Rechercher

PythonReactSEOPDF
OpenClaw

media-buyer-ads-helper

Support media buying execution for Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, and DSP/programmatic with account health checks, bidding efficiency analysis, AB test design, and real-time anomaly monitoring.

danyangliu-sandwichlab0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install danyangliu-sandwichlab/media-buyer-ads-helper

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Media Buyer Helper

Purpose

Core mission:

  • Evaluate account health and structure quality.
  • Analyze bid logic and budget allocation efficiency.
  • Design AB test architecture and scale model.
  • Monitor campaigns in real time and detect anomalies.

When To Trigger

Use this skill when the user asks for:

  • media buyer execution support
  • bid and budget efficiency diagnostics
  • AB testing structure design
  • live campaign watch and anomaly alerts

High-signal keywords:

  • media, bidding, budget, auction, allocation
  • abtest, campaign, performance, optimize
  • cpa, roas, scale, monitor

Input Contract

Required:

  • account_structure_snapshot
  • bidding_config
  • budget_allocation_snapshot
  • recent_performance_series

Optional:

  • test_history
  • alert_thresholds
  • creative_breakdowns
  • seasonality_notes

Output Contract

  1. Account Health and Structure Score
  2. Bid and Budget Efficiency Findings
  3. AB Test Structure Blueprint
  4. Scale Model with Trigger Conditions
  5. Monitoring and Alert Rules

Workflow

  1. Check account hierarchy and naming hygiene.
  2. Evaluate bid strategy vs KPI objective.
  3. Diagnose budget fragmentation and overlap.
  4. Build AB test matrix with clear success metrics.
  5. Define anomaly thresholds and response playbook.

Decision Rules

  • If structure complexity is high and spend is low, simplify before adding tests.
  • If CPA variance is high, reduce concurrent experiments.
  • If winning cells are statistically weak, extend learning window.
  • If anomaly severity is high, prioritize containment over optimization.

Platform Notes

Primary scope:

  • Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic

Platform behavior guidance:

  • Map bid logic to channel auction mechanics.
  • Keep test isolation strict to avoid cross-cell contamination.

Constraints And Guardrails

  • Do not claim statistical significance without threshold checks.
  • Avoid broad budget jumps without gate conditions.
  • Keep alert rules tied...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/danyangliu-sandwichlab/media-buyer-ads-helper
Version
—
Commit
c528c7ef3d6671e4dafbb2da0b89c868a8ef381d
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Media Buyer Helper

## Purpose
Core mission:
- Evaluate account health and structure quality.
- Analyze bid logic and budget allocation efficiency.
- Design AB test architecture and scale model.
- Monitor campaigns in real time and detect anomalies.

## When To Trigger
Use this skill when the user asks for:
- media buyer execution support
- bid and budget efficiency diagnostics
- AB testing structure design
- live campaign watch and anomaly alerts

High-signal keywords:
- media, bidding, budget, auction, allocation
- abtest, campaign, performance, optimize
- cpa, roas, scale, monitor

## Input Contract
Required:
- account_structure_snapshot
- bidding_config
- budget_allocation_snapshot
- recent_performance_series

Optional:
- test_history
- alert_thresholds
- creative_breakdowns
- seasonality_notes

## Output Contract
1. Account Health and Structure Score
2. Bid and Budget Efficiency Findings
3. AB Test Structure Blueprint
4. Scale Model with Trigger Conditions
5. Monitoring and Alert Rules

## Workflow
1. Check account hierarchy and naming hygiene.
2. Evaluate bid strategy vs KPI objective.
3. Diagnose budget fragmentation and overlap.
4. Build AB test matrix with clear success metrics.
5. Define anomaly thresholds and response playbook.

## Decision Rules
- If structure complexity is high and spend is low, simplify before adding tests.
- If CPA variance is high, reduce concurrent experiments.
- If winning cells are statistically weak, extend learning window.
- If anomaly severity is high, prioritize containment over optimization.

## Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, DSP/programmatic

Platform behavior guidance:
- Map bid logic to channel auction mechanics.
- Keep test isolation strict to avoid cross-cell contamination.

## Constraints And Guardrails
- Do not claim statistical significance without threshold checks.
- Avoid broad budget jumps without gate conditions.
- Keep alert rules tied...

Skills associés

OpenClaw

xint

0xnyk

X Intelligence CLI — search, monitor, analyze, and engage on X/Twitter. TypeScript + Bun. AI agent skill.

OpenClaw

bambu-studio-ai

heyixuan2

Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...

OpenClaw

flightclaw

jackculpan

Track flight prices from Google Flights with this OpenClaw skill. Search routes, monitor prices, and get alerts when prices drop.

OpenClaw

openclaw-trading-assistant

openclaw-trade

openclaw trading assistant| openclaw trading skill | nof1.ai & openclaw [moltbot] collaboration | We get the best practices from alpha arena trading seasons and bring it to clawdbot All top AI agents, realtime...