| name | govuk-style | |||||||
|---|---|---|---|---|---|---|---|---|
| description | Write and edit in GOV.UK / GDS house style — plain English, active voice, front-loaded content, sentence case, and no bold or italics for emphasis. Use when writing or editing reports, research write-ups, guidance, documentation, summaries, or any prose where clarity and accessibility matter. | |||||||
| user-invokable | true | |||||||
| args |
|
Discover gists
| #!/bin/bash | |
| # | |
| # This script takes one or more x509 certificates in .PEM format (from | |
| # stdin or files listed on command line) and adds helpful "bag | |
| # attributes" before each certificate. This makes it easier for | |
| # humans to identify the contents of the bundle. | |
| # | |
| # Requires (g)awk and openssl's x509 command line utility. | |
| # | |
| # Output fields included can be specified via openssl-x509 options: |
TL;DR — AgentRouter is a non-profit, OpenAI-compatible API gateway that aggregates Claude, GPT, Gemini, DeepSeek, and 30+ other models behind a single endpoint. New users get $200 in free credits via referral — no credit card required. Ideal for developers who want model flexibility without juggling five subscriptions.
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This document captures current best practices for R development, emphasizing modern tidyverse patterns, performance, and style. Last updated: August 2025
- Use modern tidyverse patterns - Prioritize dplyr 1.1+ features, native pipe, and current APIs
- Profile before optimizing - Use profvis and bench to identify real bottlenecks
- Write readable code first - Optimize only when necessary and after profiling
- Follow tidyverse style guide - Consistent naming, spacing, and structure
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.
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no"> | |
| <title>Super Platformer Adventure</title> | |
| <style> | |
| body { | |
| margin: 0; | |
| padding: 0; |
Unofficial Python script to decrypt the Income Tax Department's encrypted AIS JSON files downloaded for the AIS Utility.
This script currently handles the observed file format:
- first 32 hex characters: IV
- next 32 hex characters: salt
- remaining data: ciphertext in Base64 or hex
- key derivation: PBKDF2-HMAC-SHA256, 1000 iterations, 32-byte key
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no"> | |
| <title>Magenta Teletris MVP</title> | |
| <script src="https://cdn.tailwindcss.com"></script> | |
| <style> | |
| body { | |
| background-color: #111; |