clawd-aftermath

whale-index

0
0
# Install this skill:
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_traders β€” trader leaderboard
  • strategy_create_mirror β€” create mirror strategy
  • strategy_get_clearinghouse_state β€” check positions
  • strategy_close_strategy β€” 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.