Skogai Librarian

/librarian

Orchestrate high-level knowledge workflows using Librarian MCP tools.

Prerequisites

Librarian MCP server must be configured and running. Verify by checking that library_stats responds. If it fails, tell the user to run librarian-mcp --setup /path/to/vault and restart Codex.

Commands

/librarian ingest [path]           Ingest a solution doc into the vault
/librarian import <file>           Convert local document (PDF, DOCX, image) to vault markdown
/librarian from gmail <query>      Import Gmail threads matching a search into the vault
/librarian from web <url>          Import any web page into the vault as markdown
/librarian from twitter <url>      Import an X/Twitter thread into the vault
/librarian from calendar [query]   Import calendar events/meeting notes into the vault
/librarian from Codex [session]   Extract learnings from a Codex session into the vault
/librarian search <query>          Deep search vault for prior art
/librarian connect [path]          Find and apply missing wikilinks
/librarian daily [text]            Append a learning to today's daily note
/librarian graph [note]            Explore vault structure or a note's neighborhood
/librarian analyze                 Full vault analysis: god nodes, communities, viz, report
/librarian daydream [focus]        Discover non-obvious connections across vault notes
/librarian status                  Vault health overview

If no command is given, show the usage summary above.

ingest

Purpose: Import a docs/solutions/ file (or any local markdown) into the vault, gaining auto-wikilinks and knowledge graph integration. This is the bridge between per-project documentation (like ce:compound output) and the persistent vault.

Behavior

1. Locate the source file:

   • If [path] is provided, use it directly
   • If no path, scan docs/solutions/ for the most recently modified .md file
   • If docs/solutions/ doesn't exist or is empty, tell the user and stop

2. Read the source file using the Read tool (local filesystem)

3. Determine vault destination:

   • Parse the source file's YAML frontmatter for category (or infer from parent directory name)
   • Map to vault path: Solutions/<category>/<filename>.md
   • Categories map directly: build-errors/ -> Solutions/build-errors/, etc.

4. Write to vault using library_write with the file content

   • library_write auto-links mentions of existing vault notes as [[wikilinks]]
   • Report what links were auto-added

5. Run library_suggest_links on the newly written file

   • Report any additional link suggestions found
   • If suggestions exist, ask user if they want to apply them (re-write with links)

6. Report:

   Ingested: docs/solutions/build-errors/vite-hmr-timeout.md
        -> Solutions/build-errors/vite-hmr-timeout.md
   
   Auto-linked: [[Vite]], [[HMR]], [[SvelteKit]]
   Suggestions: 2 additional links available
   
   The solution is now searchable and connected to your knowledge graph.
   

Batch mode

If the user says /librarian ingest all or /librarian ingest docs/solutions/:

• Find all .md files in docs/solutions/ recursively
• Ingest each one, reporting progress
• Summarize: total ingested, total links added, any failures

import

Purpose: Convert any non-markdown document into markdown and store it in the vault. Uses Microsoft's MarkItDown for conversion, then auto-wikilinks the content.

Supported formats

PDF, DOCX, XLSX, PPTX, images (OCR), audio (transcription), HTML, CSV, JSON, XML, ZIP — anything MarkItDown supports.

Prerequisites

MarkItDown must be installed: pip install markitdown

If the user hasn't installed it, tell them and stop. Don't try to work around it.

Behavior

1. Determine source and destination:

   • <file> is the local filesystem path to the document
   • If the user provides a vault destination path, use it
   • Otherwise, infer from the file: Imports/<filename-stem>.md

2. Call library_import with:

   • source_path: the local file path
   • library_path: the vault destination
   • title: filename stem or user-provided title

3. Report:

   Imported: quarterly-report.pdf
        -> Imports/quarterly-report.md (12,847 bytes, 2,103 words)
   
   Auto-linked: [[Q1 Revenue]], [[Product Roadmap]]
   

Batch mode

If the user provides a directory path:

• List all non-markdown files in the directory
• Confirm with the user before proceeding
• Import each file, reporting progress
• Summarize: total imported, total words, failures

from

Purpose: Import content from external connectors (Gmail, web, Twitter/X, Google Calendar) into the vault. Each connector extracts content, converts it to a structured markdown note with frontmatter, writes it via library_write for auto-wikilinks, and integrates it into the knowledge graph.

All from subcommands follow the same output pattern:

• YAML frontmatter with source, imported, type, and tags
• Structured markdown body
• Key takeaways or summary section
• Written via library_write for auto-wikilinks

from gmail

Purpose: Import Gmail threads into the vault as structured notes. Useful for preserving important email conversations, research threads, or decisions.

