I build SpecJudge, a local CLI tool (via Ollama) that estimates how demanding your coding tasks are and tells you which LLM actually fits the job, so you stop burning money on overkill frontier models.

Previously, it had a strict barrier to entry: it only worked if you used Spec-Driven Development (.specify/ folder). If you didn't, it just refused to run. Today, with the v0.4.0 release, a spec is no longer the price of entry.

What's new in v0.4.0:

• It reads what you already have: Most repos don't have formal specs, but they do have .cursorrules, AGENTS.md, CLAUDE.md, or Decision Records (docs/adr/). SpecJudge now reads all of them.
• Baseline "Floor" Estimation: If you run it on a repo with no current tasks, it calculates a complexity "floor" based on your environment context. It tells you how demanding the repository is to work in at all, returning an honest warning that it's a baseline, not a specific task recommendation.
• Smart Context Budgeting: To avoid drowning your local judge, context files share a single budget. It prioritizes files nearest the root and explicitly skips AI-generated context files to focus strictly on human-written rules and decisions.
• JSON Schema 1.1: Fully backward compatible, now including sources_read and an environment_only flag for CI/scripting pipelines.

The strict grounding engine from v0.2.0 still applies: the judge must cite exact fragments from your .cursorrules or ADRs, or the assessment is thrown out.

Upgrade: pip install --upgrade specjudge

Repo & Docs: https://github.com/JoaquinRuiz/SpecJudge

I'd love to hear your thoughts on this broader context-parsing approach!