Research With Marginalia

Research with Marginalia

Marginalia answers questions in the style of a research assistant: it plans, picks tools, reads cited entries, and returns an answer with markdown footnotes pointing back to the source files. This skill covers the full conversation loop including export.

When to use

• The user asks a question that needs synthesis across multiple files ("how do X and Y differ", "what does my corpus say about Z").
• The user wants a citation-grounded answer they can paste elsewhere.
• The user wants to share or archive a finished conversation.

Prerequisites

• Files are already ingested. If /check shows new items, point the user at ingest-vault first.
• An LLM profile is configured (MARGINALIA_LLM_CHAT_*). Without it, the agent can't run.

Workflow

1. Ask the question. Just type it at the prompt — no prefix:

   how does my corpus describe consensus protocols?
   

   The agent streams events: planning, thinking, tool calls (search, open_entry, etc.), and finally the answer. The answer contains [^a]-style markers and a footnote block at the bottom.

2. Follow a citation. The footnote block lists entry_id=.... To inspect any cited entry:

   /info <entry_id_prefix>
   

   8 chars of the id are enough — tab completion (after the first /info in this session) suggests entries the user has already seen.

   /info shows display name, folder path, summary, and a section preview so the user can judge relevance without downloading the bytes.

3. Pull the bytes if needed. When the user wants the actual file:

   /download <entry_id> [<dest>]
   

   Defaults to cwd with the original filename. Folder ids work too (returns a zip of the folder's contents).

4. Continue the conversation. Subsequent questions in the same session reuse the agent state — follow-ups stay coherent. Type /new to start fresh when switching topics.

5. Export the result. Two formats:

   /export                                    # zip, default cwd
   /export <conv_id> notes/raft-vs-paxos.md  # single markdown
   /export <conv_id> archive.zip              # zip with refs
   

   • .md = self-contained markdown. Each footnote is rewritten from entry_id=01abcd... to **display name** — folder/path/ plus the summary. Drop into Obsidian / Notion / any markdown tool.
   • .zip = full archive. report.md (the agent's answer verbatim), manifest.json (citation metadata), and a references/ folder with the cited file bytes + per-entry metadata. Use when archiving or sharing the underlying sources.

   Omit conv_id and /export resolves to the most recent conversation from the local session, falling back to the server's most-recent.

Reading the prompt

The bracketed prompt encodes live state:

• marginalia> — minimal: no backend yet / no cwd state
• marginalia[mirror /research]> — backend + remote cwd
• marginalia[... 12 busy]> — 12 tasks in the queue (ingest, mining, reflection)

When N busy is high, search results may be stale (recently-ingested files might still be extracting). It's fine to wait or proceed; just flag it to the user if their question depends on something just added.

Common pitfalls

• Empty / shallow answer. Often means search didn't surface enough. Try: rephrase the question, or pre-warm with /search <terms> to confirm there's actually content matching the topic.

• Citation marked (reference removed) in the export. The cited entry has been soft-deleted between turn-time and export-time. The conversation still exports; just one footnote degrades gracefully.

• /export with no args fails with "no ended conversation". A conversation only counts as "ended" once the agent has finished a turn. If the user just asked a question and immediately ran /export, wait for the answer to fully stream.

Removing entries

There is no /delete command. This is intentional — AI does not mutate user files directly. To remove an entry:

1. Delete the file from the mirror vault.
2. Run /ingest --all. Marginalia detects the missing file and soft-deletes the entry (sets deleted_at).

The lifecycle field (active / demoted / archived) can demote entries out of default search results without deleting them. Set AUTO_LIFECYCLE_ENABLED=true for automatic lifecycle suggestions.

One-shot commands

All of the above can be driven non-interactively by an external agent:

marginalia ask "compare this Raft paper with my Paxos notes"
marginalia ask "..." --mode quick
marginalia search "consensus protocols" --json
marginalia info <full_entry_id> --json
marginalia discover <full_entry_id> --json
marginalia download <entry_id> [dest]
marginalia export <full_conv_id> [dest.md|dest.zip]
marginalia check --json
marginalia background --json

Add --json for machine-parseable output. Omit it for human-readable text. --server URL (or MARGINALIA_SERVER) connects to a remote backend; otherwise the CLI auto-discovers a running marginalia serve / desktop sidecar, or starts an embedded backend.

One-shot CLI requires full UUIDs (e.g. marginalia info b123a833-...). The 8-char prefix shorthand described in the REPL workflow above does not work outside the REPL.

marginalia export requires a persistent backend (marginalia serve or desktop). Two separate embedded-mode invocations (e.g. marginalia ask then marginalia export) do not share state — the first process exits and its conversations are gone. Export works inside the REPL because the embedded backend stays alive across turns. When passing a conversation id, use the full UUID — prefix shorthand does not work in one-shot mode.