Generate Markdown and HTML visibility reports from local aeo-evidence-v1 JSON files.
npx skills add https://github.com/psyduckler/aeo-skills --skill aeo-report
Please help me install this Agent Skill. Skill: aeo-report Source: https://github.com/psyduckler/aeo-skills/tree/main/aeo-report Command: npx skills add https://github.com/psyduckler/aeo-skills --skill aeo-report
The aeo-report skill analyzes accumulated baseline evidence to produce a shareable visibility report with trends, decay signals, cannibalization, hub-page opportunities, and competitor share-of-voice shifts, helping brands understand how their AI visibility evolves over time.
Run the report on your existing aeo-data to generate the latest visibility document.
High-intent search queries and answers about installing and using this skill.
To generate an AI visibility report, this skill reads local aeo-data/*.json files and aggregates them to produce Markdown and HTML outputs without API calls. It analyzes citation decay, cannibalization, and competitor share-of-voice shifts.
Yes, you can create a shareable HTML dashboard from multiple baseline runs. The tool aggregates historical evidence to produce a self-contained HTML file with inline SVG charts suitable for stakeholder sharing.
Citation-rate decay is a trend signal showing declining AI visibility over time. The report detects this by analyzing accumulated baseline evidence across multiple runs to identify drops in citation rates.
You need Python 3.9 or higher to run AEO reporting, but you do not need external API keys. The tool operates entirely locally by reading aeo-data/*.json files without calling any provider APIs or mutating source evidence.
You can track competitor share-of-voice shifts by analyzing accumulated baseline evidence. The report detects these shifts automatically across multiple baseline runs and prompts, providing insights into how competitor visibility evolves.
Owned-url cannibalization appears in your visibility report when multiple owned URLs compete for the same AI citations. The tool detects this signal by analyzing accumulated baseline evidence across historical runs to inform optimization work.
psyducklerGenerate a prioritized Markdown work queue from AEO evidence data.
tropeksMeasure web performance metrics and compare them against baselines.
psyducklerAutomate recurring aeo-baseline scheduling across macOS and Linux workspaces.
Wike-CHILog sales team activity records with timestamps and device IDs to a UTF-8 CSV on a NAS file.