Connector: Codex.ai Gmail MCP (search_threads, get_thread)

Behavior:

1. Search for threads:

   • Call search_threads with the user's query (same syntax as Gmail search bar)
   • Examples: "from:editor@publisher.com", "subject:manuscript feedback", "has:attachment newer_than:7d"
   • Display results (subject, from, date, snippet) and let user pick which to import
   • If only one result, proceed directly

2. Extract full thread:

   • Call get_thread with messageFormat: "FULL_CONTENT" for each selected thread
   • Extract: participants, dates, subject, full message bodies

3. Build the vault note:

   ---
   title: "<subject line>"
   participants: ["sender@email.com", "recipient@email.com"]
   source: gmail
   thread_id: "<thread_id>"
   imported: YYYY-MM-DD
   type: email
   tags:
     - source/gmail
     - <topic tags inferred from content>
   ---
   
   # <subject line>
   
   **Participants:** sender, recipient | **Date range:** YYYY-MM-DD to YYYY-MM-DD
   
   ---
   
   ## Message 1 — From: sender@email.com (YYYY-MM-DD HH:MM)
   
   <message body>
   
   ## Message 2 — From: recipient@email.com (YYYY-MM-DD HH:MM)
   
   <message body>
   
   ---
   
   ## Key points
   
   <3-5 bullet point summary of the thread's decisions, action items, or key information>
   

4. Vault path: Emails/<YYYY>/<slugified-subject>.md

5. Write via library_write, report auto-linked notes.

6. Report:

   Imported: "Re: Manuscript feedback round 2" (5 messages)
        -> Emails/2026/manuscript-feedback-round-2.md
   
   Auto-linked: [[Publishing]], [[Draft Review]]
   Key points: 3 action items extracted
   

from web

Purpose: Import any web page into the vault as clean markdown. Uses Exa or Tavily for extraction.

Connectors: Exa (web_fetch_exa) preferred, Tavily (tavily_extract) as fallback.

Behavior:

1. Extract the page:

   • Try web_fetch_exa with the URL first (returns clean markdown)
   • If Exa fails or returns insufficient content, fall back to tavily_extract with extract_depth: "advanced"
   • If both fail, tell the user and stop

2. Build the vault note:

   ---
   title: "<page title>"
   url: "<source URL>"
   author: "<author if detectable>"
   imported: YYYY-MM-DD
   type: article
   tags:
     - source/web
     - <topic tags inferred from content>
   ---
   
   # <page title>
   
   **Source:** [<domain>](url) | **Imported:** YYYY-MM-DD
   
   ---
   
   <cleaned markdown content>
   
   ---
   
   ## Key takeaways
   
   <3-5 bullet point summary>
   

3. Vault path: Web/<YYYY>/<slugified-title>.md

4. Write via library_write, report auto-linked notes.

5. Batch mode: If given multiple URLs, process each sequentially.

from twitter

Purpose: Import an X/Twitter thread into the vault.

Connector: bird CLI via Bash (from agent-reach tooling).

Behavior:

1. Extract the thread:

   • Run: bird thread <url> via Bash
   • If URL is a single tweet (not a thread), run: bird read <url>
   • If bird fails, fall back: try tavily_extract on the URL, or ask user to paste text

2. Parse the output:

   • Extract: author handle, date, individual tweets
   • Identify the thread topic from the first tweet

3. Build the vault note:

   ---
   title: "<thread topic summary>"
   author: "@handle"
   source: "<original URL>"
   imported: YYYY-MM-DD
   type: thread
   tags:
     - source/twitter
     - <topic tags inferred from content>
   ---
   
   # <thread topic summary>
   
   **Author:** [@handle](<profile url>) | **Date:** YYYY-MM-DD | **[Original thread](url)**
   
   ---
   
   <thread content, preserving tweet boundaries with --- separators>
   
   ---
   
   ## Key takeaways
   
   <3-5 bullet point summary of the thread's main points>
   

4. Vault path: Threads/<YYYY>/<slugified-topic>.md

5. Write via library_write, report auto-linked notes.

6. Batch mode: If given multiple URLs, process each sequentially.

from calendar

Purpose: Import calendar events into the vault, useful for meeting notes, event context, or archiving.

