Evaluate your LLM-powered apps with TypeScript
| Rating | Summary | |
|---|---|---|
| Quality | decent (15/24) | Actively maintained, excellent engineering signals, but missing README inflates documentation penalty severely. |
| PAI Relevance | watch (0.38) | TypeScript-native and drop-in compatible, but PAI already has a dedicated Evals skill covering the same function. |
Condition 5 (SKIP on overlap=2, gap_fill=0) is logically compelling but Condition 3 (WATCH, standalone >= 12) fires first by formula ordering. The repo is useful as a design reference even though it doesn't fill a PAI gap.
15/24 — actively-maintained / undocumented / high-discipline
Failed:
Passed:
Failed:
apps/evalite-docs package in the monorepo.Passed:
Failed:
Passed:
evalite-tests workspace package confirms test infrastructure.| Dimension | Score | Assessment |
|---|---|---|
| Harvest Value | 1 | The vitest-integration pattern (treating evals as test suites) and per-run trace capture with a local UI are design choices worth studying for PAI's existing Evals skill, but neither is a novel architecture PAI couldn't derive independently. |
| Integration Readiness | 2 | TypeScript-native, monorepo ships as an npm package (bun add -D evalite), exposes a CLI (evalite watch) — aligns perfectly with PAI's TypeScript+Bun+CLI-first stack. |
| Overlap Risk | 2 | PAI's Capability Manifest explicitly lists "Evals (AI agent evaluation)" under Development skills; this is near-complete functional overlap. |
| Gap Fill | 0 | PAI already has an Evals skill; evalite covers the same ground (LLM output scoring, test-driven prompt evaluation) without adding a capability PAI lacks. |
Composite: 0.38
Any TypeScript project that calls an LLM API: Run bun add -D evalite and wrap one existing LLM function — a summarizer, classifier, or prompt template — in an evalite suite with a string-similarity or custom scorer. Run bunx evalite watch during prompt iteration. Every change to the system prompt produces a recorded pass-rate delta in the local UI instead of manual spot-checks, making regressions visible before they ship.
CI pipeline for an LLM-backed feature: Add an evalite suite to the repo alongside unit tests and wire npx evalite into the CI step. Configure a score threshold (e.g., fail if semantic similarity drops below 0.8) to gate merges. The outcome is an automated quality gate on LLM output that runs on every PR, the same way type-checks and linting do.
Designing a custom eval harness from scratch: Review evalite's scorer interface and per-run trace storage as a reference design before building anything bespoke. The pattern — a scorer function returns a { score, metadata } object, results accumulate by run ID, a local UI queries the SQLite store — is reusable as an architecture even if you implement it independently in a different runtime or language.
Category: LLM & Prompt Tooling
In this category: first entry in this vault
Standing: evalite is the sole LLM & Prompt Tooling entry; no intra-category comparison is possible, though it clusters with garrytan--gbrain-evals in the cross-category evaluation overlap group.
Density: 8/10 — Available: repository metadata (stars, forks, dates, license, topics), monorepo package.json manifest, latest release tag, language classification, topic tags, prior appraisal context, landscape summary. Missing: README content (primary gap responsible for 0/8 doc score), individual package manifests for evalite and evalite-ui, CI configuration files.
The 0/8 documentation score is an artifact of README unavailability, not genuine undocumented status — the monorepo includes a dedicated apps/evalite-docs documentation site and Matt Pocock typically ships thorough docs for his TypeScript tooling. Real-world documentation quality is almost certainly higher than the score reflects; a re-appraisal with README content would likely push standalone score to 18-20 (solid to excellent). The WATCH verdict is appropriate as a design reference: evalite's scorer API and trace model are clean exemplars even if the tool itself overlaps with PAI's Evals skill.