Turn rough requests into rigorously structured prompts for any coding agent. Quality-scored to ≥90/100 across 12 dimensions, calibrated on 1,000+ real coding cases.
Enables evaluation of AI-generated code across 45 specialized dimensions using deterministic pattern matching and optional LLM-powered deep review, acting as an independent quality gate.
Score your specs before an LLM builds from them. Scores on 4 axes (completeness, clarity, constraints, specificity), calculates a balance score, and returns a verdict with radar chart visualization.
Enables AI assistants to reflect on, critique, and continuously improve their performance using Mandoline's evaluation framework. Provides tools for creating custom evaluation metrics and scoring prompt/response pairs to measure AI assistant quality.
Provides advanced evaluation tools for assessing AI safety, alignment, and performance of LLM outputs. Enables programmatic evaluation of quality, safety metrics like toxicity and PII detection, and operational metrics including carbon footprint and cost estimation.
Provides a quality framework and enforceable conventions for AI coding assistants, ensuring code quality, environment hygiene, and project standards across multiple AI tools.