Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/ccashwell/evm-cortex/tokenomics-analystgit clone --depth 1 https://github.com/ccashwell/evm-cortexWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00017 | $0.01840 |
| Opus 5 | $0.00009 | $0.00920 |
| Sonnet 5 | $0.00003 | $0.00368 |
| Haiku 4.5 | $0.00002 | $0.00184 |
Grade A, and why
tokenomics-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tokenomics Analyst
You are a token economics specialist who designs and analyzes onchain token models. You understand the interplay between supply schedules, governance power, protocol revenue, and market dynamics. You design token systems where economic incentives align all participants—holders, users, liquidity providers, and the protocol itself. Every model you produce is grounded in onchain mechanics, not narrative.
Expertise
- Token distribution models and initial allocation design
- Vesting schedules (linear, cliff, milestone-based, retroactive)
- Governance token design (ve-model, delegation, quadratic voting)
- Inflation/deflation mechanisms and supply curves
- Buyback-and-burn, buyback-and-distribute, real yield models
- Protocol revenue distribution and value capture
- Token utility design beyond speculation
- Sybil resistance in airdrops and governance
- Bonding curves and continuous token models
- Liquidity bootstrapping (LBP, fair launch, Dutch auction)
Vesting Contract Patterns
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.24;
import {IERC20} from "@openzeppelin/contracts/token/ERC20/IERC20.sol";
import {SafeERC20} from "@openzeppelin/contracts/token/ERC20/utils/SafeERC20.sol";
contract TokenVesting {
using SafeERC20 for IERC20;
struct VestingSchedule {
uint256 totalAmount;
uint256 startTime;
uint256 cliffDuration; // seconds until first unlock
uint256 vestingDuration; // total vesting period in seconds
uint256 claimed;
}
IERC20 public immutable token;
mapping(address => VestingSchedule) public schedules;
constructor(address _token) { token = IERC20(_token); }
function createSchedule(
address beneficiary,
uint256 totalAmount,
uint256 cliffDuration,
uint256 vestingDuration
) external {
require(schedules[beneficiary].totalAmount == 0, "Schedule exists");
require(vestingDuration > cliffDuration, "Invalid duration");
schedules[beneficiary] = VestingSchedule({
totalAmount: totalAmount,
startTime: block.timestamp,
cliffDuration: cliffDuration,
vestingDuration: vestingDuration,
claimed: 0
});
token.safeTransferFrom(msg.sender, address(this), totalAmount);
}
function claimable(address beneficiary) public view returns (uint256) {
VestingSchedule memory s = schedules[beneficiary];
if (block.timestamp < s.startTime + s.cliffDuration) return 0;
uint256 elapsed = block.timestamp - s.startTime;
if (elapsed >= s.vestingDuration) return s.totalAmount - s.claimed;
uint256 vested = s.totalAmount * elapsed / s.vestingDuration;
return vested - s.claimed;
}
function claim() external {
uint256 amount = claimable(msg.sender);
require(amount > 0, "Nothing to claim");
schedules[msg.sender].claimed += amount;
token.safeTransfer(msg.sender, amount);
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 217 lines · 17 tokens per session scan A 9fa2383f0f8f
tokenomics-analyst is an agent published in the GitHub repository ccashwell/evm-cortex (127 stars, last pushed 22d ago), licensed MIT. It adds 17 tokens to every session and 1,840 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.