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Token economics simulation and analysis. Supports supply modeling, staking mechanisms, liquidity mining, governance dynamics, agent-based simulations, and cadCAD integration.
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npx skills add https://github.com/a5c-ai/babysitter --skill tokenomics명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | tokenomics |
| description | Token economics simulation and analysis. Supports supply modeling, staking mechanisms, liquidity mining, governance dynamics, agent-based simulations, and cadCAD integration. |
| allowed-tools | Read, Grep, Write, Bash, Edit, Glob, WebFetch |
| graph | {"domains":["domain:security"],"specializations":["specialization:cryptography-blockchain"],"skillAreas":["skill-area:token-nft-management","skill-area:dao-governance-implementation","skill-area:on-chain-data-oracle-integration"],"roles":["role:financial-analyst","role:data-scientist"]} |
Expert token economics simulation and analysis for protocol design.
# vesting_model.py
import numpy as np
pandas pd
:
():
.total_supply = total_supply
.allocation = {
: ,
: ,
: ,
: ,
:
}
.vesting = {
: {: , : },
: {: , : },
: {: , : },
: {: , : },
: {: , : }
}
() -> :
unlocked = {}
category, params .vesting.items():
allocation = .total_supply * .allocation[category]
month < params[]:
unlocked[category] =
month >= params[] + params[]:
unlocked[category] = allocation
:
elapsed = month - params[]
unlocked[category] = allocation * (elapsed / params[])
unlocked
() -> :
unlocked = .get_unlocked(month)
(unlocked.values())
# emission_model.py
class EmissionSchedule:
def __init__(
self,
initial_emission: float = 1000,
decay_rate: float = 0.9, # 10% decay per period
period_length: int = 365 # days
):
self.initial_emission = initial_emission
self.decay_rate = decay_rate
self.period_length = period_length
def get_daily_emission(self, day: int) -> float:
period = day // self.period_length
return self.initial_emission * (self.decay_rate ** period)
def get_cumulative_emission(self, days: int) -> float:
total = 0
for day in range(days):
total += self.get_daily_emission(day)
return total
# staking_model.py
class StakingPool:
def __init__(
self,
total_staked: float = 0,
reward_rate: float = 0.10, # 10% APY
lock_period: int = 30 # days
):
self.total_staked = total_staked
self.reward_rate = reward_rate
self.lock_period = lock_period
self.stakers = {}
def stake(self, address: str, amount: float):
if address not in self.stakers:
self.stakers[address] = {
'amount': 0,
'reward_debt': 0,
'lock_until': 0
}
self.stakers[address]['amount'] += amount
self.stakers[address]['lock_until'] = self.lock_period
self.total_staked += amount
def calculate_rewards(self, address: str, days: int) -> float:
if address not in self.stakers:
return 0
staker = self.stakers[address]
share = staker['amount'] / self.total_staked if self.total_staked > 0 else 0
daily_rate = self.reward_rate / 365
return staker['amount'] * daily_rate * days
def get_apy(self) -> float:
return self.reward_rate * 100
# ve_token_model.py
import math
class VeTokenModel:
def __init__(self, max_lock_time: int = 4 * 365): # 4 years max
self.max_lock_time = max_lock_time
self.locks = {}
def lock(self, address: str, amount: float, lock_days: int):
lock_days = min(lock_days, self.max_lock_time)
ve_balance = amount * (lock_days / self.max_lock_time)
self.locks[address] = {
'amount': amount,
'lock_days': lock_days,
've_balance': ve_balance,
'start_time': 0
}
return ve_balance
def get_voting_power(self, address: str, current_day: int) -> float:
if address not in self.locks:
return 0
lock = self.locks[address]
remaining = max(0, lock['lock_days'] - current_day)
return lock['amount'] * (remaining / self.max_lock_time)
# lp_rewards_model.py
class LPRewardsPool:
def __init__(
self,
reward_per_block: float = 10,
total_lp_tokens: float = 0
):
self.reward_per_block = reward_per_block
self.total_lp_tokens = total_lp_tokens
self.acc_reward_per_share = 0
self.last_reward_block = 0
self.users = {}
def deposit(self, user: str, amount: float, block: int):
self._update_pool(block)
if user in self.users:
pending = self._pending_rewards(user)
self.users[user]['pending'] += pending
if user not in self.users:
self.users[user] = {'amount': 0, 'reward_debt': 0, 'pending': 0}
self.users[user]['amount'] += amount
self.users[user]['reward_debt'] = \
self.users[user]['amount'] * self.acc_reward_per_share
self.total_lp_tokens += amount
def _update_pool(self, block: int):
if self.total_lp_tokens == 0:
self.last_reward_block = block
return
blocks = block - self.last_reward_block
rewards = blocks * self.reward_per_block
self.acc_reward_per_share += rewards / self.total_lp_tokens
self.last_reward_block = block
def _pending_rewards(self, user: str) -> float:
if user not in self.users:
return 0
return self.users[user]['amount'] * self.acc_reward_per_share \
- self.users[user]['reward_debt']
# impermanent_loss.py
def calculate_impermanent_loss(price_ratio: float) -> float:
"""
Calculate impermanent loss for Uniswap V2 style AMM.
