GBrain is a memory and retrieval layer for AI agents that searches, connects, and synthesizes information from stored sources. It is used to give coding agents and autonomous agents access to knowledge beyond their current code, including shared company information with access controls. The catalogue add-ons help agents operate GBrain and connect it to agent workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/garrytan/gbrain/enrichnpx skills add garrytan/gbrain --skill enrichgit clone --depth 1 https://github.com/garrytan/gbrainWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/garrytan/gbrain/enrich)<a href="https://agentmods.dev/skills/garrytan/gbrain/enrich"><img src="https://agentmods.dev/badge/skills/garrytan/gbrain/enrich.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.02637 |
| Opus 5 | $0.00022 | $0.01319 |
| Sonnet 5 | $0.00009 | $0.00527 |
| Haiku 4.5 | $0.00004 | $0.00264 |
Grade A, and why
enrich scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enrich Skill
Enrich person and company pages from external sources. Scale effort to importance.
Contract
This skill guarantees:
- Every enriched page has compiled truth (State section) with inline citations
- Every enriched page has a timeline with dated entries
- Back-links are created bidirectionally
- Tiered enrichment: Tier 1 (full), Tier 2 (medium), Tier 3 (minimal) based on notability
- No stubs: every new page has meaningful content from web search or existing brain context
Filing rule: Read
skills/_brain-filing-rules.mdbefore creating any new page.
Convention: See
skills/conventions/quality.mdfor Iron Law back-linking.
Every mention of a person or company with a brain page MUST create a back-link
FROM that entity's page TO the page mentioning them. An unlinked mention is a
broken brain. See skills/_brain-filing-rules.md for format.
Philosophy
A brain page should read like an intelligence dossier, not a LinkedIn scrape. Facts are table stakes. Texture is the value -- what do they believe, what are they building, what makes them tick, where are they headed.
Citation Requirements (MANDATORY)
Convention: see
skills/conventions/quality.mdfor citation formats and source precedence.
When sources conflict, note the contradiction with both citations.
When To Enrich
Primary triggers
- User mentions an entity in conversation
- Entity appears in a meeting transcript or email
- New contact appears with significant context
- Entity makes news or has a major event
- Any ingest pipeline encounters a notable entity
Do NOT enrich
- Random mentions with no relationship signal
- Bot/spam accounts
- Entities with no substantive connection to the user's work
- Same page enriched within the past week (unless new signal warrants it)
Enrichment Tiers
Scale enrichment to importance. Don't waste API calls on low-value entities.
| Tier | Who | Effort | Sources |
|---|---|---|---|
| 1 (key) | Inner circle, close collaborators, key contacts | Full pipeline | All available APIs + deep web research |
| 2 (notable) | Occasional interactions, industry figures | Moderate | Web research + social + brain cross-ref |
| 3 (minor) | Worth tracking, not critical | Light | Brain cross-ref + social lookup if handle known |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 350 lines · 44 tokens per session scan A 9988168348f6
enrich is a skill published in the GitHub repository garrytan/gbrain (29,629 stars, last pushed 3d ago), licensed MIT. It adds 44 tokens to every session and 2,637 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.
shellm
Reference for the shellm system — recursive LLM shell, identity management, memory, skills, trajectory, and all CLI tools. Use when working on shellm itself, debugging agent behavior, or understanding how the pieces fit together.