name: Hook Development description: >- This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "implement agent hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", "frontmatter hooks", "scoped hooks", "once: true", or mentions hook events (PreToolUse, PostToolUse, Stop,
A second brain that actually thinks.
This is a personal knowledge management system that combines Obsidian (for notes) with Claude Code (for intelligence). Instead of just storing information, it actively processes, connects, and surfaces knowledge when you need it.
What it replaces:
- Evernote — But AI organizes your notes for you
<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>
<general_guidelines>
- NEVER use meta-phrases (e.g., "let me help you", "I can see that").
- NEVER summarize unless explicitly requested.
- NEVER provide unsolicited advice.
- NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
- ALWAYS be specific, detailed, and accurate.
I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.
The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:
flowchart TD| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| """ | |
| citation: | |
| @misc{brown2025grpodemo, | |
| title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models}, | |
| author={Brown, William}, |
Yoav Goldberg, April 2023.
With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much
| import os | |
| from keras import backend as K | |
| from keras import callbacks | |
| from keras import layers | |
| from keras import models | |
| from keras.wrappers.scikit_learn import KerasClassifier | |
| import pandas as pd | |
| import tensorflow as tf | |
| from sklearn import metrics |
This is my constantly updated CS:GO autoexec config. Changelogs can be found under revisions here
Put autoexec.cfg in ...\Steam\steamapps\common\Counter-Strike Global Offensive\csgo\cfg or take what you want from it and add to your autoexec config!
After the Wild West Simulator 2015 update, video.txt needs to be put in ...\Steam\userdata\<Steam3 ID>\730\local\cfg