# Kayba AI Recursive Improve Recursive Improve > Kayba Ai Recursive Improve Recursive Improve - Skill: `tomevault-io/kayba-ai-recursive-improve-recursive-improve` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/kayba-ai-recursive-improve-recursive-improve` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/kayba-ai-recursive-improve-recursive-improve/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/kayba-ai-recursive-improve-recursive-improve --- # /benchmark — Metric Quality Benchmark Snapshot the current state of metric detectors, store results, and compare against previous benchmarks. ## Usage ### Run a new benchmark ```bash recursive-improve benchmark --label "description-of-changes" ``` This will: 1. Run all metric detectors against the traces 2. Evaluate metric quality (detector runs, count parity, denominator quality, spread, coverage) 3. Store results in `eval/benchmark_results.json` 4. Auto-compare against the most recent previous benchmark 5. Print a summary table ### List all benchmarks ```bash recursive-improve benchmark list ``` Shows the history of all stored benchmarks with scores and deltas. ### When to run - **Before making changes** — label it with the current state (e.g., `--label v1-baseline`) - **After improving metrics** — label it with what changed (e.g., `--label fix-denominator-scoping`) - **After each ratchet iteration** — automatic if using the ratchet loop ### Reading the output Quality signals (0-1, higher = better): - **detector_run_rate** — fraction of metrics whose detector code runs without errors - **count_parity_rate** — fraction where len(event_ids) matches denominator - **denominator_quality** — average denominator sanity score (not too small, no extreme values) - **spread_quality** — failures spread across traces, not concentrated in 1-2 - **skill_coverage_rate** — fraction of skills with a metric or unmeasurable classification - **composite_quality** — weighted overall score Per-skill metrics are also stored for detailed analysis. --- > Source: [kayba-ai/recursive-improve](https://github.com/kayba-ai/recursive-improve) — distributed by [TomeVault](https://tomevault.io).