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AdMapix Raw Data Developer Helper

面向数据工程师、增长分析师等从业者,核心为AdMapix风格的广告、应用市场等原始数据提供 ingest、归一化、校验、故障处理全流程设计与加固,管控数据溯源、schema漂移、重复数据等问题。典型场景包括设计原始数据处理层、清洗App排名CSV、创建数据分析校验规则。输出涵盖数据模型、校验规则、本地实现脚本、可靠性 checklist等,支持先轻量本地验证再落地仓库。

kyro-ma
kyro-ma
热度 16.02
最近更新 2026-06-23 12:34:05
分类:数据依赖:代码执行 / 读写文件 / 第三方 API / 内部调用大模型应用场景:代码生成 / 数据处理 / 自动化脚本 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add software-data-admapix-raw-developer-helper-040526
Metadata
name
software-data-admapix-raw-developer-helper
description
Design and harden AdMapix-style raw data workflows for creative, app, ranking, revenue, and analytics datasets before they become dashboards or agent tools. Use when a user asks for AdMapix, raw data, app analytics, creative data, rankings, or needs practical workflow, code, checklist, documentation, or review support for this job.

AdMapix Raw Data Developer Helper

Purpose

Use this skill when a user needs to ingest, normalize, inspect, or expose raw advertising/app-market data while keeping provenance, schema drift, deduplication, and downstream safety under control.

Audience: data engineers, growth analysts, skill authors, and agent builders adapting popular AdMapix-style raw data workflows.

Read references/requirement-plan.md when demand evidence, source links, scoring rationale, or review criteria are needed.

Workflow

  1. Identify raw sources, refresh cadence, entity keys, metrics, dimensions, privacy constraints, and target consumers.
  2. Create a source-to-table map that separates immutable raw captures from cleaned views, feature extracts, and presentation layers.
  3. Specify validation rules for required columns, date ranges, currency, locale, creative IDs, app IDs, duplicate rows, and missing metrics.
  4. Plan failure handling for API quota, partial exports, schema drift, backfill gaps, and vendor naming changes.
  5. Recommend lightweight local implementation steps using CSV, JSON, SQLite, or DuckDB before heavier warehouse work.
  6. Return an audit trail with source, transform version, row counts, anomalies, and consumer-facing caveats.

Expected Outputs

  • A raw-to-clean data model for AdMapix-style inputs.
  • Validation checks for schema drift, duplicates, missing values, and metric consistency.
  • A local script or pseudocode plan for ingestion and normalization.
  • A reliability checklist for publishing the workflow as an agent skill.

Validation

  • Raw data remains reproducible and separate from cleaned outputs.
  • Every transform records source, timestamp, row count, and known anomalies.
  • The workflow handles partial data, schema drift, and quota failures explicitly.
  • The recommended implementation can run on ordinary CPU hardware.

Triggers

Keywords: AdMapix, raw data, app analytics, creative data, rankings, revenue, ETL, data quality

Example trigger sentences:

  • Use $software-data-admapix-raw-developer-helper to design a raw data layer for ad creative exports.
  • Clean this app-ranking CSV without losing the original source data.
  • Create validation checks for an AdMapix-style analytics skill.