Read the skill files in skills/ before doing brain operations. GBrain ships 50+ skills
(the current list lives in skills/manifest.json) organized by skills/RESOLVER.md
(AGENTS.md is also accepted):
Core 8: ingest (thin router), query, maintain, enrich, briefing, migrate, setup, publish.
Brain skills: signal-detector, brain-ops, idea-ingest, media-ingest, meeting-ingestion, citation-fixer, repo-architecture, skill-creator, daily-task-manager.
Operational + identity: daily-task-prep, cross-modal-review, cron-scheduler, reports,
testing, soul-audit, webhook-transforms, data-research, minion-orchestrator.
minion-orchestrator is the single skill for both lanes of background work (shell jobs
via gbrain jobs submit shell, LLM subagents via gbrain agent run), with shared
Preconditions and trigger routing limited to what the skill covers.
Skillify loop: skillify (the markdown orchestration), skillpack-check (agent-readable health report).
Finding the right skill for a task over MCP: list_skills takes an optional
query (full MCP surface, host catalog) and returns the skills whose name,
triggers or description share words with it, best first, with
match.matched_on saying which of the three matched; skills that share no
words are left out, and limit (default 10) caps the list. It is a word-overlap
ranking, not a model call, so it is free and instant, and the same call
without query returns the whole catalog exactly as before. On a brain that
serves a shared catalog (schema_version: 2, or shared skills activated)
query is refused with invalid_params instead of being ignored; narrow with
section there. Say to your agent: "Which of my brain's skills fit
preparing for this meeting?"
Brain-resident skillpacks + advisor: A brain repo can carry its
own publishable skillpack (brain_resident: true in skillpack.json + schema_pack);
gbrain skillpack init-brain-pack scaffolds one with a 5-section machine-parseable README.
Connecting harnesses discover it on gbrain sources add (Topology A advisory, bounded nag
via nag-state.ts) and over MCP via the source-scoped list_brain_skillpack op +
get_skill --source_id (gated by mcp.publish_skills). The bundled gbrain-advisor skill
gbrain advisorop compute a ranked, read-only list of high-leverage actions from brain state (8 collectors insrc/core/advisor/);--json+exit codes for CI/cron, local-only--apply <id>behind confirm, exposed over MCP behindmcp.publish_advisor(read-only; on by default for the local stdio server, opt-in for remote HTTP; an explicitfalsehides it). Thin clients cannot install a brain pack as a binary.
Routing-table compression: skills/functional-area-resolver/ —
two-layer dispatch pattern for shrinking large AGENTS.md / RESOLVER.md files
(>=12KB) without losing routing accuracy. Replaces one row per skill with one
entry per functional area, where each area declares its sub-skills in a
(dispatcher for: ...) clause. The static-prompt analog of hierarchical agent
routing (AnyTool arXiv:2402.04253, RAG-MCP
arXiv:2505.03275, Anthropic Agent Skills
progressive disclosure). Empirically validated across Opus 4.7 / Sonnet 4.6 /
Haiku 4.5: +13 to +17pp over the verbose baseline at 48% the size (25KB → 13KB
on a real fork). The (dispatcher for: ...) clause is the load-bearing signal
— strip it and lenient accuracy collapses to 41.7% on Sonnet (the
resolver-of-resolvers ablation case). A/B eval surface lives at
evals/functional-area-resolver/ (outside skills/ deliberately so the
skillpack bundler doesn't ship eval infrastructure to downstream installs):
gateway-routed TypeScript harness, 20 training + 5 held-out fixtures, strict +
lenient scoring, three committed cross-model receipts in baseline-runs/.
Receipt header binds (model, prompt_template_hash, fixtures_hash, harness_sha,
ts) so future contributors can verify reproduction. Companion rescore.mjs
re-scores existing JSONL with lenient tolerance for zero API cost. Reproduce
with cd evals/functional-area-resolver && node harness.mjs --model {opus|sonnet|haiku} (~$0.30–1.70 per model). Open follow-ups (held-out corpus
growth, cross-vendor verification, hierarchical area-of-areas, an embedding-based
pre-router, and the run-1 vs run-2 prompt-design ablation methodology) are tracked
in TODOS.md.
Operational health: smoke-test (8 post-restart health checks; bounded
auto-fix for Bun, CLI, and Zod CJS; read-only worker topology via native supervisor
status with duplicate detection; DB, gateway, API key, brain repo; user-extensible
via ~/.gbrain/smoke-tests.d/*.sh).
Conventions: skills/conventions/ has cross-cutting rules (quality, brain-first,
model-routing, test-before-bulk, cross-modal). skills/_brain-filing-rules.md and
skills/_output-rules.md are shared references.