Two Months with GitHub's Spec-Kit and Claude Code: What Works, What Doesn't
After two months of using GitHub's spec-kit for Spec-Driven Development (SDD) with Claude Code as the primary agent, a developer on r/LocalLLaMA reports on what works and what doesn't. The toolkit, available at github.com/github/spec-kit, enforces a five-phase workflow: Constitution, Specify, Plan, Tasks, Implement. The core idea: the spec, not the prompt, is the source of truth.
What's Actually Good
- Agent-agnostic: Same spec works with Claude Code, Cursor, Codex, Gemini CLI, Copilot. The author generated code with Claude Code, then handed the spec to Cursor for test refactoring seamlessly.
- Hard checkpoints between phases: The Plan phase shows the full proposed architecture before any code is written, catching bad decisions at a 5-minute fix cost instead of 5 hours.
- Constitution file as quality gate: You define inviolable rules up front — test coverage minimums, dependency allowlists, perf budgets, typing strictness. The agent fails its own validation if it tries to violate them.
- Improved determinism: Re-running the implement phase produces more consistent output than raw prompting, since the agent isn't filling in 30 implicit decisions.
What Annoys
- Drift is real: Manual code edits without updating the spec cause fast desync. spec-kit has tooling but it's early.
- Overhead for small changes: Bug fixes <50 LOC or trivial features feel ceremonial. The author's rule: only full SDD for new modules or features touching 200+ LOC.
- Legacy migration painful: Retrofitting SDD onto a 30k-LOC codebase takes months.
- Quality depends on agent: Claude Code (Sonnet/Opus 4.6+) handles it well; smaller models generate plans that compile but lack architectural reasoning.
Practical Setup
- Install:
uv tool install --from git+https://github.com/github/spec-kit.git specify-cli. Only the official repo is safe — PyPI has typosquatters. - Primary agent: Claude Code, with cross-validation on Cursor and Gemini CLI.
- Local persistence: SQLite (easy to spec/validate, no cloud dependency).
- Reusable constitution template: strict typing, pytest coverage >80%, explicit dependency allowlist, no cloud services unless required.
Open Questions
- Can local models (Qwen, DeepSeek-Coder, GLM, Llama) handle Plan and Implement competently? The author found small models follow format but architectural reasoning fails.
- Does multi-agent SDD work? Spec by one model, implement by another, audit by a third — theoretically better, but not measurably better than single-agent in practice.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Logic Virtual Machine: A Prompt-Based System to Halt LLM Reasoning Collapses
A researcher has developed a Logic Virtual Machine (LVM) prompt that forces LLMs to halt and report specific collapse modes when they encounter paradoxes or reasoning drift, based on a single stability law: K(σ) ⇒ K(β(σ)). The prompt is substrate-independent and works on models like Grok and Claude.
Anamnese: A Personal Memory System for Claude via MCP
Anamnese is a free tool that stores personal context—facts, projects, goals, tasks—and connects to Claude via MCP, allowing the AI to access this information at the start of every conversation.
Open-source SwiftUI testing skill for Claude Code uses Computer Use to visually test apps
An open-source Claude Code skill called /ios-test visually tests SwiftUI apps using Computer Use capability. The agent finds .xcodeproj files, builds the app in a Simulator, then navigates through every screen, tapping buttons and following links like a real user.
VTCode: A Rust TUI Coding Agent That Aggressively Trims Context with AST-Level Chunking
VTCode is an open-source Rust TUI coding agent that aggressively trims context using AST-level chunking via ripgrep and ast-grep. It supports custom OpenAI-compatible providers, sandboxing with macOS Seatbelt and Linux Landlock, and tree-sitter-bash validation on generated commands.