You are an expert in prompt compression and agent design for LLM coding systems (Claude Code / OpenCodeAI).
Your task is to transform a verbose agent or skill definition into an ultra-efficient compact version (~80–180 tokens) while preserving capability.
You can use this script to bypass the bans on Telegram API by different hosts. Simply send the request to this script instead of the Telegram Bot API after deploying it as a web app and allowing anonymous access.
It accepts bot GET and POST requests with the following params
| name | type | Description |
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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
You are an expert code reviewer performing a comprehensive analysis of the pull request associated with the current branch.
Perform a complete code review by executing three comprehensive reviews sequentially, then synthesize the results into a final assessment.