Connector: Codex.ai Google Calendar MCP (gcal_list_events, gcal_get_event)

Behavior:

1. Find events:

   • If [query] is provided, use it as a search term via gcal_list_events with q parameter
   • If no query, list today's events via gcal_list_events with timeMin/timeMax set to today
   • Display results and let user pick which to import

2. Get full event details:

   • Call gcal_get_event for each selected event
   • Extract: title, date/time, attendees, description, location, attachments

3. Build the vault note:

   ---
   title: "<event summary>"
   date: YYYY-MM-DD
   attendees: ["person1@email.com", "person2@email.com"]
   source: google-calendar
   event_id: "<event_id>"
   imported: YYYY-MM-DD
   type: meeting
   tags:
     - source/calendar
     - <topic tags inferred from content>
   ---
   
   # <event summary>
   
   **Date:** YYYY-MM-DD HH:MM - HH:MM | **Location:** <location>
   **Attendees:** person1, person2
   
   ---
   
   ## Event description
   
   <description from the calendar event>
   
   ---
   
   ## Notes
   
   <placeholder for user to fill in meeting notes>
   

4. Vault path: Meetings/<YYYY>/<YYYY-MM-DD>-<slugified-title>.md

5. Write via library_write, report auto-linked notes.

6. Batch mode: If importing a date range, ask user: /librarian from calendar --week imports all events from the current week.

from Codex

Purpose: Extract key learnings, decisions, and solutions from a Codex session transcript and archive them in the vault. Closes the knowledge loop — your conversations become searchable vault knowledge.

Data source: Local JSONL files at ~/.Codex/projects/<project>/<session-id>.jsonl

Behavior:

1. Find the session:

   • If [session] is a UUID, use it directly
   • If [session] is a project name or path fragment, find matching project directory under ~/.Codex/projects/
   • If no argument, list the 10 most recent sessions across all projects with date, size, project, and first user message as topic hint. Let the user pick.
   • To list sessions: use Bash to scan ~/.Codex/projects/*/ for .jsonl files, sort by mtime descending

2. Parse the JSONL transcript:

   • Read the file using the Read tool (it's local plaintext)
   • Each line is a JSON object with a type field: user, assistant, system, file-history-snapshot, permission-mode, attachment
   • Focus on user and assistant messages:
     • user messages have message.content (string or array of {type: "text", text: "..."} blocks)
     • assistant messages have message.content as array of blocks: thinking, text, tool_use
     • Extract only text blocks from assistant messages (skip thinking and tool_use)
   • Skip system, file-history-snapshot, permission-mode, and attachment types

3. Synthesize the session into a vault note:

   • Read through the conversation and extract:
     • Topic: What was the session about? (infer from first user message and overall flow)
     • Decisions made: Architecture choices, approach selections, trade-offs resolved
     • Problems solved: Bugs fixed, issues resolved, with root causes
     • Key artifacts: Files created/modified, commands established
     • Open items: Things deferred, noted for future, or left incomplete
   • Be selective — not every message is worth archiving. Focus on knowledge that compounds.

4. Build the vault note:

   ---
   title: "<session topic summary>"
   project: "<project path>"
   session_id: "<uuid>"
   date: YYYY-MM-DD
   source: Codex
   imported: YYYY-MM-DD
   type: session
   tags:
     - source/Codex
     - <project tag>
     - <topic tags inferred from content>
   ---
   
   # <session topic summary>
   
   **Project:** <project path> | **Date:** YYYY-MM-DD | **Messages:** N
   
   ---
   
   ## Decisions
   
   - <decision 1 — what was chosen and why>
   - <decision 2>
   
   ## Solutions
   
   ### <problem 1 title>
   
   **Problem:** <what was wrong>
   **Root cause:** <why>
   **Fix:** <what was done>
   
   ## Artifacts
   
   - Created: `path/to/file.md`
   - Modified: `src/main.rs` — added skill installation to --setup
   
   ## Open items
   
   - <anything deferred or left for future sessions>
   

5. Vault path: Sessions/<YYYY>/<YYYY-MM-DD>-<slugified-topic>.md

6. Write via library_write, report auto-linked notes.

7. Report:

   Imported session: "Librarian skill + MCP connector integration"
        -> Sessions/2026/2026-04-11-librarian-skill-mcp-connectors.md
   
   Auto-linked: [[librarian-mcp]], [[ce-compound]], [[Obsidian]]
   Extracted: 4 decisions, 2 solutions, 1 open item
   

Tips for from Codex

• Current session: You can import the current session — just use the session ID from the JSONL filename. The transcript is written incrementally, so it includes everything up to now.
• Pairs with session-close: Run /session-close to update project memory, then /librarian from Codex to archive the knowledge in the vault.
• Large sessions: For sessions with 100+ messages, focus extraction on the second half where solutions typically land. Skip early exploration/dead-ends unless they contain useful "what didn't work" context.
• Privacy: Session transcripts may contain API keys, passwords, or sensitive data that was read by tools. The synthesis step should never copy raw credentials — only extract the conceptual knowledge.

search

Purpose: Deep search across the vault for prior art on a topic. More than a raw search -- synthesizes results into actionable context.

