The GA-readiness audit found the public docs had drifted from the shipped
surface and presented uncited performance numbers as measured fact.
- quick-start: `FindResult`→`Result`, `VerbType.BuiltOn`→`DependsOn` (the
canonical getting-started example now compiles).
- noun-verb-taxonomy: rewrote every sample off removed/fictional APIs
(`augment`/`connectModel`/`getVerbs`/two-arg `add`/`like`/`$gte`) onto the
real single-object `add`/`find`/`relate`/`related`; replaced the stale
31-noun/40-verb catalogs with accurate, complete tables (42 nouns, 127 verbs).
- triple-intelligence: `like:`→`query:`, dollar-operators→bare operators, and
several other fictional keys swept to the real `FindParams`.
- FIND_SYSTEM / PERFORMANCE / index-architecture / BATCHING: replaced
fabricated, mutually-inconsistent latency tables and uncited speedup
multipliers with Big-O characterizations, qualitative mechanism descriptions,
and the one genuinely-measured benchmark (graph O(1) neighbor lookup), per the
evidence-based-claims rule.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Add YAML frontmatter (slug, public, category, template, order) to 8
existing docs and 2 new getting-started guides (installation, quick-start).
Include docs/**/*.md in npm package files so the portal sync-docs script
can read them from node_modules after publish.
Update CLAUDE.md with docs pipeline trigger phrases and release checklist.
The __words__ keyword index stores 50-5000 entries per entity (one per
word), which inflated avg entries/entity well above the corruption
threshold of 100. This caused:
1. validateConsistency() to falsely detect corruption on every startup,
triggering unnecessary clearAllIndexData() + rebuild() cycles
2. getStats() to log false "Metadata index may be corrupted" warnings
and report inflated totalEntries/totalIds stats
Both methods now skip __words__ when counting, so stats and health
checks reflect metadata fields only (noun, type, createdAt, etc.).
Keyword search is unaffected since the __words__ field index itself
is not modified.
Implements comprehensive batching infrastructure (brain.batchGet, storage.getNounMetadataBatch, storage.getVerbsBySourceBatch) with native cloud adapter APIs for GCS, S3, R2, and Azure. VFS operations now use parallel breadth-first traversal with batching, reducing directory reads from 22 sequential calls to 2-3 batched calls. Improves cloud storage performance by 90%+ (12.7s → <1s for 12 files). Fully compatible with type-aware storage, sharding, COW, fork(), and all indexes.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
