Hugging Face LLM Trainer
huggingface/skills
Trains or fine-tunes language and vision models with TRL or Unsloth on Hugging Face Jobs cloud GPUs, then converts the results to GGUF.
A skill your agent uses when implementing staking contracts, reward distribution systems, or yield farming.
$ npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ccashwell/evm-cortex staking-reward-patterns --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ccashwell/evm-cortex.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/staking-reward-patterns .claude/skills/staking-reward-patterns && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "staking-reward-patterns" agent skill from https://github.com/ccashwell/evm-cortex/tree/main/skills/staking-reward-patterns into .claude/skills/staking-reward-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "staking-reward-patterns", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ccashwell/evm-cortex/tree/main/skills/staking-reward-patternsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ccashwell/evm-cortex staking-reward-patterns --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ccashwell/evm-cortex.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/staking-reward-patterns .agents/skills/staking-reward-patterns && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "staking-reward-patterns" agent skill from https://github.com/ccashwell/evm-cortex/tree/main/skills/staking-reward-patterns into .agents/skills/staking-reward-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "staking-reward-patterns", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ccashwell/evm-cortex staking-reward-patterns --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ccashwell/evm-cortex.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/staking-reward-patterns .cursor/skills/staking-reward-patterns && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "staking-reward-patterns" agent skill from https://github.com/ccashwell/evm-cortex/tree/main/skills/staking-reward-patterns into .cursor/skills/staking-reward-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "staking-reward-patterns", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ccashwell/evm-cortex.git --path skills/staking-reward-patterns--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ccashwell/evm-cortex staking-reward-patterns --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ccashwell/evm-cortex.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/staking-reward-patterns .gemini/skills/staking-reward-patterns && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "staking-reward-patterns" agent skill from https://github.com/ccashwell/evm-cortex/tree/main/skills/staking-reward-patterns into .gemini/skills/staking-reward-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "staking-reward-patterns", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ccashwell/evm-cortex staking-reward-patternsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ccashwell/evm-cortex.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/staking-reward-patterns .github/skills/staking-reward-patterns && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "staking-reward-patterns" agent skill from https://github.com/ccashwell/evm-cortex/tree/main/skills/staking-reward-patterns into .github/skills/staking-reward-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "staking-reward-patterns", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ccashwell/evm-cortex staking-reward-patterns --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ccashwell/evm-cortex.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/staking-reward-patterns .opencode/skills/staking-reward-patterns && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "staking-reward-patterns" agent skill from https://github.com/ccashwell/evm-cortex/tree/main/skills/staking-reward-patterns into .opencode/skills/staking-reward-patterns/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "staking-reward-patterns", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
staking-reward-patternsA skill your agent uses when implementing staking contracts, reward distribution systems, or yield farming.
Staking Reward Patterns is an agent skill from ccashwell/evm-cortex. Use when implementing staking contracts, reward distribution systems, or yield farming. Covers the Synthetix reward model, per-second accrual, cooldown periods, and boosted reward mechanics.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Reinforcement learning. The repository describes itself as: Ethereum protocol engineering squad for AI coding assistants. The licence is MIT.
Read from SKILL.md and the folder at commit f8f3301. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are solidity).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Staking Reward Patterns loads about 1.7k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 103 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ccashwell/evm-cortex at commit f8f3301, republished under its MIT licence (© ccashwell). 103 words, ~1,739 tokens.
.claude/skills/staking-reward-patterns/SKILL.md (or your agent's skills folder).The industry-standard approach for distributing rewards proportionally to stakers without iterating over all stakers. Gas cost is O(1) per user action.
