Ad Eval — Orchestrator

Evaluation skill. Converts launched paid-ad evidence into a cycle snapshot + ledger row + narrowly-scoped next action inside an existing eval loop. One cycle = one network (+ audience-temp on Meta/TikTok).

Core Question: "Did this ad cycle, on this network, create measurable signal strong enough to keep / discard / watch / block — and what should the next strategy/execution skill know?"

Why, methodology, history: references/playbook.md [PLAYBOOK]. Capability metadata (route triggers, prerequisites, load map): routing.yaml.

Critical Gates

1. Existing eval loop required. program.md + context.md absent → NEEDS_CONTEXT, recommend /run-pipeline. This skill does not create loops.
2. Measurement evidence required + the NETWORK's primary metric decides the row. Need ≥1 metric source, window, current value for the network's primary metric — Meta CTR/CPA/ROAS, Google Quality Score + impression share, TikTok thumbstop/hold rate, LinkedIn cost-per-qualified-lead — per the loaded metric surface (references/ad-intelligence/{google-ads,tiktok-ads,linkedin-ads}-metrics.md; Meta is inline). Secondary metrics explain diagnosis; don't override unless program.md defines a guardrail failure.
3. One network per cycle (+ one audience-temp on Meta/TikTok). Networks (and Meta cold/retargeting) evaluated in separate cycles. Mirrors write-ad's one-network-per-artifact. Mixed-network/audience metrics → split before ingest or return BLOCKED.
4. No fabricated analytics. Unknown stays unknown. Manual notes only when labeled operator-supplied + tied to date/window/source.
5. Attribution confidence must be explicit. Every verdict includes sample size (impressions + spend window), baseline comparability, confounders (creative change mid-flight, audience shift, iOS attribution gap), confidence: high | medium | low | blocked.
6. Evaluation does not generate creative. Recommend next changes; route creative authorship to write-ad (revised brief), channel-mix to plan-campaign, LP-bottleneck to brief-landing-page.