cited_pages_count, reference_domains, reference_count, fetched_at. Failures carry error_message and error_type.

A run summary is written to the key-value store.

How tiers rank the queue

• Tier A: a competitor is cited in the AI Overview and you rank position 5 to 20. Highest-leverage rewrites; do these first.
• Tier B: a competitor is cited or the overview has no references, and you rank 1 to 4. You are close; a rewrite can win the citation.
• Tier C: you are already cited but your CTR is below your own baseline for that query.
• Tier D: no AI Overview for the query.
• Tier X: the citation check failed, or the query did not match a Search Console row. Kept, never dropped, so you can see the gap.

Prerequisites

• Apify account (sign up at https://apify.com?fpr=9n7kx3&fp_sid=skillrepo).
• Authentication via apify login, or an APIFY_TOKEN environment variable (Apify Console, Settings, Integrations).
• Your Search Console Queries report. Export it from the Performance report as CSV, host it at a URL and pass search_console_csv_url, or pass the rows inline as search_console_rows.

The Actor

• Store page: https://apify.com/johnvc/ai-overview-rewrite-queue?fpr=9n7kx3&fp_sid=skillrepo
• Actor ID: johnvc/ai-overview-rewrite-queue
• Pricing: a small per-run setup fee plus a scored-query fee. This is a composition Actor, so the citation half runs a second Actor that bills your own account separately. Read references/gotchas.md before scheduling.

Run it with the Apify CLI

From a hosted Search Console CSV:

apify actors call "johnvc/ai-overview-rewrite-queue" -i '{"target_domains":["example.com"],"search_console_csv_url":"https://example.com/exports/search-console-queries.csv","min_impressions":10,"gl":"us","hl":"en"}' \
  --json \
  --user-agent apify-awesome-skills/apify-search-console-ai-overview \
  2>/dev/null

From inline rows, when you already have the Queries report in hand:

apify actors call "johnvc/ai-overview-rewrite-queue" -i '{"target_domains":["example.com"],"search_console_rows":[{"query":"best crm for startups","clicks":12,"impressions":540,"ctr":0.022,"position":7.3},{"query":"crm with free tier","clicks":4,"impressions":210,"ctr":0.019,"position":11.4}],"min_impressions":10}' \
  --json \
  --user-agent apify-awesome-skills/apify-search-console-ai-overview \
  2>/dev/null

Read the rows back later, for example from a scheduled run:

apify datasets get-items <DATASET_ID> --format json --user-agent apify-awesome-skills/apify-search-console-ai-overview 2>/dev/null

Every call carries the three flags this repo expects: --json (or --format json), --user-agent apify-awesome-skills/apify-search-console-ai-overview, and 2>/dev/null.

Run it from Claude or another AI agent (MCP)

The Actor is MCP-ready. Add the hosted server URL:

https://mcp.apify.com/?tools=actors,docs,johnvc/ai-overview-rewrite-queue

Then ask, for example: "Here is my Search Console CSV for example.com. Build the rewrite queue and show me the tier A pages first." MCP setup docs: https://docs.apify.com/platform/integrations/mcp

Workflow

1. Export the Queries report. In Search Console, open Performance, set the date range and property, and export the Queries table as CSV (query, clicks, impressions, ctr, position). Host it and pass search_console_csv_url, or paste the rows into search_console_rows.
2. Set target_domains. List every property you count as "yours" (apex plus subdomains you own). The Actor compares these against the AI Overview's cited domains to decide cited versus competitor_cited.
3. Set min_impressions to trim noise. The default is 10; raise it on a long export so you score the queries that actually carry traffic and keep the child citation cost down.
4. Estimate the cost for both Actors and confirm. Query count drives the bill on both sides. See references/gotchas.md and set a run budget.
5. Run, then work the queue top down. Start with tier A (competitor cited, you rank 5 to 20), then tier B (you rank 1 to 4). Use tier_reason, position, and reference_domains to brief the rewrite.
6. Re-run after you ship rewrites to confirm the tier moved (A to B to cited).

Inputs

• target_domains (array, required): the domains you own; used to classify cited versus competitor_cited.
• search_console_csv_url (string): URL to a Search Console Queries CSV. One of the three query sources.
• search_console_rows (array): inline rows of {query, clicks, impressions, ctr, position}. Preserves the join fields.
• queries (array): a bare query list when you have no Search Console metrics; those rows join_status as unmatched and land in tier X, but still get a citation check.
• min_impressions (int, default 10): drop queries below this impression floor before scoring.
• gl (string, default us) and hl (string, default en): market targeting for the citation check.
• location (string): optional named location for local-intent queries.

Cost

Two Actors bill on one run. This Actor charges a per-run setup fee plus a per-scored-query fee; the citation half runs the sibling johnvc/google-ai-overview-api, whose run bills your own account separately (a setup fee plus a per-retrieval fee, one or two retrievals per query). So the query count drives the bill on both sides. Keep query counts modest, raise min_impressions, and set a run budget. Worked numbers and the exact per-event prices are in references/gotchas.md.

Honest limits

• Join rate. Search Console anonymizes long-tail queries, so a long-tail export will not fully match; expect roughly 30 to 60 percent of a long-tail list to join. Unmatched queries are labelled tier X and kept, never dropped, so the gap is visible.
• AI Overviews are not deterministic. The cited set can shift between identical runs, so treat a single tier A as a candidate and confirm over a couple of runs before a big rewrite.
• The CTR baseline in tier C is your own historical CTR for that query, not an industry number.
• The queue tells you which pages to rewrite and why; the rewrite itself is your call.
• Point-in-time, not a live feed: fetched_at timestamps each row.

Troubleshooting

• Everything lands in tier X: the join is not matching. Check that query values in your CSV match live queries, that target_domains is set, and that min_impressions is not filtering everything out.
• Few or no tier A rows: either you already rank 1 to 4 (look at tier B) or few overviews cite competitors in your space; lower min_impressions to widen the set, or confirm gl matches your market.
• Whole run shows ai_overview_present false: keep hl at en, try gl at us, and check the queries actually trigger overviews.
• Cost higher than expected: the child citation checks dominate; cut the query count or raise min_impressions, and set a run budget.

See references/gotchas.md for cost guardrails and error recovery, and references/actor-index.md for the Actor routing table.

Related answer-engine Actors

• Google AI Overview API (the citation engine this Actor composes, and a standalone check): https://apify.com/johnvc/Google-AI-Overview-API?fpr=9n7kx3&fp_sid=skillrepo
• Bing Copilot API: https://apify.com/johnvc/bing-copilot-api?fpr=9n7kx3&fp_sid=skillrepo
• Brave AI Mode API: https://apify.com/johnvc/brave-ai-mode-api?fpr=9n7kx3&fp_sid=skillrepo