Let the Agent read and write your material
The ceiling of a chat window is that you keep re-feeding it context. With the skill installed, the Agent reads your library directly and writes back to your notes.
Chat has a hidden cost: shuttling. Copy material in, copy conclusions out. Once is fine. Every day is a waste.
The skill wires the Agent to your library
With the LLM-notebook skill installed, the Agent can:
- read: search your material by keyword and get the body text plus its source;
- write: turn the result into a note and file it back into your workspace;
- archive: collect the current session itself into the library.
You stop hand-feeding context, because all your material already lives in one place.
A real sequence
Ask it to draft an industry overview from the three research reports you saved last week:
- Search material → three reports matched.
- Read the bodies → market size, growth rate, competitive landscape located.
- Draft the piece → citation markers kept.
- Write it back → you edit it in the workspace.
You say one sentence, then you review.
Draw the boundaries
Read-and-write access means the permissions have to be obvious:
- It searches what you authorised, not your whole machine.
- Writing back creates a new note; it does not overwrite your work.
- Every citation in the draft can be clicked and checked.
The Agent is not valuable because it thinks for you. It is valuable because it carries and files for you.
Keep reading
Why we did not build a nicer chat box
The chat box is the default shape of every AI product, and also its ceiling: context is used once and thrown away. Notes on the trade-off we made.
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Generating slides is easy. Wanting to use them afterwards is hard. Most of our effort went into the second half: editable, exportable, structurally sound.
Read postConnected in five minutes: extension and skill
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