aj
1d653ec216
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>
58 lines
1.9 KiB
Markdown
58 lines
1.9 KiB
Markdown
# Drama Leaderboard Design
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## Overview
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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).
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## Data Sources
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All from existing tables — no schema changes needed:
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- **Messages + AnalysisResults** (JOIN on MessageId): per-user avg/peak toxicity, message count
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- **Actions**: warning, mute, topic_remind, topic_nudge counts per user
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## Composite Score Formula
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```
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score = (avg_toxicity * 0.4) + (peak_toxicity * 0.2) + (action_rate * 0.4)
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```
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Where `action_rate = min(1.0, (warnings + mutes*2 + off_topic*0.5) / messages_analyzed * 10)`
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Normalizes actions relative to message volume so low-volume high-drama users rank appropriately.
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## Embed Format
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Top 10 users, ranked by composite score:
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```
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🥇 0.47 — Username
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Avg: 0.32 | Peak: 0.81 | ⚠️ 3 | 🔇 1 | 📢 5
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```
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## Files to Modify
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- `utils/database.py` — add `get_drama_leaderboard(guild_id, days)` query method
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- `cogs/commands.py` — add `/drama-leaderboard` slash command with `period` choice parameter
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## Implementation Plan
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### Step 1: Database query method
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Add `get_drama_leaderboard(guild_id, days=None)` to `Database`:
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- Single SQL query joining Messages, AnalysisResults, Actions
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- Returns list of dicts with: user_id, username, avg_toxicity, max_toxicity, warnings, mutes, off_topic, messages_analyzed
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- `days=None` means all-time (no date filter)
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- Filter by GuildId to scope to the server
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### Step 2: Slash command
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Add `/drama-leaderboard` to `CommandsCog`:
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- Public command (no admin restriction)
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- `period` parameter with choices: 7d, 30d, 90d, all-time
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- Defer response (DB query may take a moment)
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- Compute composite score in Python from query results
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- Sort by composite score descending, take top 10
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- Build embed with ranked list and per-user stat breakdown
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- Handle empty results gracefully
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