Problem
ponytail-review relies entirely on LLM analysis to detect over-engineering in diffs. This is thorough but slow -- each review requires a full model inference pass. For large diffs or CI pipelines, this adds latency and cost.
Proposed addition
A standalone detectOverEngineering(text) function that uses regex patterns to quickly flag known over-engineering signals before the LLM does deeper analysis. Returns [{ pattern, alternative, snippet }] -- actionable findings that map directly to ladder rungs 2-4.
Patterns
| Regex |
Ladder Rung |
Native Alternative |
import (moment|dayjs|date-fns|luxon) |
3 -- Native platform |
Intl.DateTimeFormat / native Date |
import (lodash|ramda|underscore) |
2 -- Stdlib |
native Array/Object methods |
import (axios|node-fetch|got) |
3 -- Native platform |
native fetch() |
JSON.parse(JSON.stringify( |
3 -- Native platform |
structuredClone() |
new Date().toISOString() |
3 -- Native platform |
Date.now() |
| trivial getter/setter class |
6 -- One line |
direct property access |
| small function wrapping single expression |
6 -- One line |
inline expression |
Usage in ponytail-review
// Fast pre-filter: regex pass catches obvious patterns
const findings = detectOverEngineering(diffText);
// Then LLM does deeper analysis on remaining context
// findings are passed as hints to the model
This is a complement, not a replacement -- regex catches import-level signals; the LLM catches architectural over-engineering that regex can't see.
Source
I built this for a transparent compression proxy (adal-compress) that sits between AdaL CLI and the API. The function is standalone, MIT-licensed, and not coupled to the proxy. It has 32 unit tests covering edge cases.
The function lives in my compression engine at compression.mjs:719. Happy to extract it into a standalone module for easy integration.
Why this helps
- Faster reviews: regex pass is O(n) vs LLM inference
- CI-friendly: can run as a pre-commit check without model access
- Teaches the pattern: each finding includes the native alternative, reinforcing the ladder
- Complementary: catches import-level signals that the LLM might miss in large diffs
Problem
ponytail-reviewrelies entirely on LLM analysis to detect over-engineering in diffs. This is thorough but slow -- each review requires a full model inference pass. For large diffs or CI pipelines, this adds latency and cost.Proposed addition
A standalone
detectOverEngineering(text)function that uses regex patterns to quickly flag known over-engineering signals before the LLM does deeper analysis. Returns[{ pattern, alternative, snippet }]-- actionable findings that map directly to ladder rungs 2-4.Patterns
import (moment|dayjs|date-fns|luxon)Intl.DateTimeFormat/ native Dateimport (lodash|ramda|underscore)import (axios|node-fetch|got)fetch()JSON.parse(JSON.stringify(structuredClone()new Date().toISOString()Date.now()Usage in ponytail-review
This is a complement, not a replacement -- regex catches import-level signals; the LLM catches architectural over-engineering that regex can't see.
Source
I built this for a transparent compression proxy (adal-compress) that sits between AdaL CLI and the API. The function is standalone, MIT-licensed, and not coupled to the proxy. It has 32 unit tests covering edge cases.
The function lives in my compression engine at
compression.mjs:719. Happy to extract it into a standalone module for easy integration.Why this helps