Core formula:
rewardPerToken = rewardPerToken + (elapsed * rewardRate / totalStaked)
earned(user) = balance(user) * (rewardPerToken - userRewardPerTokenPaid(user)) + rewards(user)// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
import {IERC20} from "@openzeppelin/contracts/token/ERC20/IERC20.sol";
import {SafeERC20} from "@openzeppelin/contracts/token/ERC20/utils/SafeERC20.sol";
import {ReentrancyGuard} from "@openzeppelin/contracts/utils/ReentrancyGuard.sol";
contract StakingRewards is ReentrancyGuard {
using SafeERC20 for IERC20;
IERC20 public immutable stakingToken;
IERC20 public immutable rewardToken;
address public rewardDistributor;
uint256 public rewardRate; // rewards per second
uint256 public periodFinish; // when current reward period ends
uint256 public lastUpdateTime;
uint256 public rewardPerTokenStored;
uint256 public totalSupply;
mapping(address => uint256) public balanceOf;
mapping(address => uint256) public userRewardPerTokenPaid;
mapping(address => uint256) public rewards;
uint256 public constant DURATION = 7 days;
constructor(address _stakingToken, address _rewardToken, address _distributor) {
stakingToken = IERC20(_stakingToken);
rewardToken = IERC20(_rewardToken);
rewardDistributor = _distributor;
}
modifier updateReward(address account) {
rewardPerTokenStored = rewardPerToken();
lastUpdateTime = lastTimeRewardApplicable();
if (account != address(0)) {
rewards[account] = earned(account);
userRewardPerTokenPaid[account] = rewardPerTokenStored;
}
_;
}
function lastTimeRewardApplicable() public view returns (uint256) {
return block.timestamp < periodFinish ? block.timestamp : periodFinish;
}
function rewardPerToken() public view returns (uint256) {
if (totalSupply == 0) return rewardPerTokenStored;
return rewardPerTokenStored + (
(lastTimeRewardApplicable() - lastUpdateTime) * rewardRate * 1e18 / totalSupply
);
}
function earned(address account) public view returns (uint256) {
return (
balanceOf[account] * (rewardPerToken() - userRewardPerTokenPaid[account]) / 1e18
) + rewards[account];
}
function stake(uint256 amount) external nonReentrant updateReward(msg.sender) {
require(amount > 0, "Cannot stake 0");
totalSupply += amount;
balanceOf[msg.sender] += amount;
stakingToken.safeTransferFrom(msg.sender, address(this), amount);
emit Staked(msg.sender, amount);
}
function withdraw(uint256 amount) external nonReentrant updateReward(msg.sender) {
require(amount > 0, "Cannot withdraw 0");
totalSupply -= amount;
balanceOf[msg.sender] -= amount;
stakingToken.safeTransfer(msg.sender, amount);
emit Withdrawn(msg.sender, amount);
}
function claim() external nonReentrant updateReward(msg.sender) {
uint256 reward = rewards[msg.sender];
if (reward > 0) {
rewards[msg.sender] = 0;
rewardToken.safeTransfer(msg.sender, reward);
emit RewardPaid(msg.sender, reward);
}
}
function exit() external {
withdraw(balanceOf[msg.sender]);
claim();
}
function notifyRewardAmount(uint256 reward)
external
updateReward(address(0))
{
require(msg.sender == rewardDistributor, "unauthorized");
if (block.timestamp >= periodFinish) {
rewardRate = reward / DURATION;
} else {
uint256 remaining = periodFinish - block.timestamp;
uint256 leftover = remaining * rewardRate;
rewardRate = (reward + leftover) / DURATION;
}
require(rewardRate > 0, "reward rate = 0");
require(
rewardRate * DURATION <= rewardToken.balanceOf(address(this)),
"reward amount > balance"
);
lastUpdateTime = block.timestamp;
periodFinish = block.timestamp + DURATION;
emit RewardAdded(reward);
}
event Staked(address indexed user, uint256 amount);
event Withdrawn(address indexed user, uint256 amount);
event RewardPaid(address indexed user, uint256 reward);
event RewardAdded(uint256 reward);
}uint256 public constant COOLDOWN_DURATION = 10 days;
uint256 public constant UNSTAKE_WINDOW = 2 days;
mapping(address => uint256) public cooldownStart;
function startCooldown() external {
require(balanceOf[msg.sender] > 0, "nothing staked");
cooldownStart[msg.sender] = block.timestamp;
}
function withdraw(uint256 amount) external {
uint256 cooldown = cooldownStart[msg.sender];
require(cooldown > 0, "cooldown not started");
require(block.timestamp >= cooldown + COOLDOWN_DURATION, "cooldown active");
require(
block.timestamp <= cooldown + COOLDOWN_DURATION + UNSTAKE_WINDOW,
"unstake window closed"
);
cooldownStart[msg.sender] = 0;
// ... transfer logic
}// Boost based on lock duration: longer lock = higher multiplier
function getBoost(address user) public view returns (uint256) {
uint256 lockEnd = lockEndTime[user];
if (lockEnd <= block.timestamp) return 1e18; // 1x (no boost)
uint256 remaining = lockEnd - block.timestamp;
uint256 maxDuration = 4 * 365 days;
// Linear boost: 1x to 2.5x based on lock duration
return 1e18 + (remaining * 15e17 / maxDuration);
}
function earned(address account) public view returns (uint256) {
uint256 base = balanceOf[account] * (rewardPerToken() - userRewardPerTokenPaid[account]) / 1e18;
return (base * getBoost(account) / 1e18) + rewards[account];
}updateReward modifier on every state-changing functionrewardPerToken() handles totalSupply == 0 (avoid division by zero)notifyRewardAmount checks sufficient reward token balanceReentrancyGuard on stake/withdraw/claimSafeERC20 for all token transfers© ccashwell, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/staking-reward-patterns of ccashwell/evm-cortex.
