Why Obsidian needs preparation before Gemini
An Obsidian vault can be a deep knowledge base, but pasting one note at a time loses links, folder context, and the supporting notes around an idea.
Gemini is useful when you want a large working context and need to combine notes, docs, and technical material in one pass. The result depends heavily on how cleanly your source material is grouped, labeled, and filtered before it reaches the model.
Step-by-step workflow
Point Riflet at your vault folder and select the notes, MOCs, and project folders that should shape the answer.
- Add Obsidian vaults as a Riflet source.
- Select MOCs, project notes, evergreen notes, research summaries, source notes, and short instruction files that explain naming conventions.
- Use filters to remove stale exports, generated files, and unrelated background material.
- Export one Markdown or text bundle and use it in Gemini Gems.
What to include
For this workflow, prioritize MOCs, project notes, evergreen notes, research summaries, source notes, and short instruction files that explain naming conventions. Add a short instruction file at the top if the AI needs naming conventions, project goals, or constraints.
A full-vault dump is rarely the best context. Curate topical folders, current notes, and project maps so the model does not spend tokens on unrelated daily notes.
Getting the bundle into Gemini Gems
Gemini Gems keeps uploaded material in Gem knowledge. Create or edit a Gem and attach the bundle as knowledge, sitting alongside the instructions that define how the Gem should behave. It accepts text, Markdown, PDF, and Google Docs, so a Markdown or plain-text export from Riflet needs no further conversion.
A large context window tempts you to attach everything. The binding constraint is usually attention, not capacity: a focused bundle beats a full dump even when both comfortably fit, because every irrelevant page competes with the relevant ones.
A Gem's knowledge is edited in the Gem itself, so the update is a deliberate step. That is an advantage when you want the Gem's answers to stay pinned to a known version of the source material.
Token budget advice
Riflet lets you check the bundle against Gemini 3.7 Flash's 1,048,576-token context window before export. Keep room for your actual question and for the model's answer.
If the bundle is too large, remove files that merely repeat what another file already explains. Context quality usually beats context volume.
Honest limitation
Riflet does not push live updates into Gemini. It creates the context file. When the source changes, re-export and upload the new bundle yourself.
Build your AI context file with Riflet
Select the sources that matter, check the token budget, and export one clean file for the AI you already use.
Download Riflet freeFrequently asked questions
Can I use this Obsidian to Gemini bundle in more than one AI tool?
Yes. Riflet exports plain text or Markdown, so the same context file can be used in Claude, ChatGPT, Gemini, Perplexity, or a local model if that tool accepts pasted text or file uploads.
Does Riflet automatically update the AI after my files change?
No. Riflet is deliberately export-based. When your files change, reopen the workspace, let Riflet reapply the saved sources and filters, export a fresh bundle, and upload it where you want to use it.
How do I avoid giving Gemini too much context?
Start with the decision or task, then include only the files that would help a smart human answer it. Use Riflet's token count to remove stale, duplicated, generated, or background-only files before exporting.
Do my files leave my computer when Riflet builds the bundle?
Riflet processes selected files locally. Your content leaves your machine only if you choose to paste or upload the exported bundle into an AI product.