GBrain Infrastructure Layer (orientation pointer)

The shared foundation that all skills, recipes, and integrations build on. This page is a router — the detailed, current-state references live in the docs below (this file once carried its own copies of the pipeline and schema; those rotted, so each concept now has exactly one home).

Where things live

Topic	Home
Ingest pipeline (file resolution → frontmatter parse → content-hash idempotency → chunking → embedding → atomic write)	per-file entries in KEY_FILES.md: src/core/import-file.ts, src/core/sync.ts, src/core/markdown.ts, src/core/embedding.ts, src/core/chunkers/*
Chunking strategies (recursive / semantic / LLM-guided)	src/core/chunkers/{recursive,semantic,llm}.ts entries in KEY_FILES.md
Search pipeline (hybrid RRF, graph, reranker, autocut, dedup, budgets)	RETRIEVAL.md
Search modes + cost knobs	docs/guides/search-modes.md + the CLAUDE.md Search Mode table
Per-file index of src/ (what each file does + its invariants)	KEY_FILES.md
Schema DDL	the MIGRATIONS array in src/core/migrate.ts (source of truth) + src/schema.sql; per-table classification in system-of-record.md
Engines (PGLite vs Postgres, parity rules)	docs/ENGINES.md + the engine entries in KEY_FILES.md
Operations contract (CLI + MCP generated from one source)	src/core/operations.ts (100+ operations; run gbrain --tools-json for the live list)
Brains vs sources (which database vs which repo inside it)	brains-and-sources.md

The Thin Harness Principle

GBrain is the deterministic layer. Skills and recipes are the latent-space layer.

See Thin Harness, Fat Skills for the full architecture philosophy.

• GBrain CLI = thin harness (same input → same output)
• Skills (the bundled set routed by skills/RESOLVER.md) = fat skills
• Recipes (voice-to-brain, email-to-brain) = fat skills that install infrastructure

The agent reads the skill/recipe and uses GBrain's deterministic tools to do the work.