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 yBookoff/thebrain-mcp --skill thebrain-ingestgit clone --depth 1 https://github.com/yBookoff/thebrain-mcpWrote 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/ybookoff/thebrain-mcp/thebrain-ingest)<a href="https://agentmods.dev/skills/ybookoff/thebrain-mcp/thebrain-ingest"><img src="https://agentmods.dev/badge/skills/ybookoff/thebrain-mcp/thebrain-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/ybookoff/thebrain-mcp/thebrain-ingest"><img src="https://agentmods.dev/badge/skills/ybookoff/thebrain-mcp/thebrain-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 | $0.00112 | $0.01581 |
| Opus 5 | $0.00056 | $0.00790 |
| Sonnet 5 | $0.00022 | $0.00316 |
| Haiku 4.5 | $0.00011 | $0.00158 |
Grade A, and why
thebrain-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 10d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Breaking material down into TheBrain
The job is not "store the text" but graft meaning into the existing graph so that six months from now it can be found, and is already connected to whatever arrives later.
Fifteen thoughts dangling under a single article is a bad outcome, even if every one of them is accurate.
Order of work
1. Find out where you are landing
brain_list → which brain, is the index ready
brain_list_types_and_tags → the conventions this brain already uses
If the index is not ready, brain_search will say so. Tell the person — without
an index, finding existing material works worse and the risk of duplicates goes up.
2. Extract the concepts, then search for each one
Read the material and write out the concepts — what it is actually about. Then search for each of them:
brain_search { query: "<concept>", variants: ["synonym", "translation", "broader term"] }
Variants are mandatory. Without an index, search runs on them alone; with an index, they still raise recall.
If something close turns up, do not create a new thought. Extend the existing one and link to it. This is the whole difference between a second brain and a folder of files.
3. Look at the neighbourhood of your attachment points
brain_traverse { thoughtId: "<what you found>", depth: 2 }
This shows which area has already taken shape and what naming conventions hold there. Graft into the existing structure, not alongside it.
4. Assemble a plan and write it in one call
brain_ingest { thoughts: [...], links: [...] }
Do not create thoughts one at a time. Fifteen thoughts means fifteen calls plus links plus notes — minutes of work, and a failure halfway through.
5. Show the result
brain_activate { thoughtId: "<root thought>" }
brain_index { action: "sync" }
The person sees the structure in the app. Syncing the index makes the new material immediately findable.
How finely to split
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.
- 10d ago First seen · 170 lines · 112 tokens per session scan A 407f1acd5dda
thebrain-ingest is a skill published in the GitHub repository yBookoff/thebrain-mcp (6 stars, last pushed 29d ago), licensed MIT. It adds 112 tokens to every session and 1,581 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
refresh-context
A context snapshot updater for Core Context.md, a summary file built from nine main system files and selected essays. It refreshes that summary when the underlying material changes or the snapshot is more than 30 days old.
llm-wiki
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management"…
llm-wiki
Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management"…
wiki-retrieve
Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta…
yopedia
Save research and reflections into (and recall from) your personal knowledge vault (yopedia) — your second brain.
lc-curate-context
Decide which files a task actually needs, record that as a reusable llm-context rule, verify it against the codebase - including the files your selection references but leaves out - and pack it for your own context, a chat, or a sub-agent you dispatch. Load when choosing what code to put in front of a model, packing…