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 skills add cynthiajones34/GBrain --skill qm-harness-snippetsgit clone --depth 1 https://github.com/cynthiajones34/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/cynthiajones34/gbrain/qm-harness-snippets)<a href="https://agentmods.dev/skills/cynthiajones34/gbrain/qm-harness-snippets"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/qm-harness-snippets/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cynthiajones34/gbrain/qm-harness-snippets"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/qm-harness-snippets.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00039 | $0.00814 |
| Opus 5 | $0.00019 | $0.00407 |
| Sonnet 5 | $0.00008 | $0.00163 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
gbrain 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 11d 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.
This is a copy
100% identical to gbrain — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gbrain — the company brain
This sandbox has the gbrain CLI connected (thin-client) to the org's central
brain. It is the deep, indexed, cross-source memory: org docs, shared channel
knowledge, and every agent's durable notes. Your scope's own notebook stays the
fast per-turn memory; the brain is where knowledge outlives a scope and becomes
searchable by everyone entitled to it.
First-run setup (once per sandbox — skip if gbrain remote doctor passes)
Your scope's brain credentials arrive via the deployment's secret handoff
(keychain entry or one-time secret drop named gbrain). Then:
gbrain init --mcp-only \
--issuer-url "https://brain.<org>.com" \
--mcp-url "https://brain.<org>.com/mcp" \
--oauth-client-id "<client id from the handoff>" \
--oauth-client-secret "<client secret from the handoff>"
gbrain whoami # must succeed before using any other command
Pass the secret with --oauth-client-secret, not via GBRAIN_REMOTE_CLIENT_SECRET:
an env-sourced secret is deliberately NOT written to ~/.gbrain/config.json, so
every later command would fail with "No client_secret available" once the
variable is out of scope. The flag persists it to the config file on this
sandbox's durable disk, which is what the tool's credential capture expects.
Do not run gbrain remote doctor — it needs admin scope, which your client
does not have (by design). gbrain whoami is the read-scope health check.
Reading (do this liberally)
gbrain search "who decided X and why" # hybrid semantic + keyword search
gbrain get <slug> # read one page
gbrain query "question" --json # search tuned for agent consumption
You can read: the shared agent-memory source, org read-only sources (wiki, handbook), and everything under them. Reads are isolation-enforced server-side; you only ever see sources your client is entitled to.
Writing (durable knowledge only, under YOUR prefixes)
Your client is write-fenced to slug prefixes — your own namespace plus the channels you belong to. Writes outside them are rejected server-side.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 80 lines · 39 tokens per session scan A c1f15bf15526
gbrain is a skill published in the GitHub repository cynthiajones34/GBrain (0 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 814 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gbrain, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
memory-proactive
Proactive layered recall and generic domain-aware routing.
memory-archivist
A set of scripts for archiving conversations, syncing them to a knowledge graph, updating summaries, and managing stored memories over time. A knowledge graph is a linked collection of information and relationships.
memory-starter-kit
Historical starter note for the memory sidecar stack.
mind
Local project memory with recall, provenance, policy, and dreams.
personal-knowledge-graph
Use when maintaining a LoomKG/Obsidian knowledge graph.
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.