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 ingestgit 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/ingest)<a href="https://agentmods.dev/skills/cynthiajones34/gbrain/ingest"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/ingest/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/ingest"><img src="https://agentmods.dev/badge/skills/cynthiajones34/gbrain/ingest.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 | $0.00017 | $0.02982 |
| Opus 5 | $0.00009 | $0.01491 |
| Sonnet 5 | $0.00003 | $0.00596 |
| Haiku 4.5 | $0.00002 | $0.00298 |
Grade A, and why
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 8d 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 ingest — 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest Skill
Ingest meetings, articles, media, documents, and conversations into the brain.
Filing rule: Read
skills/_brain-filing-rules.mdbefore creating any new page.
Contract
- Every fact written to a brain page carries an inline
[Source: ...]citation with date and provenance. - Every entity mention creates a back-link from the entity's page to the page mentioning them (Iron Law).
- Raw sources are preserved for provenance via
gbrain files upload-rawwith automatic size routing. - State sections are rewritten with current best understanding, never appended to.
- Entity detection fires on every inbound message; notable entities get pages or updates.
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.
Citation Requirements (MANDATORY)
Every fact written to a brain page must carry an inline [Source: ...] citation.
- User's statements:
[Source: User, {context}, YYYY-MM-DD] - Meeting data:
[Source: Meeting "{title}", YYYY-MM-DD] - Email/message:
[Source: email from {name} re: {subject}, YYYY-MM-DD] - Web content:
[Source: {publication}, {URL}, YYYY-MM-DD] - Social media:
[Source: X/@handle, YYYY-MM-DD](URL)(include link) - Synthesis:
[Source: compiled from {sources}]
Phases
Router note: This skill is a router. For specialized ingestion, see: idea-ingest, media-ingest, meeting-ingestion.
- Parse the source. Extract people, companies, dates, and events from the input.
- For each entity mentioned:
- Read the entity's page from gbrain to check if it exists
- If exists: update compiled_truth (rewrite State section with new info, don't append)
- If new: check notability gate, then store the page in gbrain with the appropriate type and slug
- Append to timeline. Add a timeline entry in gbrain for each event, with date, summary, and source citation.
- Create cross-reference links. Link entities in gbrain for every entity pair mentioned together, using the appropriate relationship type.
- Back-link all entities. Update EVERY mentioned entity's page with a back-link to this page (Iron Law).
- Timeline merge. The same event appears on ALL mentioned entities' timelines. If Alice met Bob at Acme Corp, the event goes on Alice's page, Bob's page, and Acme Corp's page.
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.
- 8d ago First seen · 312 lines · 17 tokens per session scan A dc40ecc00728
ingest is a skill published in the GitHub repository cynthiajones34/GBrain (0 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 2,982 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ingest, 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.