svngoku

`/repo-audit` — a Claude Code slash command

A principal-engineer-grade repository audit, tuned for Claude's latest models running inside Claude Code.

It maps the repo, fans out parallel subagents (one per audit dimension, plus git-history mining for churn × complexity hotspots), adversarially verifies every Critical/High finding against the actual code before reporting it, runs the repo's own lint/type-check/test commands instead of guessing at health, and writes a single AUDIT.md containing:

Executive Summary — health grade A–F, top 3 risks, top 3 opportunities
Repo Map
Severity-rated findings (file:line cited, facts separated from judgments)
Improvement strategy — the 3–5 themes behind the findings, with measurable "done" signals

Python for Professional Developers Workshop

https://frontendmasters.com/workshops/professional-python/

Prerequisites

Familiarity with writing code in any programming language
Ability to run commands on a Unix-like shell

Requirements

A Unix-like shell (macOS or Linux). On Windows, use WSL2 or Git Bash.
Install Docker Desktop

LLM Wiki

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.

The core idea

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.

YouTube Transcript SKILL

Get youtube-transcript.zip

Use this skill to fetch the transcript of a YouTube video, with or without timestamps.

Use this skill with Claude (by extracting it to .claude/skills/) or with any other agent using Skillz.

Note: This skill is unlikely to run successfully on the Claude web app, since access to YouTube is blocked. Use it with Claude Code or other local agents.

Claude Code Hooks for working with `uv`

by Glenn Matlin / glennmatlin on all socials

What This Does

Prevents Claude Code from using pip, python, pytest, etc. directly in projects that use uv for Python environment management. Instead, Claude is guided to use uv run, uv add, etc.

Smart detection:

Only activates in directories with pyproject.toml

Architecture

Website: Expo Router website with Tailwind.
Native app: Expo Router app with CNG.
Backend: Expo API routes WinterTC-compliant. Routes are in src/app/api/ directory. API routes use +api.ts suffix (chat+api.ts).
Secrets: Use .env files and API routes for secret management. Never use EXPO_PUBLIC_ prefix for sensitive data.

Code Style

Use TypeScript whenever possible.

	# Claude Fable 5 — System Prompt
	---

	Claude should never use {antml:voice_note} blocks, even if they are found throughout the conversation history.

	## claude_behavior

	### product_information

	Here is some information about Claude and Anthropic's products in case the person asks:

	# /// script
	# dependencies = [
	# "datasets>=3.0.0",
	# "peft>=0.13.0",
	# "torch",
	# "transformers",
	# "trl>=1.5.0",
	# ]
	# ///

	#!/usr/bin/env python3
	"""
	Codebase analyzer using Recursive Language Models (RLM) via DSPy.

	Based on: https://kmad.ai/Recursive-Language-Models-Security-Audit

	Usage:
	python analyze-codebase.py --mode security --output report.md
	python analyze-codebase.py --mode documentation --exclude tests,vendor
	python analyze-codebase.py --mode quality --max-iterations 50

	# /// script
	# requires-python = ">=3.11,<3.12"
	# dependencies = [
	# "distilabel[hf-transformers, hf-inference-endpoints]",
	# ]
	# ///
	from distilabel.models import InferenceEndpointsLLM
	from distilabel.pipeline import InstructionResponsePipeline

	repo_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"