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From spotify
Generate a magazine-style visual musical profile from your Spotify listening data
How this command is triggered — by the user, by Claude, or both
Slash command
/spotify:profileThe summary Claude sees in its command listing — used to decide when to auto-load this command
Generate a visual profile of the user's complete listening data as a single-file HTML artifact. ## Steps 1. Confirm setup is done — check that `~/.claude-spotify/token.json` exists. If not, tell the user to run `/spotify:setup` first and stop. 2. Gather data (this takes ~30 seconds for a library of a few thousand tracks): This pulls all liked tracks with timestamps, top artists/tracks across three time ranges, genres, decade spread, monthly activity, and the full artist tenure map. 3. Render the HTML: Output lands at `~/.claude-spotify/profile-out/listening-life.html`....
Generate a visual profile of the user's complete listening data as a single-file HTML artifact.
Confirm setup is done — check that ~/.claude-spotify/token.json exists. If not, tell the user to run /spotify:setup first and stop.
Gather data (this takes ~30 seconds for a library of a few thousand tracks):
python3 ${CLAUDE_PLUGIN_ROOT}/skills/spotify/profile.py
This pulls all liked tracks with timestamps, top artists/tracks across three time ranges, genres, decade spread, monthly activity, and the full artist tenure map.
Render the HTML:
python3 ${CLAUDE_PLUGIN_ROOT}/skills/spotify/render.py
Output lands at ~/.claude-spotify/profile-out/listening-life.html.
Open it:
open ~/.claude-spotify/profile-out/listening-life.html
Give the user the file path in case they want to save, move, or publish it.
npx claudepluginhub kaushalvivek/plugins --plugin spotify/profileProfiles application performance: analyzes CPU, memory, execution time, bottlenecks; generates markdown report with hotspots, patterns, breakdowns, and optimization recommendations with code fixes.
/profileAnalyzes codebase for performance bottlenecks, resource inefficiencies, and scalability concerns. Detects anti-patterns in DB, memory, async, frontend, and network; reports quantified findings by impact.
/profileRuns a static code profiler to detect backend performance anti-patterns and validates findings using LSP reference analysis.
/profileSwitches between built-in or custom profiles to adjust effort, autonomy, and verification settings in .vbw-planning/config.json. Supports listing profiles, saving new customs, direct switching, and deletion.
/profileProfiles dataset from CSV/Parquet/JSON file or database table, generating Markdown report with summary stats, column profiles, detailed statistics, quality flags, and recommendations.