القائمة
Cruza foot traffic Store A/Store B con Meta geo spend, GA4 city sessions y Shopify POS. Estima halo effect por tienda y recomienda redistribucion spend.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Never re-fight a settled battle. Discipline for recording and RETRIEVING past failures — gotchas, incidents, postmortems, dead ends, reverted fixes — as symptom → root cause → evidence → status. Use before debugging anything that smells familiar, when an incident closes, when a fix gets reverted, when an approach is abandoned, or when asked to document a bug just solved, write a postmortem, do failure archaeology, incident memory, or "did we hit this before". Not for capturing general knowledge (use brain_capture / brain_write) or live task tracking.
Write research prompts that come back decision-ready — zero-prior-knowledge framing, evidence hierarchy, fact/inference separation, and a mandatory gap round. Use when formulating a research prompt, deep research request, competitive research, market research, due diligence question, or when delegating research to exa_search, an agent, Codex, or an external researcher. Not for running the research itself end-to-end (deep-research, autoresearch, gemini-deep-research, exa_search EXECUTE research — this skill writes the brief that feeds them), not for the reference deployment strategic deliberation (use council/punta-de-flecha), and not for Brain lookups (brain_search first, always).
Transfer full working context to a fresh agent session with zero memory — end of session, before compaction, switching models/agents mid-project, or pausing multi-day work. Use when asked for a handoff, handoff doc, session transfer, "deja esto listo para la próxima sesión", context dump for another agent, or when a long session is about to hit context limits. Not for delegating a bounded task (use codex-handoff) or for task cards (use handoff-task / brain_task tools).
Evaluate whether a the reference deployment skill actually works — blind runner + separate judge against a bar, rubric scoring, pass-rate on re-runs, and defect diagnosis. Use when asked to evaluate a skill, eval skills, run a blind eval, score against a rubric, test a skill, audit skill quality, grade a skill, review the skill library, or before promoting a new skill to the company master prompt or the whole swarm. Not for writing skills (use skill-authoring-standards) or deciding which to create (use suggest-skills).
Turn a big or ambiguous technical task into a spec of independently verifiable slices, implement slice-by-slice with verification gates, and close the spec with a claim audit. Use when asked to write a spec, implement a spec, plan a build, break down a feature, structure a multi-day or multi-week technical project or migration, or when a Codex/agent delegation is too big for one self-contained prompt. Not for small tasks (single codex-handoff prompt suffices) or non-technical plans.
Verify visual work with evidence instead of vibes — screenshot critique by a fresh judge, before/after comparison, crops of the disputed detail, and a distance-not-verdict mindset. Use when reviewing a UI, landing, email template, dashboard, canvas, or design change; when doing a screenshot critique, comparing screenshots, doing visual QA or UI QA on a landing page, checking a clone against a reference, or before declaring any visual deliverable done. Not for choosing a design direction or running a dedicated accessibility audit.
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| name | omnichannel-foot-traffic-attribution |
| description | Cruza foot traffic Store A/Store B con Meta geo spend, GA4 city sessions y Shopify POS. Estima halo effect por tienda y recomienda redistribucion spend. |
| metadata | {"source":"the company-native","category":"ads_diagnostics","secondary_category":"marketing","primary_user":"marketing-agent","owner":"strategy-hub","cadence":"monthly_day_1_0900_cet","tools_needed":"tc_analytics_query, meta_ads_query, ga4_query, shopify_query, gsc_search_analytics, brain_search, slack_send_message"} |
Public, anonymized version of a skill running in the Compai reference deployment (an 8-figure consumer SME). Names, stores, channels and endpoints replaced with placeholders; the logic is unchanged. Adapt to your own context.
Implementa Stage 2.3 de the company-omnichannel-growth-loop como playbook mensual. Cruza foot traffic diario de Store A/Store B con Meta Ads geo spend, GA4 city sessions, GSC branded search y Shopify POS revenue para estimar halo effect por tienda y recomendar redistribucion de spend entre Madrid, Barcelona y futuras aperturas.
Este skill se ejecuta en un agente con context window limitado. Para NO agotarlo:
fields=... para traer solo las columnas necesarias. Nunca pidas el objeto completo.limit=50 máximo por query. Si necesitas más, agrega/cuenta server-side o pagina explícitamente solo si es imprescindible.Si en mitad de la ejecución el context se siente lleno, ENTREGA lo que tengas con una nota "[parcial: faltó stage N por límite de context]" en vez de fallar en silencio.
Usa tc_analytics_query para el rango minimo necesario: por defecto ultimos 30 dias + periodo comparable anterior. Solo usa 180 dias si el caller pide analisis mensual historico o apertura. TC Analytics es scraping lento (~60s), asi que ejecuta 1 tienda a la vez:
store=store_a, date_range=needed_range, fields=date,exterior_traffic,entries,attraction_rate,tickets,conversion_rate,revenue,dwell_time.store=store_b, mismos campos.Extrae por dia: exterior traffic, entries, attraction rate, tickets, conversion rate, revenue y dwell time si esta disponible.
Usa meta_ads_query con breakdown geografico para Madrid y Barcelona:
Usa limit=50, fields=date,region,city,campaign_name,adset_name,spend,impressions,reach,clicks,purchases,purchase_value, mismo rango que Stage 1 y resumen por ciudad/campaign antes de seguir. No mezcles spend nacional con geo spend si el breakdown no permite asignacion razonable.
Usa ga4_query para el mismo rango acotado de Stage 1:
Pide agregacion diaria por city con fields=date,city,defaultChannelGroup,sessions,users,conversions,totalRevenue y limit=50. Resume y descarta raw antes de Stage 4.
Usa shopify_query para POS revenue y orders por location:
Pide agregacion por dia/location con fields=date,location_name,sales_channel,orders,net_revenue,refunds, limit=50, mismo rango que Stage 1. Separa POS, online y refunds. Mantener timezone consistente Europe/Madrid. Resume y descarta raw.
Calcula correlaciones y lags:
gsc_search_analytics) vs meta_spend_geo.Para gsc_search_analytics, pide solo branded query summary por city/country si esta disponible, fields=date,query,clicks,impressions,ctr,position, limit=50, mismo rango. Resume antes de correlacionar.
Output debe separar:
Tabla por tienda:
La recomendacion debe decir cuanto mover, desde donde hacia donde, durante que periodo y que KPI observar.
Para Bilbao o Valencia:
punta_de_flecha antes de recomendar.tc_analytics_querymeta_ads_queryga4_queryshopify_querygsc_search_analyticsbrain_searchslack_send_messageMensual, dia 1 a las 9:00 CET.