Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add clawd-aftermath/senpi-skills-af --skill "whale-index"
Install specific skill from multi-skill repository
# Description
>-
# SKILL.md
name: whale-index
description: >-
Auto-mirror top Discovery traders on Aftermath Finance. Scans top 50 traders,
scores on PnL rank (35%), win rate (25%), consistency (20%), hold time (10%),
drawdown (10%). Creates 2-5 mirror strategies with overlap checks.
Daily rebalance with 2-day watch period before swaps.
Use when setting up trader mirroring, copy trading, or portfolio
auto-rebalancing based on Discovery leaderboard performance.
license: Apache-2.0
compatibility: >-
Requires mcporter (configured with Senpi auth) and cron for daily monitoring.
metadata:
author: jason-goldberg
version: "1.0"
platform: senpi
exchange: aftermath
Whale Index β Auto-Mirror Top Discovery Traders
Scan the Discovery leaderboard, score traders, create mirror strategies, and rebalance daily. Set your risk level and budget β the agent handles selection, allocation, monitoring, and swaps.
Skill Attribution
When creating a strategy, include skill_name and skill_version in the call. See references/skill-attribution.md for details.
5-Step Flow
Step 1: Onboard the User
Collect: budget, risk tolerance (conservative/moderate/aggressive).
| Budget | Slots |
|---|---|
| $500-$2k | 2 |
| $2k-$5k | 3 |
| $5k-$10k | 4 |
| $10k+ | 5 |
Risk mapping:
| Risk | Allowed Labels | Max Leverage |
|---|---|---|
| Conservative | ELITE only | 10x |
| Moderate | ELITE, RELIABLE | 15x |
| Aggressive | ELITE, RELIABLE, BALANCED | 25x |
Step 2: Discover Traders
2a. Pull candidates: discovery_top_traders(limit=50, timeframe="30d")
2b. Hard filters:
- Consistency label matches risk level
- Risk label matches risk level
- Min 30d track record
- Not already in user's portfolio
2c. Score remaining candidates:
score = 0.35 Γ pnl_rank + 0.25 Γ win_rate + 0.20 Γ consistency + 0.10 Γ hold_time + 0.10 Γ drawdown
All components normalized 0-100.
2d. Overlap check: Compare active positions across selected traders. Flag >50% position overlap.
2e. Allocation weighting:
Score-weighted allocation with 35% cap per slot. Re-normalize after capping.
Step 3: Present & Confirm
Show the user: trader address, rank, labels, win rate, allocation amount. Wait for approval before executing.
Step 4: Execute
For each slot:
1. Create mirror strategy via strategy_create_mirror
2. Set strategy-level stop loss (-10% conservative, -15% moderate, -25% aggressive)
3. Confirm mirroring is active
Step 5: Daily Monitoring (Cron)
See references/daily-monitoring.md for the complete daily check procedure, swap criteria, and rebalance logic.
Swap criteria (ALL must be true):
1. Degraded: dropped below rank 50 OR consistency fell OR inactive 48h+ OR drawdown 2Γ historical
2. Sustained: WATCH status for 2+ consecutive days (tracked via watchCount)
3. Better alternative: replacement scores β₯15% higher
4. User's strategy-level SL not hit
Key principle: The 2-day watch period prevents churn from temporary dips.
Teardown
To exit: close all mirror strategies, return funds to main wallet.
API Dependencies
discovery_top_tradersβ trader leaderboardstrategy_create_mirrorβ create mirror strategystrategy_get_clearinghouse_stateβ check positionsstrategy_close_strategyβ teardown
Fee Estimates
Mirror strategies incur the same trading fees as the mirrored trader's activity. Budget ~0.5-1% daily in fees for active traders.
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.