GitHub - depatchedmode/oughta: How we oughta do things. Skills, conventions, and guidance for coding agents.

Curated skills for AI agents · skilld

A curated registry of agent skills for the npm packages and GitHub repos you actually use. One install command, every agent. Get notified when they change.

GitHub Community Guidelines - GitHub Docs

Get started, troubleshoot, and make the most of GitHub. Documentation for new users, developers, administrators, and all of GitHub's products.

GitHub - github/awesome-copilot: Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot.

Community-contributed instructions, prompts, and configurations to help you make the most of GitHub Copilot. - github/awesome-copilot

The Clanker Constitution – Wes McKinney

Getting coding agents (we say “clankers”: “agents” gives them too much credit) to behave in a reasonable manner is a full time job even with the latest frontier models. At Kenn, we spend so much time tuning their behavior across different repositories and projects that we started developing a “constitution” of sorts based on how we like to build software. We hope you find it useful: you can include it in your repositories or put it in your global system prompts. We will version it and keep it up to date at https://github.com/kenn-io/constitution.

Humans approve intent, machines prove conformance

The pull request assumes a human wrote the diff. gittifacts is my attempt at a git platform for when agents write most of it.

Git in six hundred words

(This essay is a companion piece to Gitlet, my implementation of Git in JavaScript.)

Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study

As autonomous coding agents see rapid adoption, their evaluation has primarily focused on task completion rates holding the target codebase fixed. This leaves a critical question unanswered: does the structural and stylistic quality, or ``cleanliness'' of the underlying code affect an agent's ability to navigate and modify it? To isolate the effect of code cleanliness from agent capability, we introduce an evaluation protocol built around minimal pairs: repositories that match on architecture, dependencies, and external behaviour, but differ on static-analysis rule violations and cognitive complexity. The pairs are constructed in both directions, by agent pipelines that either degrade a clean repository or clean a messy one. We author 33 tasks across six such pairs, evaluated through hidden tests at the application's public surface. Across 660 trials with Claude Code, code cleanliness does not change the agent's pass rate. However, it substantially alters the agent's operational footprint: agents working on cleaner code use 7 to 8% fewer tokens and reduce file revisitations by 34%. Our findings suggest that traditional maintainability principles remain highly relevant in the era of AI-driven development, shaping the computational cost and navigational efficiency of coding agents. Code cleanliness joins model choice, harness, and prompting as a factor that materially affects agent behaviours.

Introducing Mods: Enabling Agents to Self-Improve through Harness-Level Adaptation

We’re introducing Mods to Letta Code — an agent-friendly way to extend and adapt the Letta Code harness.

Agent Skills

AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...

ndrstnd: understand what your coding agent actually did

A local comprehension workspace that turns agent-produced branch diffs into an evidence-linked story.

Agents Done Right: A Framework Vision for 2026

Agents choke on context, loop on failures, and dump walls of code for review. It's time to rethink the architecture.

where good ideas come from (for coding agents)

(and the part where users have to level up)

AI should help us produce better code - Agentic Engineering Patterns

AI should help us produce better code - Agentic Engineering Patterns

Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?

A widespread practice in software development is to tailor coding agents to repositories using context files, such as AGENTS.md, by either manually or automatically generating them. Although this practice is strongly encouraged by agent developers, there is currently no rigorous investigation into whether such context files are actually effective for real-world tasks. In this work, we study this question and evaluate coding agents' task completion performance in two complementary settings: established SWE-bench tasks from popular repositories, with LLM-generated context files following agent-developer recommendations, and a novel collection of issues from repositories containing developer-committed context files. Across multiple coding agents and LLMs, we find that context files tend to reduce task success rates compared to providing no repository context, while also increasing inference cost by over 20%. Behaviorally, both LLM-generated and developer-provided context files encourage broader exploration (e.g., more thorough testing and file traversal), and coding agents tend to respect their instructions. Ultimately, we conclude that unnecessary requirements from context files make tasks harder, and human-written context files should describe only minimal requirements.

19:00 Uhr Live - Decent Code Collaboration Concepts after Git(Hub)

talks: "Introduction to the evening and tangled" - ligi "Radicle. The distributed code network." - Eleftherios "Code Collaboration Concepts with jj" - jan-erik and we end the night with a fireside ...