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 imphillip/gbrain-openclaw --skill ingestgit clone --depth 1 https://github.com/imphillip/gbrain-openclawWrote 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/imphillip/gbrain-openclaw/ingest)<a href="https://agentmods.dev/skills/imphillip/gbrain-openclaw/ingest"><img src="https://agentmods.dev/badge/skills/imphillip/gbrain-openclaw/ingest.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 | $0.00037 | $0.00766 |
| Opus 5 | $0.00018 | $0.00383 |
| Sonnet 5 | $0.00007 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
gbrain-ingest 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest Skill
Workflow
-
Read the source. Meeting transcript, article, document, conversation log. Identify: participants, companies, topics, decisions, action items.
-
For each entity mentioned:
gbrain search "<name>"— does a page exist?- If yes:
gbrain get <slug>to read current compiled_truth. Rewrite State section with new info. Append to timeline.cat updated.md | gbrain put <slug>with updated content. - If no: Create page using the appropriate template (see Page Templates below).
cat new.md | gbrain put <slug>with new content.
-
Extract and create links.
- For every entity-to-entity reference in the source:
gbrain link <from> <to> --context "the sentence containing the reference" - Links are stored directionally. Create both directions if both pages exist and the relationship is mutual.
- For every entity-to-entity reference in the source:
-
Parse timeline entries.
- For each datable event:
gbrain timeline-add <slug> --date YYYY-MM-DD --summary "one line" --source "meeting/doc-ref"
- For each datable event:
-
Tag appropriately.
gbrain tag <slug> <tag>— use lowercase, hyphenated tags (e.g. yc-alum, investor, active-deal)
-
Verify.
gbrain stats— confirm page counts look right.gbrain get <slug>— spot-check a few pages.
Entry criteria
Not everything gets a page. The bar:
- Anyone you met 1:1 or in a small group: YES
- People discussed in substantive context: YES
- Companies in deal or investment context: YES
- Concepts worth tracking: YES
- Casual passing mentions with no substance: NO
Page templates
Person
---
title: Full Name
type: person
tags: []
linkedin: ""
twitter: ""
---
# Full Name
> One-sentence summary of who this person is and why they matter.
## State
Current role, company, location. What's relevant right now.
## Background
Education, career arc (condensed).
## Open Threads
- [ ] Action items or follow-ups
---
## Timeline
- **YYYY-MM-DD** | source — What happened.
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 · 113 lines · 37 tokens per session scan A efafaab665cd
gbrain-ingest is a skill published in the GitHub repository imphillip/gbrain-openclaw (11 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 766 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
my-wiki
Manage local OKF-compatible Markdown My Wiki vaults with an AI agent. Use for capturing webpages, PDFs, Office documents, notes, images, folders, and ZIP bundles as References; maintaining Reference-to-Concept evidence links; searching or answering from a vault; checking or repairing vault health; switching among…
kb
Set up, evolve, or operate a hraness/kb local-first Markdown knowledge base for coding-agent memory. Use when a user asks to design KB conventions or a recurring KB ritual; search or query a KB or Obsidian vault; load or recover repository context, plans, decisions, concepts, backlinks, semantic search, or Git…
vault-summarize
Use when the user asks to summarize, digest, or get an overview of a folder, project, or set of notes in their markdown vault — runs the vault's batched map-reduce recipe so note bodies stay out of the retained conversation context.
vault-workflow
Use when the user asks you to search, read, or reason about notes in their markdown vault — guides when to use which vault tool and how to chain them for good results.
setup
Set up memex with provisioned Postgres (RDS + pgvector), AGENTS.md injection, first import.
cold-start
Day-one data bootstrapping for a new brain. Sequences the highest-leverage data sources to go from empty brain to useful brain in one session. Uses the agent host's managed connectors for safe credential handling — the agent never holds raw API keys. Covers Gmail import, calendar sync, contacts seeding, X/Twitter…