AI Citation Tracker (the 2026 GEO measurement surface)

You build a multi-engine AI citation tracker. Modern SEO platforms still optimize for Google's blue links. Meanwhile, generative engines deliver 30%+ of informational answers without a click — and those engines DO cite sources, just not in a place Search Console can see. Your job is to make that surface measurable.

============================================================ === PRE-FLIGHT === ============================================================

• [ ] Brand name(s): canonical + variants (e.g., "Stripe" + "stripe.com" + product names + key personnel).
• [ ] Domain(s): your URL(s) to detect in citations.
• [ ] Competitor list: 5-15 brands you track share of voice against. Without competitors, you have a vanity-metric tracker.
• [ ] Query universe: 25-200 priority queries. From: GSC top queries, internal taxonomy, sales objection list, support tickets, customer interviews.
• [ ] API access: At least one of — OpenAI API (ChatGPT with browsing), Anthropic API (Claude), Perplexity API, Google AI Studio API (Gemini), Bing Custom Search. Without APIs, fall back to headless browser polling (slower, more brittle).
• [ ] Storage: SQLite for solo / Postgres for team / BigQuery for enterprise.

Recovery:

• No competitor list → auto-derive top 5 from cited URLs after 1 week of polling, then prompt user to confirm.
• No API keys → generate Playwright-based engine adapters that hit the consumer UIs (with explicit fragility warning + need-for-residential-proxy notice).

============================================================ === PHASE 1: ENGINE ADAPTERS === ============================================================

One adapter per engine. Each adapter answers: "given query Q, what answer was generated and which URLs were cited?"

class EngineAdapter(Protocol):
    name: str  # "chatgpt" | "perplexity" | "claude" | "gemini" | "ai_overviews" | "bing_copilot"

    async def query(self, query: str) -> EngineResult: ...

@dataclass
class EngineResult:
    engine: str
    query: str
    asked_at: datetime
    answer_text: str           # the synthesized answer
    citations: list[Citation]  # ordered as cited
    sources_attribution: str | None  # raw sources HTML/markup if returned
    raw_response: dict          # for replay/debug
    cost_usd: Decimal

@dataclass
class Citation:
    rank: int                  # 1-indexed position in the answer
    url: str
    title: str | None
    snippet: str | None

Engine-specific notes:

VALIDATION: At least 3 engines wired and returning citations end-to-end against a smoke-test query.

FALLBACK: If an engine API is down/rate-limited, the polling job continues other engines + retries the failed one with exp backoff. Single-engine failure never blocks the run.

============================================================ === PHASE 2: STORAGE SCHEMA === ============================================================

SQLite/Postgres schema (Prisma-style):

CREATE TABLE Query (
  id INTEGER PRIMARY KEY,
  text TEXT NOT NULL UNIQUE,
  intent TEXT,                -- informational | commercial | transactional
  priority INTEGER DEFAULT 5, -- 1=critical, 10=long-tail
  added_at DATETIME
);

CREATE TABLE Brand (
  id INTEGER PRIMARY KEY,
  name TEXT NOT NULL,         -- canonical
  aliases TEXT,               -- JSON array
  domain TEXT,                -- if applicable
  is_self BOOLEAN DEFAULT FALSE,  -- our brand vs competitor
  added_at DATETIME
);

CREATE TABLE Poll (
  id INTEGER PRIMARY KEY,
  query_id INTEGER REFERENCES Query(id),
  engine TEXT NOT NULL,
  polled_at DATETIME NOT NULL,
  answer_text TEXT,
  cost_usd DECIMAL,
  raw_response JSON
);

CREATE TABLE Citation (
  id INTEGER PRIMARY KEY,
  poll_id INTEGER REFERENCES Poll(id),
  rank INTEGER,
  url TEXT NOT NULL,
  domain TEXT,                -- derived
  title TEXT,
  snippet TEXT,
  brand_matched INTEGER REFERENCES Brand(id)  -- nullable
);

CREATE TABLE BrandMention (
  id INTEGER PRIMARY KEY,
  poll_id INTEGER REFERENCES Poll(id),
  brand_id INTEGER REFERENCES Brand(id),
  mention_count INTEGER,      -- in answer_text
  cited BOOLEAN,              -- did a URL of theirs appear in citations
  sentiment REAL              -- -1 to 1, optional
);

CREATE INDEX idx_poll_query_engine_date ON Poll(query_id, engine, polled_at DESC);

VALIDATION: Schema migrates cleanly. Sample query inserts + reads.

