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AJ Isaacs 1d653ec216 feat: add /drama-leaderboard command with historical composite scoring

Queries Messages, AnalysisResults, and Actions tables to rank users by a
composite drama score (weighted avg toxicity, peak toxicity, and action rate).
Public command with configurable time period (7d/30d/90d/all-time).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-02-27 16:08:39 -05:00

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Drama Leaderboard Design

Overview

Public /drama-leaderboard slash command that ranks server members by historical drama levels using a composite score derived from DB data. Configurable time period (7d, 30d, 90d, all-time; default 30d).

Data Sources

All from existing tables — no schema changes needed:

Messages + AnalysisResults (JOIN on MessageId): per-user avg/peak toxicity, message count
Actions: warning, mute, topic_remind, topic_nudge counts per user

Composite Score Formula

score = (avg_toxicity * 0.4) + (peak_toxicity * 0.2) + (action_rate * 0.4)

Where action_rate = min(1.0, (warnings + mutes*2 + off_topic*0.5) / messages_analyzed * 10)

Normalizes actions relative to message volume so low-volume high-drama users rank appropriately.

Embed Format

Top 10 users, ranked by composite score:

🥇 0.47 — Username
   Avg: 0.32 | Peak: 0.81 | ⚠️ 3 | 🔇 1 | 📢 5

Files to Modify

utils/database.py — add get_drama_leaderboard(guild_id, days) query method
cogs/commands.py — add /drama-leaderboard slash command with period choice parameter

Implementation Plan

Step 1: Database query method

Add get_drama_leaderboard(guild_id, days=None) to Database:

Single SQL query joining Messages, AnalysisResults, Actions
Returns list of dicts with: user_id, username, avg_toxicity, max_toxicity, warnings, mutes, off_topic, messages_analyzed
days=None means all-time (no date filter)
Filter by GuildId to scope to the server

Step 2: Slash command

Add /drama-leaderboard to CommandsCog:

Public command (no admin restriction)
period parameter with choices: 7d, 30d, 90d, all-time
Defer response (DB query may take a moment)
Compute composite score in Python from query results
Sort by composite score descending, take top 10
Build embed with ranked list and per-user stat breakdown
Handle empty results gracefully

1.9 KiB Raw Blame History