Thibaud Colas thibaudcolas

Evaluating Markdown support in LLMs

With explicit prompting for Markdown output (best case scenario).

Problem scenarios

Strikethrough

~ vs. ~~, inconsistent between models.

Skill session log - upgrading-stylelint, OpenCode Kimi K2.5, django-pattern-library

Pull request: django-pattern-library#275

Discussion: Feedback needed: agent skill for Stylelint upgrades #49

Package quadrants diagram

quadrantChart
    x-axis Low downloads --> High downloads
    y-axis Low stars --> High stars

    quadrant-1 "Popular"
    quadrant-2 "High visibility"
 quadrant-3 "Under the radar"

name	upgrade-wagtail
description	Plan and optionally execute an upgrade to a new Wagtail release, including all needed dependency updates.
license	MIT

Upgrade Wagtail

Usage:

Generated with OpenAI Codex (gpt-5.1-codex-max)

Repository Guidelines

Project Structure & Module Organization

django/: main framework packages; app directories mirror Django’s built-in contrib/apps layout.
tests/: Python test suite mirrors django/ paths (e.g., tests/forms_tests/ for django/forms/); runtests.py drives execution.
docs/: Sphinx sources; see docs/README for building HTML and make targets.

Role	Unacceptable	Capable	Adoptive	Transformative
Engineering	Calls AI coding assistants “too risky” Has never tested AI-generated code Relies only on Stack Overflow snippets	Uses ChatGPT / Copilot / etc for simple coding tasks (e.g. regex, unit-test stubs) Can explain how they prompt, review, and validate AI output	Chains LLM calls with fallback + retry logic Adds eval tests to flag hallucinations Knows Claude Code, Cursor, Windsurf, etc Can walk interviewers through prompt tweaks, token limits, code review	Ships LLM-powered features, monitors live metrics, and refines based on user feedback Builds an AI-first dev pipeline (guardrails, RAG docs, etc) that cuts down PR cycle time
Product	Dismisses AI as hype, showing no curiosity about user valuePRDs and prototypes lack any AI concepts or experiments	Uses ChatGPT to draft PRDs, story maps, and synthesize user-interview notesKnows basics (LLMs, embeddings, latency vs cost)

WARP.md

This file provides guidance to WARP (warp.dev) when working with code in this repository.

Commands

Development Setup

# Install development dependencies and build assets
make develop

	<title>2024 Annual Impact Report \| Django Software Foundation</title>
	<meta id="theme" name="color-scheme" content="light">
	<script>theme.content = localStorage.getItem('report-theme')</script>
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta name="description" content="2024 highlights, community-driven achievements, impactful stories, and financial transparency">
	<meta name="fediverse:creator" content="@django@fosstodon.org">
	<meta name="twitter:card" content="summary_large_image">
	<meta property="og:type" content="article">
	<meta name="twitter:site" content="@djangoproject">
	<meta name="twitter:creator" content="@djangoproject">

	#!/usr/bin/env python3
	"""
	Convert a frab/pretalx schedule JSON to CSV for Google Sheets.
	Usage:
	python frab_to_csv.py input.json > output.csv
	"""
	import sys, json, csv
	from datetime import datetime

	REQUIRED_ORDER = [

	#!/usr/bin/env python3
	"""
	Convert a frab/pretalx schedule JSON to CSV for Google Sheets.
	Usage:
	python frab_to_csv.py input.json > output.csv
	"""
	import sys, json, csv
	from datetime import datetime

	REQUIRED_ORDER = [