Audit Adobe Analytics data and generate a structured performance-profile.md context file.
npx skills add https://github.com/FunnelEnvy/funnelenvy-skills --skill aa-audit
Please help me install this Agent Skill. Skill: aa-audit Source: https://github.com/FunnelEnvy/funnelenvy-skills/tree/main/skills/aa-audit Command: npx skills add https://github.com/FunnelEnvy/funnelenvy-skills --skill aa-audit
Adobe Analytics data often requires manual auditing to identify trends, anomalies, and opportunities. This skill automates pulling and interpreting AA 2.0 reports to generate a structured performance-profile context file that informs downstream experiments and optimizations.
Run the aa-audit skill with a valid config to generate the performance profile
High-intent search queries and answers about installing and using this skill.
Automate an Adobe Analytics audit by running the aa-audit Python script with a valid client config file and Adobe credentials stored in environment variables. The script pulls AA 2.0 reporting data and generates a structured performance-profile.md file for your property.
A performance profile in Adobe Analytics data analysis is a structured markdown context file generated by interpreting AA 2.0 reports. It identifies trends, anomalies, and opportunities to inform downstream experiments and optimizations without manual data pulling.
Yes, you need Adobe credentials provided via environment variables alongside the aa_audit.py script to pull Adobe Analytics 2.0 reports. A valid config file is also required to authenticate and retrieve the reporting data successfully.
Yes, you can compare date ranges when auditing Adobe Analytics data. The audit skill supports configurable days-lookback parameters with optional date-range comparisons for single-property AA implementations to analyze performance trends over time.
The Adobe Analytics audit outputs a machine-actionable performance-profile.md file. This structured markdown context is saved to the .claude/context directory, making it readily accessible for informing downstream experiments and optimization workflows.
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