aeo-visibility-monitor

name	aeo-visibility-monitor
description	Keep a brand's AI answer-engine visibility under continuous watch — on a recurring schedule re-run the AEO visibility check across Perplexity/ChatGPT/Gemini over a frozen prompt set, persist each run, diff against history, and alert the team only when mention rate, prominence, or share-of-voice materially moves (a competitor overtakes you, an engine drops you, a new citation source appears). The recurring wrapper around aeo-visibility. Requires one answer-engine key (Perplexity canonical); durable history defaults to a local JSONL ledger, with Supabase as an optional upgrade.
metadata	{"version":"1.0.1","category":"seo","type":"composite"}

AEO Visibility Monitor

The recurring/scheduled wrapper around aeo-visibility. It inherits all engine-query and scoring logic from that capability and adds: scheduling, frozen-query-set config, durable history, deterministic diffing, and no-noise alerting.

Clean-room note: no source skill of this name existed — it was designed from the README one-liner plus the aeo-visibility capability. All engine-query and scoring logic is inherited from aeo-visibility, not reinvented.

When to use

• "Track AEO over time", "watch our AI visibility", "alert me when ChatGPT stops recommending us", "monitor competitor AEO weekly/monthly".
• Any standing AEO mandate rather than a one-off snapshot.

How to run

Scripts are Python 3 stdlib only. Use ${WORKSPACE} for scratch files.

1. Schedule the recurring run

Register a recurring trigger at the configured cadence (default weekly). On the Robomotion platform this is a Cron/scheduled trigger firing the agent's flow; in this harness, wire it via the scheduling mechanism available to you. Each fire runs steps 2–5.

2. Resolve the FROZEN config

On the first run only, generate the ~50 buyer-intent prompts (as in aeo-visibility), get human review, then freeze them — store the prompt set, competitors, and engine list durably. Later runs reuse the frozen set verbatim; changing prompts between runs breaks comparability (note any deliberate change as a baseline reset).

3. Query engines + score (inherited from aeo-visibility)

python3 ${SKILL_DIR}/scripts/query_engines.py \
  --queries-file ${WORKSPACE}/frozen_prompts.json \
  --engines perplexity,openai,gemini \
  --output ${WORKSPACE}/responses.json

Then you, the agent, score each response into a metrics blob (see diff_runs.py docstring for the exact shape): {domain, timestamp, complete, overall, per_engine, competitor_sov, competitor_citations, source_domains}. Mark complete:false if any engine errored, so the diff is skipped (avoids false drop-to-zero alerts). Write it to ${WORKSPACE}/scored_run.json.

4. Persist + diff against the prior run (script)

python3 ${SKILL_DIR}/scripts/diff_runs.py \
  --run ${WORKSPACE}/scored_run.json \
  --threshold 0.05 \
  --history ${WORKSPACE}/aeo_history.jsonl \
  --output ${WORKSPACE}/diff.json

diff_runs.py appends the run to durable history (Supabase when SUPABASE_URL/ SUPABASE_KEY are set — table aeo_runs(domain, ts, payload jsonb); otherwise the local JSONL ledger given by --history) and computes deltas: overall + per-engine mention rate, prominence, share-of-voice vs each competitor, new/lost competitor citations, new cited source domains. It emits an alert verdict on threshold crossings, competitor overtakes, or drop-to-zero events. First run returns baseline: true.

5. Alert on material change (you, the agent)

If diff.json has alert: true, narrate the change (what/where/since-when + a suggested action) and push it to the alert channel (Slack/Discord/Telegram, or write to the workspace if no channel is configured). If baseline: true, emit "baseline established", not an alert. Otherwise stay silent and just update the trend artifact.

Outputs

• Per-cycle run appended to durable history (Supabase or JSONL), keyed by domain + timestamp.
• diff.json: deltas, new/lost competitor citations, new source domains, alert verdict.
• Trend artifact (markdown/CSV, agent-written) of the metric series over time.
• A threshold-triggered alert only when a material change occurs (silent when stable).

Credentials / env

• Required (inherent): PERPLEXITY_API_KEY (or another single answer-engine key the query_engines.py run uses) — querying answer engines is the task, so one engine key stays required; the core engine query is dead without one. The additional engines are optional and the run degrades to whatever keys are present.
• Optional:
  • OPENAI_API_KEY, GEMINI_API_KEY, TAVILY_API_KEY — each gates only its own engine; absent → that engine is skipped, not a failure.
  • Storage — SUPABASE_URL/SUPABASE_KEY: if set → diff_runs.py persists each run to Supabase (durable, shareable). If not → the default keyless fallback is a local JSONL ledger via --history <path> (the step above always passes it), so the monitor runs fully without Supabase. diff_runs.py only exits if neither is available, and the skill always supplies --history.
  • Channel creds (SLACK_*/DISCORD_*/TELEGRAM_*) — needed only if alerting to a channel; absent → write the alert to the workspace.

Notes & edge cases

• Freeze the query set after the first run — comparability depends on it.
• No-noise alerting: only fire on a threshold crossing or a hard event (overtake / drop-to-zero); otherwise update the trend silently.
• Cost compounds with cadence — weekly × 50 prompts × N engines adds up; keep the default engine set to 3 and let the user opt into more.
• Partial-failure cycles: mark the run complete:false; diff_runs.py then skips the diff rather than emitting false "dropped to zero" alerts.
• First run produces no diff — diff_runs.py returns baseline: true; emit "baseline established" instead of an alert.