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Aurora Scharff
393de1ebe0
## Summary - add a Partial Prefetching optimizer Skill for selected source Links and destinations - keep prefetch stages and Link policy in the public [Optimizing prefetching guide](https://nextjs.org/docs/app/guides/optimizing-prefetching), and rely on the same static-shell documentation as the Cache Components optimizer for cache placement, Suspense, loading UI, and authorization - keep the Skill focused on the product decision, production `instant()` loop, differential verification, and mutation freshness when an optimization adds a cache around mutable data - reference the canonical [`unstable_prefetch()`](https://nextjs.org/docs/app/api-reference/functions/prefetch) and [`unstable_navigation()`](https://nextjs.org/docs/app/api-reference/functions/navigation) docs instead of explaining the APIs again in the Skill - link cache-writing optimizations to the canonical [Revalidating](https://nextjs.org/docs/app/getting-started/caching-and-revalidating#revalidating) documentation and verify a populated-cache mutation in the Skill and eval - bundle a self-contained production rig aligned with the related adoption and optimizer Skills, tuned to record each source-link and prefetch-stage contract - add an eval that compares baseline, the bundled guide, and the local Skill against the same prefetched-UI and cache-freshness goal - document the optimizer in the AI agents guide This follows the same guide/Skill split as #97737. Partial Prefetching adoption preserves existing behavior; this optimizer is the later decision about what is worth preparing before a click and what should wait for navigation. Related: #97712, #97737, #97759, #98122, #98175 ## Eval One run per treatment with Claude Opus 4.8 and Claude Sonnet 4.5, using a fixed Claude Haiku 4.5 judge. The bundled-guide and Skill treatments use a local Next.js tarball containing the new guide and API references. | Model | Treatment | Criteria | Score change | Overall | Duration | Tokens processed | Total run cost | Cost change | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | | Opus 4.8 | Baseline | 10/10 (100%) | — | Pass | 18m 42s | 3.39M | $3.46 | — | | Opus 4.8 | Bundled guide | 9/10 (90%) | -10 pp | Fail | 13m 37s | 3.02M | $2.84 | -18.0% | | Opus 4.8 | Local Skill | 10/10 (100%) | +10 pp | Pass | 17m 22s | 2.80M | $2.54 | -10.6% | | Sonnet 4.5 | Baseline | 2/10 (20%) | — | Fail | 15m 55s | 1.97M | $1.06 | — | | Sonnet 4.5 | Bundled guide | 5/10 (50%) | +30 pp | Fail | 15m 49s | 2.17M | $1.11 | +4.5% | | Sonnet 4.5 | Local Skill | 8/10 (80%) | +30 pp | Fail | 15m 31s | 1.41M | $0.77 | -30.4% | Token totals count each unique model message once and include uncached input, cache creation, cache reads, and output. Cache reads account for most of the total in every treatment. Total run cost applies the current AI Gateway base rates to each token category: Opus 4.8 costs $5/M input, $6.25/M cache writes, $0.50/M cache reads, and $25/M output; Sonnet 4.5 costs $3/M input, $3.75/M cache writes, $0.30/M cache reads, and $15/M output. The six agent runs cost $11.78 in total. Compared with baseline, the Opus Skill run maintained a 100% criteria pass rate while reducing cost by 26.7%. The Sonnet Skill run improved the criteria pass rate from 20% to 80%, a 60 percentage-point increase, while reducing cost by 27.3%. The stronger model could solve the fixture without the Skill, although it independently searched the bundled Next.js documentation and took slightly longer than the Skill treatment. The Opus guide-only run implemented the correct behavior but missed the required RED-before-GREEN proof. The Sonnet treatments show a clearer progression. Baseline did not implement the required caching, navigation stage, `instant()` contract, or cache invalidation. The bundled guide added the cached summary and production test, but missed the navigation stage, deferred-state assertions, tag invalidation, and RED-before-GREEN proof. The Skill treatment completed the `instant()` RED-to-GREEN loop and final production behavior, but left `connection()` in the related-sessions read and used path revalidation instead of tagging the cached read. It therefore passed 8 of 10 criteria but failed the overall eval. All six measured runs completed through grading. Chromium and its system dependencies are provisioned before the agent starts, so missing browser libraries did not determine these results. ## Verification - `pnpm --filter=next build` - `pnpm prettier --check` for the changed guide, prompt, Skill, and eval fixture - `pnpm exec tsc -p evals/evals/agent-051-optimize-partial-prefetching/tsconfig.json --noEmit` - `node --check run-evals.js` - `git diff --check` - packed a local Next.js tarball and confirmed it contains the optimizer guide plus the `navigation()` and `prefetch()` references - verified the `navigation()` contract in the minimal reproduction and NextBeats on Next.js 16.4.0-canary.22 - merged the latest `canary`, preserving both generic fixture setup and Playwright provisioning <!-- NEXT_JS_LLM -->
23 lines
585 B
JSON
23 lines
585 B
JSON
{
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"agent-046-adopt-partial-prefetching": {
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"skills": ["next-partial-prefetching-adoption"],
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"timeout": 1800
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},
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"agent-047-adopt-cache-components": {
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"skills": ["next-cache-components-adoption"],
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"timeout": 1800
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},
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"agent-051-optimize-partial-prefetching": {
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"skills": ["next-partial-prefetching-optimizer"],
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"timeout": 1800
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},
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"agent-054-cache-components-empty-static-params": {
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"skills": ["next-cache-components-adoption"],
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"timeout": 1800
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},
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"agent-055-stale-build-error": {
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"skills": ["next-dev-loop"],
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"timeout": 900
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}
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}
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