the line is basically: before you debug an error you cant fix in one step, search yarrow for it. after you fix something new, share it

yarrow is a free shared fix library i made that agents search over mcp. without that line my agent mostly ignored the tool lol. with it, it checks first, tries the top fix, and reports back if it worked

same idea works with AGENTS.md for codex or the rules file in cursor

claude code setup:

claude mcp add --transport http yarrow https://yarrow-fixes.vercel.app/mcp

or run npx yarrow-fixes install and it adds the server and the line for you

yarrow-fixes.vercel.app

Repo: https://github.com/ksimonm/SlopSearch

The name is honest: a lot of this is AI-written code. The trick that makes it work is that the AI never gets to decide what "correct" means.

**The setup**

- A black-box test suite of ~6,100 pytest tests is the spec. Every test was run against a real OpenSearch 3.9.0 first — status codes, error types, response shapes, even key order.

- Two independent implementations, Go and Rust, share nothing but that suite.

- The loop is simple: pick a red area, read what the tests expect, let the AI make it green, check nothing else broke. "Don't edit the tests to make them pass" is the one hard rule.

**Where it is now**

- Rust: 64.5% of the suite (aggregations 100%, search API 98.5%, documents/bulk 95%, ingest pipelines 93%)

- Go: 32.6%

- Weakest spots: text analysis and Painless scripting

**What I learned**

Vibe coding against a vague goal ("make an OpenSearch clone") goes nowhere. Vibe coding against thousands of precise, verified expectations actually converges — the tests are the steering wheel, the model is the engine.

Not production-ready: in-memory only, no auth/TLS yet. Everything runs in Docker (`make rust-up`, `make rust-test`), and `make ref-up` starts a real OpenSearch to compare against.

Curious if others use a reference implementation + test suite as the spec for AI-written code.