price_ratio: new_price / initial_price
"""
return 2 * math.sqrt(price_ratio) / (1 + price_ratio) - 1
def il_vs_holding(initial_value: float, price_ratio: float) -> dict:
il = calculate_impermanent_loss(price_ratio)
lp_value = initial_value * (1 + il)
hold_value = initial_value * (1 + price_ratio) / 2
return {
'lp_value': lp_value,
'hold_value': hold_value,
'il_percentage': il * 100,
'il_dollar': hold_value - lp_value
}
# cadcad_model.py
from cadCAD.configuration import Configuration
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
# State Variables
initial_state = {
'token_price': 1.0,
'total_supply': 100_000_000,
'circulating_supply': 10_000_000,
'staked_supply': 0,
'treasury': 20_000_000
}
# Parameters
system_params = {
'staking_apr': [0.10, 0.15, 0.20],
'inflation_rate': [0.05],
'buy_pressure': [0.01, 0.02]
}
# State Update Functions
def update_price(params, step, sL, s, _input):
buy_pressure = params['buy_pressure']
sell_pressure = s['circulating_supply'] * 0.001
price_change = (buy_pressure - sell_pressure) / s['circulating_supply']
new_price = max(0.01, s['token_price'] * (1 + price_change))
return ('token_price', new_price)
def update_staking(params, step, sL, s, _input):
staking_apr = params['staking_apr']
stake_incentive = staking_apr * s['token_price']
new_staked = s['staked_supply'] + s['circulating_supply'] * stake_incentive * 0.1
return ('staked_supply', new_staked)
# Policies
def staking_policy(params, step, sL, s):
return {'stake_action': 'stake' if s['token_price'] > 0.5 else 'unstake'}
# Configuration
partial_state_update_blocks = [
{
'policies': {'staking': staking_policy},
'variables': {
'token_price': update_price,
'staked_supply': update_staking
}
}
]
# governance_model.py
class GovernanceSimulation:
def __init__(self, total_voting_power: float):
self.total_voting_power = total_voting_power
self.proposals = {}
self.quorum = 0.04 # 4% quorum
self.pass_threshold = 0.5 # 50% to pass
def create_proposal(self, id: str, description: str):
self.proposals[id] = {
'description': description,
'for_votes': 0,
'against_votes': 0,
'abstain_votes': 0,
'status': 'active'
}
def vote(self, proposal_id: str, voting_power: float, support: int):
proposal = self.proposals[proposal_id]
if support == 1:
proposal['for_votes'] += voting_power
elif support == 0:
proposal['against_votes'] += voting_power
else:
proposal['abstain_votes'] += voting_power
def execute(self, proposal_id: str) -> bool:
proposal = self.proposals[proposal_id]
total_votes = proposal['for_votes'] + proposal['against_votes']
# Check quorum
if total_votes < self.total_voting_power * self.quorum:
proposal['status'] = 'defeated'
return False
# Check threshold
if proposal['for_votes'] / total_votes >= self.pass_threshold:
proposal['status'] = 'executed'
return True
else:
proposal['status'] = 'defeated'
return False
| Process | Purpose |
|---|---|
economic-simulation.js | Protocol economics |
staking-contract.js | Staking design |
governance-system.js | Governance modeling |
yield-aggregator.js | Yield optimization |
skills/defi-protocols/SKILL.md - DeFi integrationagents/defi-architect/AGENT.md - DeFi expert