audience-review

name	audience-review
description	Audit who your ads actually target and where they run, compare it against the STRATEGY.md Persona, and surface exclusions, bid adjustments, lookalikes, and placement pruning tied to that Persona. Use when the user asks to review targeting, audiences, demographics, placements, or device performance, to check whether spend matches the Persona, to find wasted placements (e.g. Audience Network with no conversions), or requests オーディエンスレビュー / 配置レビュー / ターゲティング見直し / ペルソナと配信のズレを確認 / 除外設定を提案して. Reads Persona + Target Audience from STRATEGY.md and drives the read-only targeting tools.
metadata	{"version":"0.10.29"}

Audience Review

PREREQUISITE: Read ../_mureo-shared/SKILL.md for auth, security rules, output format, and Tool Selection (Read/Write on Code, mureo_strategy_* / mureo_state_* MCP on Desktop / Cowork).

Reconcile who you say you want (the STRATEGY.md Persona / Target Audience) with who you are actually paying to reach (the live targeting and the segments where spend lands). Accounts drift: a broad audience quietly spends on a segment the Persona excludes, a placement like Audience Network burns budget with zero conversions, or the Persona implies a lookalike that was never built. This skill inventories current targeting, scores performance by segment, flags the mismatches, and proposes Persona-anchored changes.

Prerequisites

• STRATEGY.md and STATE.json must exist (run the onboard skill first)

Steps

Before you start: Run the Diagnostic preamble from ../_mureo-shared/SKILL.md — load learning insights (mureo_learning_insights_get) and consult advisors (mureo_consult_advisor) before drawing conclusions.

1. Load context: Read STRATEGY.md — especially the Persona and Target Audience (age, gender, interests, geography, device intent, exclusions the Persona implies) and each Goal's success metric — and STATE.json. The Persona is the yardstick for every judgement below; if STRATEGY.md has no Persona, say so and offer to capture one via /onboard rather than inventing demographics.

2. Discover platforms: Identify configured platforms from STATE.json platforms. Also include any hosted official-MCP connector in the session (e.g. TikTok, key tiktok_ads) and mcp__mureo__<plugin>_* plugin platforms — for those, report only the targeting surface their own tools expose and emit analytics_not_available_for_<platform> for the segment-scoring value-adds below; do not fabricate targeting verdicts from an integration's tool schemas (see ../_mureo-shared/SKILL.md → Plugin platforms / Hosted-connector platforms).

3. Inventory current targeting — Google Ads (prefer mureo native):

   • Demographics: google_ads_demographic_targeting_list — explicit age / gender / parental-status / income criteria and their negative (excluded) flag. Remember an empty result means "all demographics, no exclusions", not "nothing targeted" — segments with no explicit criterion are targeted by default.
   • Audiences: google_ads_audience_targeting_list — affinity / in-market interests, remarketing & customer-match user lists, custom / combined audiences, with their negative flag.
   • Devices: google_ads_device_analyze for per-device (Desktop / Mobile / Tablet) spend, CPA, CVR and its built-in insights, plus google_ads_device_targeting_get / google_ads_bid_adjustments_get for the current device bid modifiers in place.
   • If mureo's Google Ads tools are unavailable (MUREO_DISABLE_GOOGLE_ADS=1 after mureo providers add google-ads-official), fall back to the official google-ads-official MCP for the targeting-listing surface it exposes; note the segment-scoring heuristics below still apply.

4. Inventory current targeting — Meta Ads (prefer mureo native):

   • Custom Audiences: meta_ads_audiences_list — id, name, subtype (WEBSITE / CUSTOM / LOOKALIKE / …), approximate_count, retention_days.
   • Ad-set targeting: meta_ads_ad_sets_list — each ad set's targeting_summary, budget, and optimization_goal (targeting lives at the ad-set level on Meta).
   • Placement & segment analysis: meta_ads_analysis_placements (per-placement spend / CPA / CTR with keep / exclude / scale recommendations) and meta_ads_analysis_audience (age × gender efficiency scoring). For raw slices use meta_ads_insights_breakdown with breakdown = age,gender, publisher_platform, placement, or device_platform.
   • If mureo's Meta tools are unavailable, fall back to the official meta-ads-official hosted MCP for its targeting / breakdown surface, and self-apply the guardrail/confirm rules yourself.

