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3 posts tagged with "claude-code"

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ponytailReading the Design of a Ruleset That Teaches Agents Not to Write

· 34 min read
Software Engineer

When you have an agent write code for you, you sometimes get back more than you asked for. Ask for a date input and it installs flatpickr, writes a wrapper component, adds a stylesheet, and opens a discussion about time zones. Something that works comes out, but the amount to read and the amount to maintain both grow.

ponytail is a ruleset for curbing that behavior. Dietrich Gebert publishes it under the MIT license, and in the three months since its release on June 12, 2026 it has reached 132,417 stars and 7,085 forks (figures as of September 9, 2026). Its version as a Claude Code plugin is 4.9.0. The GitHub repository description reads: "Make your AI agent think like the laziest senior developer in the room. The best code is the code you didn't write."

A Claude Code Best-Practice Collection: How to Navigate shanraisshan/claude-code-best-practice

· 51 min read
Software Engineer

Once you start adopting Claude Code at work, there comes a stage where even reading the official documentation makes it hard to see "what to adopt next" and "which one fits my level." The features Anthropic itself ships exceed 30 kinds; the workflows the community publishes number 12 or more; and the tips from Claude Code's developers like Boris Cherny and Thariq are scattered across 80-plus.

What helps organize that is shanraisshan/claude-code-best-practice, which won GitHub Trending #1 in March 2026. It is a repository published and continuously updated under the MIT license by Shayan Raisshan (organizer of the Claude Pakistan community, a Claude Community Ambassador). As of this writing (May 25, 2026), it is a curation collection with 445 files and a 585-line README.

Introduction to Harness Engineering: An AI Agent's Reliability Is Decided by the Design Around It

· 26 min read
Software Engineer

"I upgraded the model to the latest version, but it still repeats the same mistakes." "No matter how I tune the prompt, I hit a wall somewhere." When you try to put an AI agent to work, you always run into this wall. In many cases this is not a problem with the model itself; it stems from the design quality of the environment surrounding the model (= the harness). In this post I organize the idea of harness engineering — which was rapidly systematized starting in February 2026 — together with configuration examples from my own ~/.claude/ directory.