v0.1.0 to v0.1.1

---

name: improve-search-visibility-citation-check

description: >-

Step of the improve-search-visibility playbook: runs the agreed buyer questions on AI and Google

surfaces, records every run and scores how often the brand is named. Run improve-search-visibility

instead; not for direct use.

---

# citation-check

**Reads:** `search/questions.md`; `../improve-search-visibility/references/citation-protocol.md`; the last run record's per-question snapshot (`../improve-search-visibility/references/run-memory.md`); the canonical brand facts from the brain (for accuracy checks on brand questions); the tracked competitor list.

**Does:**

1. **Free pass (step 1):** ChatGPT, AI Overview and AI Mode in the host's own browser (private window, signed out; stop on any CAPTCHA or refusal and mark the surface), and the host's web search for who ranks, labelled "web search". **Paid top-up (step 3, only after the one yes):** Google's exact page one through `gooseworks:data_call_provider` (provider `scrapecreators`, path `/v1/google/search`, the query and region), inside the ceiling (the `spend-gate` skill (`../spend-gate/SKILL.md`) proxy branch), stopping at it. No browser → the minimum paste set, offered in the first message.

2. **Run** — ChatGPT once per question and three times for the key questions; AI Overview and AI Mode once each when Google allows; Google page one once per question in the top-up; pasted: 5 questions (up to 4 key questions + at least one brand question), ChatGPT twice, AI Overview and AI Mode once each. New chat per question, memory off, never the brand name in non-brand prompts. Wait for each answer to finish (protocol's wait rule); unfinished answers are retried once, never scored as misses.

3. **Only this run's observations count.** An earlier run's answers are the baseline from the last run record (`../improve-search-visibility/references/run-memory.md`): shown as "last checked <date>" beside today's reading and compared only like with like (same question version, same route and location, the noise rule there), never counted in today's numbers or tables. A surface not checked today says "not checked today".

4. **Record** every run with the full field list; save verbatim answers. Note whether ChatGPT searched (sources shown) — answers without search come from training memory.

5. **Classify** every cited URL by source type. For brand questions, list each stated fact and compare it to the brain: correct / wrong (quote both) / not on file.

6. **Score** each row (named k of N, won/partial/lost/no-brand answer, prominence, stable or unstable), then mention rate per surface and stage, own-citation rate, third-party coverage and share of voice against the brands the engines named. Won = ≥ ⅔ of runs; unstable = some but not all; one run is never stable.

**Writes:** the rows table used by the report and the verbatim runs file (`search/visibility/<date>-runs.md`, not imported); every charge is logged by the spend gate, and the report states credits charged against the ceiling in the gate's own words.

**Never:** reports one run as fact; passes an organic or API read off as what buyers see in the AI apps; ranks the brand "#2 in ChatGPT".