A Repo of Open Agent Skills
Just a pointer to a GitHub repo of agent skills. Low signal on its own, but these skill collections are where the real how-to lives. Clone it, read how the skills are structured, keep what fits.
Issue · June 1–7, 2026
16 reads.
Just a pointer to a GitHub repo of agent skills. Low signal on its own, but these skill collections are where the real how-to lives. Clone it, read how the skills are structured, keep what fits.
An honest field report: dynamic workflows felt useless until they were pointed at adversarial review. The move is splitting a review into thin, mean, single-focus lanes, correctness, duplication, safety, each agent required to bring a way to verify its own finding. It beats self-review because the model likes its own work too much. The step-by-step is copy-and-adapt ready.
Anthropic's own writeup on building agents that do data analysis. The useful parts are the unglamorous ones: skills, clean data foundations, and evaluations. If you're wiring an agent into real business numbers, this is the part everyone skips and then regrets.
The line most people skim: every agent in a workflow can run a different model. This is the proof. A 33-agent audit, cheap readers plus one smart synthesizer, ran in under five minutes and found a real buried error. Put the expensive model where the thinking happens and run the rest on the cheap one. That's the economics of parallel agents in one example.
The clearest map of where interfaces are heading: agents drawing the UI in real time instead of describing it. Three patterns, controlled, declarative, open-ended, each with a different failure mode at scale, and most teams pick one by accident. If you build anything agent-facing, this is the decision tree to read before you're locked in.
A clean way to think about agent design: the model is deliberately thin, and intelligence gets pushed outward into memory, skills, and protocols that the harness composes at runtime. The useful question it hands you is where any new capability should live. Good conceptual scaffolding if agents still feel like a bag of tricks.
A short thread on a real problem: when agents do the work, how do you actually understand what got done? The value is in seeing how people inside Anthropic keep oversight without babysitting. Worth a scroll if your agents are starting to outrun your attention.
The canonical piece on dynamic workflows, from the Anthropic engineer who built them. Claude writes its own custom harness on the fly to beat the failure modes of one long context window: laziness, self-preference, goal drift. The example prompts alone are worth the read, and it names the reusable patterns (fan-out, adversarial verify, tournament) you'll see everywhere else. Start here.
A design studio's field report on a real shift: the deliverable is no longer a PDF brand guide but a folder of structured files an agent can build from. The value moves upstream to the thinking, and the sharp bit is what they call magic_trick.md, the one human, left-of-center idea the system can't generate on its own. Best essay here on where human creativity stays scarce.
A sharp look at how youth sports became a $40 billion industry pricing out ordinary families. Not an AI piece, but a clear read on how private capital reshapes a market that used to be a childhood pastime. Worth it if you care about where consolidation goes when it meets kids and weekends.
A pointed demo of where the moat isn't: someone cloned the core of two heavily funded legal-AI products in two weeks and open-sourced it. The argument, that firms should own their application layer instead of renting it forever, is the uncomfortable question every vertical SaaS company is now facing. Provocative, and hard to dismiss.
Karpathy on a workflow more people should steal: point an LLM at a pile of raw sources and let it compile and maintain a markdown wiki you rarely touch by hand. Once it's big enough, you query it like a research assistant, no fancy RAG required. The best part is that your own explorations file back in, so the knowledge base compounds. One of the most-shared AI ideas of the year for a reason.
Karpathy's follow-up carries the sharper point: in the agent era you stop shipping code and start shipping the idea, kept deliberately vague, and the other person's agent builds it for their needs. It's a small reframe with big implications for how software gets distributed. The knowledge-base spec is the worked example.
A genuinely good step-by-step on running Claude Code 24/7 on an always-on Mac Mini so you can text it tasks from anywhere. The best detail is the one nobody mentions: there's no message queue, so a sleeping laptop drops everything, which is the whole case for dedicated hardware. Practical if you want a personal agent that never goes dark.
A crisp reminder of what belongs in your project config. Most hand-written setup was stuff the agent can discover by reading the code. The new approach keeps only what Claude would get wrong without it: security rules, mandatory workflows, non-obvious gotchas. Delete the rest. 458 lines to 68.
The reference for anyone configuring Claude Code seriously. It walks the whole control center: CLAUDE.md, path-scoped rules, and hooks, with the key nuance that instructions are suggestions but hooks are deterministic. Keep CLAUDE.md under 200 lines or adherence drops. This is the one to bookmark.