Eight Hooks That Guarantee AI Agent Reliability
CLAUDE.md rules get followed about 80% of the time. Hooks get followed 100% of the time. After six months of testing, these are the eight I never removed.
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CLAUDE.md rules get followed about 80% of the time. Hooks get followed 100% of the time. After six months of testing, these are the eight I never removed.
I installed three popular Claude Code extensions and productivity barely moved. The problem was never which tools to pick.
I edited config.toml, wrote rules in AGENTS.md, and nothing stuck. Turns out the folder structure itself was the issue, not my settings.
OpenAI shipped Codex as a Claude Code plugin on the same day Anthropic announced Computer Use. I think it's the smartest concession of 2026.
A month ago I couldn't leave my laptop during a build. Three features in four weeks fixed that.
I thought a single SKILL.md file was enough. Then I saw how Anthropic's own team structures theirs, and rebuilt everything.
I tested dozens of design skills for AI coding agents. Most didn't last a week. These 12 are the ones I still use.
I spent a year getting wildly inconsistent results from Claude Code and Codex. Three spec files, each with a distinct role, fixed it.
Subscribing puts you in the top 0.3%. These five configurations — agents, teams, MCP, monitoring, automation — push you into the top 0.01%.
I classified every term I kept encountering while using Claude Code and Codex daily. Five groups emerged, and they map the entire system these tools run on.
I dug into SDK type definitions and system prompts for both tools. The 29 vs 7 gap isn't about feature count. It's about two fundamentally different answers to the same question: how should an AI coding agent interact with your system?
After a year of agent-assisted development, I found that structured spec files fixed the inconsistency problem better than any prompt technique.
A race condition between Auto Memory and context compaction in Claude Code v2.1.59–v2.1.61 broke prompt caching and corrupted sessions. Anthropic reset all weekly limits as compensation.
Agentation gives AI agents pixel-perfect visual feedback via CSS selectors. Readout replays Claude Code sessions like video. Together they eliminate the two biggest friction points in AI-assisted frontend development.
Five SKILL.md body writing principles buried in Anthropic's official documentation. From separating description and body roles to embedding verification loops.
A practical guide to Claude Code's new multi-agent teams feature: activation, keyboard shortcuts, terminal compatibility, task management, and known limitations.
Boris Cherny's workflow hit 5K likes in 2 hours. His setup is simpler than you'd expect - parallel sessions, plan mode, CLAUDE.md, and verification loops.
An Anthropic hackathon winner's 10-month Claude Code configuration - context management, hooks, subagents, and the principles that actually matter.
After installing hundreds of AI coding agent skills, only 4 made it into my daily workflow. Here's what survived the weekend audit.
A game-style status bar for Claude Code that shows context usage, active tools, sub-agents, and todo progress in real time.
Connecting Context7 via MCP floods your main context with docs. Skills and subagents isolate queries, keeping long coding sessions stable.
With AI reading 50% of developer docs and bot traffic outpacing humans 3-to-1, services are racing to package their knowledge as agent skills. Here's what's driving the shift.
Andrej Karpathy admits he's never felt this behind as a developer. Here's the new AI agent abstraction layer he says you must master - or risk falling 10x behind.
Not all multi-agent patterns are equal. Learn when subagents, skills, handoffs, and routers actually outperform a single agent - with real scenarios and numbers.
A deep dive into Oh-My-OpenCode's multi-agent orchestration architecture - how programmatic context isolation, parallel execution, and evidence-based research are redefining what AI coding agents can do.
Opencode's open-source documentation doubles as an introductory guide to agent architecture. Here are the seven core concepts every developer should understand.
How a Claude Code plugin named after Ralph Wiggum is redefining autonomous coding through iterative loops, memory architecture, and stop hooks.
Six battle-tested AI agent patterns that emerged globally in one month - from persistent loops to multi-agent orchestration.
Context engineering took the world by storm in early 2026. Here are six battle-tested principles from Manus, Cursor, and Claude Code that define modern AI agent development.
Anthropic replaced TodoWrite with Tasks and Slash Commands with Skills in two days. Both changes point in the same direction - unhobbling the model.
How I use Ghostty, Yazi, Fish, and LazyGit to run multiple AI agents in parallel - a lightweight terminal stack built for agentic workflows.