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).
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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.
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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).
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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.
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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.
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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.
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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.
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Recommendations table — each row tied to a Persona/Goal rationale (do not execute yet):
| Platform | Segment / Placement | Current | Signal | Recommendation | Persona/Goal rationale |
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| 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.
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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.
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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.
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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.
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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 runperiod: the window analysed (e.g. "LAST_30_DAYS")kpis: per-platform segment counts (targeted / excluded) and the worst-CPA segments vs Personaflags: 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.
