Benchmark e2e

Benchmark E2E

Single-command pipeline that creates projects, exercises skill injection via claude --print, launches dev servers, verifies they work, analyzes conversation logs, and generates actionable improvement reports.

Quick Start

# Full suite (9 projects, ~2-3 hours)
bun run scripts/benchmark-e2e.ts

# Quick mode (first 3 projects, ~30-45 min)
bun run scripts/benchmark-e2e.ts --quick

Options:

Flag	Description	Default
--quick	Run only first 3 projects	false
--base <path>	Override base directory	~/dev/vercel-plugin-testing
--timeout <ms>	Per-project timeout (forwarded to runner)	900000 (15 min)

Pipeline Stages

The orchestrator chains four stages sequentially, aborting on failure:

1. runner — Creates test dirs, installs plugin, runs claude --print with VERCEL_PLUGIN_LOG_LEVEL=trace
2. verify — Detects package manager, launches dev server, polls for 200 with non-empty HTML
3. analyze — Matches JSONL sessions to projects via run-manifest.json, extracts metrics
4. report — Generates report.md and report.json with scorecards and recommendations

Contracts

run-manifest.json

Written by the runner at <base>/results/run-manifest.json. Links all downstream stages to the same run.

interface BenchmarkRunManifest {
  runId: string;           // UUID for this pipeline run
  timestamp: string;       // ISO 8601
  baseDir: string;         // Absolute path to base directory
  projects: Array<{
    slug: string;          // e.g. "01-recipe-platform"
    cwd: string;           // Absolute path to project dir
    promptHash: string;    // SHA hash of the prompt text
    expectedSkills: string[];
  }>;
}

The analyzer and verifier read this manifest to correlate sessions precisely instead of guessing from directory listings.

events.jsonl

The orchestrator writes NDJSON events to <base>/results/events.jsonl tracking pipeline lifecycle:

// Each line is one JSON object:
{ "stage": "pipeline", "event": "start", "timestamp": "...", "data": { "baseDir": "...", "quick": false } }
{ "stage": "runner",   "event": "start", "timestamp": "...", "data": { "script": "...", "args": [...] } }
{ "stage": "runner",   "event": "complete", "timestamp": "...", "data": { "exitCode": 0, "durationMs": 120000 } }
// On failure:
{ "stage": "verify",   "event": "error", "timestamp": "...", "data": { "exitCode": 1, "durationMs": 5000, "slug": "04-conference-tickets" } }
{ "stage": "pipeline", "event": "abort", "timestamp": "...", "data": { "failedStage": "verify", "exitCode": 1, "slug": "04-conference-tickets" } }

report.json

Machine-readable report at <base>/results/report.json for programmatic consumption:

interface ReportJson {
  runId: string | null;
  timestamp: string;
  verdict: "pass" | "partial" | "fail";
  gaps: Array<{
    slug: string;
    expected: string[];
    actual: string[];
    missing: string[];
  }>;
  recommendations: string[];
  suggestedPatterns: Array<{
    skill: string;   // Skill that was expected but not injected
    glob: string;    // Suggested pathPattern glob
    tool: string;    // Tool name that should trigger injection
  }>;
}

Overnight Automation Loop

Run the pipeline repeatedly with a cooldown between iterations:

while true; do
  bun run scripts/benchmark-e2e.ts
  sleep 3600
done

Each run produces timestamped report.json and report.md files. Compare across runs to track improvement.

Self-Improvement Cycle

The pipeline enables a closed feedback loop:

1. Run — bun run scripts/benchmark-e2e.ts exercises the plugin against realistic projects
2. Read gaps — report.json lists which skills were expected but never injected, with exact slugs
3. Apply fixes — Use suggestedPatterns entries (copy-pasteable YAML) to add missing frontmatter patterns; use recommendations to fix hook logic
4. Re-run — Execute the pipeline again to verify the gaps are closed
5. Compare — Diff report.json across runs: verdict should trend from "fail" → "partial" → "pass"

For overnight automation, combine with the loop above. Wake up to reports showing exactly what improved and what still needs work.

Prompt Table

Prompts never name specific technologies — they describe the product and features, letting the plugin infer which skills to inject.

#	Slug	Expected Skills
01	recipe-platform	auth, vercel-storage, nextjs
02	trivia-game	vercel-storage, nextjs
03	code-review-bot	ai-sdk, nextjs
04	conference-tickets	payments, email, auth
05	content-aggregator	cron-jobs, ai-sdk
06	finance-tracker	cron-jobs, email
07	multi-tenant-blog	routing-middleware, cms, auth
08	status-page	cron-jobs, vercel-storage, observability
09	dog-walking-saas	payments, auth, vercel-storage, env-vars

Cleanup

rm -rf ~/dev/vercel-plugin-testing