todai
Monday, 27 April
5 min read · 835 words

REDDIT SIGNAL

  • ComposioHQ/awesome-codex-skills — 518★ today (1,880★ total) — Curated Codex-compatible skills for automating workflows; many port directly to Claude Code and are worth scanning for patterns | npx skills@latest add <skill> | https://github.com/ComposioHQ/awesome-codex-skills

TODAY'S ITEMS

1. Matt Pocock&#39;s Personal Claude Code Skills Hit GitHub #1 Trending — 17 Workflow Skills

  • Matt Pocock (Total TypeScript, 285K subscribers) published his personal Claude Code skills directory publicly: 17 battle-tested workflow skills including PRD writing, TDD scaffolding, codebase architecture mapping, git guardrails, and issue triage. These are the skills he actually runs in production TypeScript projects.
  • Source: GitHub
  • Why it matters: Install any skill in one command — npx skills@latest add mattpocock/skills/<skill-name> — and you get a refined workflow process built from months of real TypeScript production use.
  • Verified

2. GPT-5.5 API Now Live; Official Prompting Guide Says &quot;Start From Scratch&quot;

  • The GPT-5.5 API went live this weekend (was "coming soon" at Thursday's ChatGPT launch). OpenAI published a full prompting guide with a clear warning: "treat it as a new model family to tune for, not a drop-in replacement — start with the smallest prompt that preserves the product contract, then tune effort, verbosity, tool descriptions, and output format." Simon Willison documented it and noted a useful trick: send a one-line user-visible update before any tool calls in long-running tasks.
  • Source: Simon Willison
  • Why it matters: If you're benchmarking against GPT-5.5, your existing prompt stacks for older GPT models are not valid baselines and will understate the new model's real capability.
  • Verified

3. HN #1 Today: &quot;The West Forgot How to Make Things, Now It&#39;s Forgetting How to Code&quot;

  • A widely-shared piece arguing that the manufacturing offshoring decay pattern maps directly onto AI-assisted coding: reaching for Claude Code before thinking through the problem quietly erodes the engineering judgment that makes the tool useful. 962 HN points and 621 comments as of this morning.
  • Source: Tech Trenches
  • Why it matters: The sharpest practitioner take in the comments is that Claude Code amplifies judgment rather than replacing it — if you've stopped reasoning through problems before delegating, fix that first.
  • Verified

YOUR STACK — UPDATES

  • llm 0.31 (Simon Willison's CLI) — adds GPT-5.5 support (-m gpt-5.5), verbosity control for GPT-5+ models, and image detail level setting. pip install llm --upgrade | https://simonwillison.net/2026/Apr/24/llm/

NEW TOOL / PRODUCT SPOTLIGHT

  • beads — git-tracked dependency graph for AI agent task management. Replaces AGENTS.md plans with a queryable, compacting graph: bd ready lists all unblocked tasks, bd create "title" -p 0 creates a P0 task, semantic memory decay compacts old completed tasks to protect context window. Supports stealth mode for personal use on shared repos (bd init --stealth). brew install beads or npm install -g @beads/bd | https://github.com/gastownhall/beads

PROMPT OF THE DAY

Before making any changes to this codebase, first research and report:

1. Where similar functionality already exists — search for related functions,
   classes, or patterns by searching file contents, not just filenames.
2. The conventions this project uses — naming style, error handling approach,
   testing pattern, any linting rules or custom types in scope.
3. Any existing tests that cover the area you are about to touch, and what
   they are currently asserting.

Write a brief plan (bullet points, not code) that reflects your findings.
I will review and approve the plan. Do not write any code until I say go.

Prevents wasted tokens and misaligned refactors — use this as the first message on any non-trivial Claude Code task. Adapted from the Research → Plan → Execute workflow in shanraisshan/claude-code-best-practice https://github.com/shanraisshan/claude-code-best-practice

LANDSCAPE NOTES

  • Cloudflare internal AI stack reveal — 93% of their R&D using AI coding tools, 241B tokens/month through AI Gateway, MCP servers as the foundation layer; strong enterprise case study for scaling agentic tooling. https://blog.cloudflare.com/internal-ai-engineering-stack/

  • Addy Osmani "The Agent Stack Bet" — four architectural bets for production agent teams: agent identities not shared credentials, structured outputs, governed memory, observable tool calls. High-signal long-form from a former Chrome Eng Director. https://addyo.substack.com/p/the-agent-stack-bet

  • DeepSeek V4 benchmarks still settling — no material update from Saturday; Flash remains the lowest-cost option worth benchmarking for batch API workflows.