============================================================ === PHASE 3: POLLING ENGINE === ============================================================

Generate the polling worker (Python / Node). Schedule:

• Daily: top 25 queries × all engines (≈ $5-15/day for ~150 calls).
• Weekly: full universe (100-200 queries) × all engines.
• On-demand: ad-hoc query investigation.

For each poll:

1. Call engine adapter.
2. Persist Poll row + Citation rows.
3. Run brand-mention detection on answer_text:

• Match canonical + aliases (case-insensitive, word-boundary regex).
• Count occurrences.
• Optional: sentiment via small LLM call on the mention context.

1. Run domain-matching on citations against Brand.domain.

Rate-limit aware: respect each provider's RPM. Built-in token-bucket. Exponential backoff on 429.

VALIDATION: Daily run completes within window. Cost stays under budget (configurable, default $20/day cap).

============================================================ === PHASE 4: METRICS & DELTAS === ============================================================

Computed per (engine, query, week):

Persist daily; aggregate weekly. Generate metrics_weekly.csv.

VALIDATION: Metrics reconcile (per-query sums match aggregates). Weekly delta is non-empty after 2+ weeks of polling.

============================================================ === PHASE 5: REPORTING === ============================================================

Generate three reports:

`weekly_report.md` — for the team / boss:

• TL;DR: citations gained, lost, share-of-voice movement.
• Top 5 winners (queries newly citing you).
• Top 5 losers (queries no longer citing you).
• Top 5 citation gaps (where competitor X is cited 5+ engines, you're cited 0).
• Recommended content actions (per query, what to write/update to get cited).

`competitor_matrix.csv` — engine × competitor matrix of citations:

`citation_gap_actions.md` — prescriptive: For each citation gap, output: target query + which engines miss us + competitor URLs cited + a content brief stub (chain into /seo-content-brief).

VALIDATION: Reports render. CSV imports cleanly into Excel/Sheets.

============================================================ === PHASE 6: ALERTING & DASHBOARD === ============================================================

Push-style alerts:

• Brand mention sentiment swing (sentiment drops > 0.3 in any engine) → Slack/email.
• Citation loss on top-10 query → Slack/email same-day.
• Competitor newly cited on tracked query → daily digest.
• Cost over budget → throttle + alert.

Optional dashboard: Streamlit or Next.js + Prisma. Tabs: Overview, Per-Engine, Per-Query, Competitor Drill, Cost.

VALIDATION: Alerts fire on simulated event in test. Dashboard renders against the SQLite DB.

============================================================ === PHASE 7: HOW THIS BEATS LEGACY SEO TOOLS === ============================================================

VALIDATION: This positioning resonates with users who already pay for one of the above.

============================================================ === SELF-REVIEW === ============================================================

Score 1-5:

• Complete: 6+ engine adapters, schema, polling, metrics, reports, alerts?
• Robust: Single-engine failure doesn't break the pipeline? Rate-limit / budget controls?
• Clean: Citations dedupe? Brand matching handles aliases/case?
• GEO-credible: Would a CMO who's seen Visibly / Profound / Otterly / Brand24's AI module recognize this as production-grade?

Common gap: matching only canonical brand name, missing variants. Generate the alias seed list from the user's marketing site + Wikipedia + Crunchbase.

============================================================ === LEARNINGS CAPTURE === ============================================================

~/.claude/skills/ai-citation-tracker/LEARNINGS.md.

============================================================ === STRICT RULES === ============================================================

• Never poll without rate-limit / budget caps. AI engine APIs are billed; runaway loops are expensive.
• Never silently drop a failed engine. Surface the failure in the run report.
• Never use brand matching by canonical name alone. Aliases + variants are required.
• Always include competitor tracking. Share-of-voice without comparison is a vanity number.
• Always preserve raw_response. New citation patterns emerge; you'll want to replay.