Open the folder on GitHubat commit f8f3301
Staking Reward Patterns next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Staking Reward Patterns this skillccashwell/evm-cortex | 131 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Hugging Face LLM Trainerhuggingface/skills | 11k | 3 repos | ~7.2k | Automated safety check: Pass | Apache-2.0 | |
| Train RlOpenPipe/ART | 11k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Qwopus27b Rl TrainingR6410418/Jackrong-llm-finetuning-guide | 1.7k | — | ~830 | Automated safety check: Pass | Apache-2.0 | |
| Fine Tuning With TrlOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Optim AgentOptim-Agent/optim-agent | 801 | — | ~1.3k | Automated safety check: Pass | MIT |
huggingface/skills
Trains or fine-tunes language and vision models with TRL or Unsloth on Hugging Face Jobs cloud GPUs, then converts the results to GGUF.
OpenPipe/ART
RL training reference for the ART framework. An agent skill from OpenPipe/ART.
R6410418/Jackrong-llm-finetuning-guide
Prepare, validate, launch-plan, monitor, resume, and stop configurable Qwopus 27B reinforcement-learning workflows for GRPO or GSPO.
Orchestra-Research/AI-Research-SKILLs
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training.
Optim-Agent/optim-agent
A skill your agent uses when the user wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies…
AI45Lab/SAfactory
Integrate a benchmark or custom environment into SAfactory using fixed adapter templates and local contract tests, optionally run Docker/RJob evaluation, or prepare GRPO/RL training.
ccashwell/evm-cortex
A skill your agent uses when preparing for a security audit, performing reconnaissance on a new codebase, or creating a protocol overview.
ccashwell/evm-cortex
A skill your agent uses when integrating with Aave V3 for lending, borrowing, flash loans, or building on top of Aave markets.
ccashwell/evm-cortex
Access control design patterns for Solidity protocols. An agent skill from ccashwell/evm-cortex.
ccashwell/evm-cortex
A skill your agent uses when running a local Ethereum node with Anvil.
ccashwell/evm-cortex
A skill your agent uses when performing systematic breadth-first review of all contracts during a security audit.
ccashwell/evm-cortex
A skill your agent uses when performing deep analysis of specific findings or high-risk areas during a security audit.
Categories
A skill your agent uses when implementing staking contracts, reward distribution systems, or yield farming. Staking Reward Patterns is an agent skill from ccashwell/evm-cortex. Use when implementing staking contracts, reward distribution systems, or yield farming.
Staking Reward Patterns fits situations like: implementing staking contracts; reward distribution systems.
Run `npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a claude-code`. Or copy the skill folder (skills/staking-reward-patterns in ccashwell/evm-cortex) into .claude/skills/staking-reward-patterns in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a codex`. Or copy the skill folder (skills/staking-reward-patterns in ccashwell/evm-cortex) into .agents/skills/staking-reward-patterns in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ccashwell/evm-cortex --skill staking-reward-patterns -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/staking-reward-patterns, .gemini/skills/staking-reward-patterns, .github/skills/staking-reward-patterns and .opencode/skills/staking-reward-patterns in your project.
SKILL.md names no scripts, command-line tools or credentials: Staking Reward Patterns is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Staking Reward Patterns is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.7k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Staking Reward Patterns: Hugging Face LLM Trainer (huggingface/skills, 11k stars), Train Rl (OpenPipe/ART, 11k stars), Qwopus27b Rl Training (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars) and Fine Tuning With Trl (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ccashwell (a GitHub user) maintains it in ccashwell/evm-cortex, which has 131 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on September 30, 2026.
Source: ccashwell/evm-cortex on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.