Three skill frameworks promise to make coding agents write better code: an autonomous pipeline, a full-lifecycle system with adversarial guards, and a requirements-first toolkit. Their repos differ in philosophy — and a measured same-feature bake-off puts numbers on the tradeoffs: 78 minutes and $34 against 148 minutes and $92.
Spec-driven development works when the specification is a workflow, not a document filed away after kickoff. The point is a sequence of reviewable artifacts — requirements, design, tasks, implementation, validation — each reducing ambiguity before anyone, human or agent, changes production code.
Most coding agents keep getting bigger: planning modes, subagents, permission layers, IDE integrations, background workers. Pi walks the other way. Four default tools — read, write, edit, bash — with everything interesting left to extensions, skills, packages, and your own workflow. It is less a finished product than an editable harness, and that is exactly the point.
Claude Code isn’t autocomplete with better marketing — it’s a terminal-native junior engineer that needs review. This guide covers install, settings layering, self-hosted Ollama and llama.cpp backends, pricing rails, and troubleshooting.
A spec nobody re-checks against the running system is worse than no spec: reviewers trust the document instead of the diff while the agent follows whatever the code actually does. Agents regenerate code fast, and each regeneration is a fresh chance for spec and implementation to diverge. The fix is a small enforceable link between requirement, design decision, tests, and the commits that changed them.
AI-generated code is cheap; judgment is scarce. When models write the code, nobody remembers why PostgreSQL beat DynamoDB, why AI emails must stay drafts, or why suggestions live in a side panel. Decision records — short, version-controlled documents capturing what was decided, why, and what was rejected — turn that judgment into durable project memory both humans and agents can read.
Developers comparing spec-driven setups in 2026 usually aren’t asking which model is smartest. They’re asking which workflow keeps an AI agent aligned without burying them in ceremony. Spec Kit, Kiro, and Claude Code custom workflows all implement specify-plan-tasks-implement-validate — but they bet differently on portability versus integration depth.
Spec-driven development entered 2026 as the serious developer’s answer to vibe coding drift: implement against a reviewed specification, not an ad-hoc prompt. Hacker News called it “Waterfall Strikes Back.” Both sides have a point — and the practical question was never which side wins, but how much spec a specific task needs.
Most spec-driven setups fail the same way: a solid plan-implement skill gets used diligently for a week, then quietly abandoned the first time a deadline gets close. Nothing in a hand-rolled skill stops that drift. Superpowers answers the discipline problem directly — a skills package that makes brainstorming, planning, subagent review, and strict TDD mandatory steps the agent checks before every task.
OpenCode’s command-line interface turns the coding agent into a scriptable tool: the same agent environment runs in the interactive TUI and non-interactively from shell scripts, CI jobs, and Makefiles. Small tasks work within minutes, but serious use depends on repository instructions, permission boundaries, context discipline, and model selection.