5. Performance by segment: For each platform, pull the demographic / placement / device breakdowns the tools support (step 3–4) and compute spend, conversions, CPA, and CVR per segment. Respect sample-size discipline from ../_mureo-learning/SKILL.md — a segment with a handful of clicks is not evidence of "poor CVR"; note thin segments as insufficient data, not as waste.

6. Mismatch analysis vs Persona: Cross-read the segment table against the Persona and flag three failure shapes:

   • Contradicts the Persona: a segment spending meaningfully with poor CVR that the Persona did not call for (e.g. a 55–64 bucket eating budget when the Persona is 25–34) — a candidate exclusion or negative bid.
   • Missing what the Persona implies: a high-intent segment the Persona describes that has no targeting or lookalike behind it — a coverage gap.
   • Placement waste: a placement with spend and zero (or far-below-average) conversions — classically Audience Network / Search Partners — that is diluting the average.

7. Recommendations table — each row tied to a Persona/Goal rationale (do not execute yet):

   Platform	Segment / Placement	Current	Signal	Recommendation	Persona/Goal rationale
   Meta	Audience Network	on, ¥18k spend	0 conv	Exclude placement	Persona converts on IG Feed; AN is waste
   Google	Mobile	+0% modifier	CPA 1.8× Desktop	−20% device bid	Protect Goal CPA
   Meta	(Persona lookalike)	none	—	Create 1% LAL of purchasers	Persona = past-buyer profile

   Recommendation types: exclusions, bid adjustments, lookalike creation, placement pruning. Reference past action_log outcomes — if a device bid adjustment was previously REJECTED as no-impact, note that before proposing it again.

8. Approval gate before ANY write: Apply the Confirm Before Write Operations rule from ../_mureo-shared/SKILL.md — show current-vs-proposed for every change and get explicit approval. Respect Operation Mode and ## Guardrails; a targeting change during ONBOARDING_LEARNING / CREATIVE_TESTING is discouraged — warn and ask before proceeding.

9. Execute — only the mutations mureo actually supports; everything else is an honest manual step:

   • Google Ads device bid adjustments: google_ads_bid_adjustments_update (bid_modifier 0.1–10.0; use google_ads_device_targeting_set to toggle a device fully on/off at 0.0).
   • Meta lookalike creation: meta_ads_audiences_create_lookalike (source_audience_id, country, ratio 0.01–0.20).
   • Meta ad-set targeting edits (add an exclusion, narrow a facet, swap an audience): meta_ads_ad_sets_update with a targeting object — by default it is a safe read-modify-write (supplied keys merge onto the current spec; omitted keys preserved); set replace_targeting true only to replace the whole spec.
   • Google Ads demographic/audience criterion add/exclude and Meta placement exclusions have no dedicated mureo mutation tool — present these as a manual step in the platform UI (exact segment + action), honestly, rather than referencing a tool that does not exist. Do not fabricate a *_targeting_update for demographics/audiences.

10. Record outcome context: For each change executed, append to action_log via mureo_state_action_log_append with metrics_at_action (the segment's current spend / conversions / cpa / cvr / reach) and observation_due 14 days out (the targeting-change window from ../_mureo-learning/SKILL.md), so daily-check's evidence step verifies the change helped. A pure audit with no changes still logs a summary entry (findings + mismatch table), with no observation_due.

11. Persist the report summary (best-effort): Call mureo_state_report_set with report="audience" and a concise summary object so the read-only dashboard can render this review without re-running you. Follow this convention:

    • generated_at: ISO 8601 timestamp of this run
    • period: the window analysed (e.g. "LAST_30_DAYS")
    • kpis: per-platform segment counts (targeted / excluded) and the worst-CPA segments vs Persona
    • flags: notable items (e.g. ["meta_audience_network_zero_conv", "google_mobile_cpa_1.8x", "persona_lookalike_missing"])
    • narrative: the 1-2 sentence verdict (targeting matches Persona / drift found)

    Reflect the FINAL state, and persist this LAST — after every action_log entry and any change you applied this run. This is best-effort: if mureo_state_report_set is unavailable (e.g. a pure file-mode host without the context MCP), skip it silently — the rest of this skill still works.