Behavior

1. Call library_search with the query (limit: 10)

2. For the top 3-5 results, call library_read to get full content

3. Synthesize a brief summary:

   • What the vault knows about this topic
   • Which notes are most relevant (with paths)
   • Key insights or solutions found
   • Related tags discovered

4. Call library_traverse from the most relevant result (depth: 1) to find connected notes the search might have missed

5. Report:

   Vault knowledge on "<query>":
   
   Direct matches (N files):
   - Solutions/build-errors/vite-hmr-timeout.md — HMR fix for Tauri apps
   - Research/Deep Dives/vite-internals.md — Vite architecture notes
   
   Key insights:
   - [synthesized takeaways from the matched content]
   
   Connected notes (via graph):
   - [[SvelteKit]] (1 hop from vite-hmr-timeout)
   
   Tags: #build, #vite, #frontend
   

connect

Purpose: Find unlinked mentions across the vault and optionally apply them. Strengthens the knowledge graph.

Behavior

1. If [path] is provided:

   • Run library_suggest_links on that specific file
   • Show suggestions
   • Ask user to confirm before applying

2. If no path (vault-wide scan):

   • Call library_stats to get orphan notes
   • For each orphan (up to 10), run library_suggest_links
   • Report which orphans could be connected and how
   • Ask user before applying any changes

3. Applying links:

   • Read the file via library_read
   • Write it back via library_write (which auto-links on write)
   • Report the links that were added

daily

Purpose: Append a learning, note, or reflection to today's daily note.

Behavior

1. If [text] is provided:

   • Call library_daily with append: <text>
   • Done

2. If no text:

   • Summarize the current conversation's key outcomes (what was solved, what was learned, what decisions were made)
   • Format as a bullet list under a ## Learnings section
   • Call library_daily with the formatted summary
   • Show what was appended

3. Always report the daily note path (e.g., Journal/2026/2026-04-11.md)

graph

Purpose: Explore vault structure or a specific note's neighborhood in the knowledge graph.

Behavior

1. If [note] is provided:

   • Call library_traverse with start: <note>, depth: 2

   • Call library_links on the note for backlinks/outgoing detail

   • Render a text-based neighborhood map:

     Topic Neighborhood: "Vite"
     
        [[SvelteKit]] ---> [[Vite]] <--- [[HMR]]
                             |
                             v
                        [[Tauri Build]]
     
     Backlinks (3): SvelteKit, HMR, Frontend Tooling
     Outgoing (2): Tauri Build, ESBuild
     2 hops: 8 notes reachable
     

2. If no note (vault-wide):

   • Call library_stats for overview
   • Call library_graph_analysis for structure
   • Report: file count, word count, connected components, hub notes, bridge notes, orphan count
   • Highlight the top 5 hub notes (most connected)
   • Flag orphans that might need linking

analyze

Purpose: Run full vault intelligence analysis — community detection, structural importance ranking (god nodes), cross-community bridge detection, and interactive visualization. Generates two artifacts in the vault root.

Behavior

1. Call library_report (no params, uses default output path)

   • Runs the full pipeline: graph build → community detection → betweenness centrality → PageRank → surprising connections
   • Writes GRAPH_REPORT.md to vault root
   • Report includes: god nodes table, community breakdown, surprising cross-topic connections, suggested questions

2. Call library_visualize (no params, uses default output path)

   • Generates self-contained interactive HTML with force-directed graph layout
   • Nodes colored by community, sized by structural importance
   • Click to inspect, search to filter
   • Writes GRAPH_VIZ.html to vault root

3. Report:

   Vault analysis complete.
   
   Report: GRAPH_REPORT.md
   - 847 notes, 2,341 links, 12 communities, 43 orphans
   - God nodes: [[Codex]], [[Obsidian]], [[Knowledge Management]]
   - 8 surprising cross-community connections found
   
   Visualization: GRAPH_VIZ.html
   - Open in browser to explore the interactive graph
   
   Next steps:
   - /librarian connect — link orphan notes into the graph
   - /librarian from Codex — archive this session's learnings
   

What the report contains

daydream

Purpose: Discover non-obvious connections between vault notes using multi-agent combinatorial exploration. Inspired by Gwern's LLM Daydreaming essay and glebis's Daydream skill — implements the brain's "default mode network" for your vault.

How it works

The command pairs random notes and asks parallel sub-agents to find surprising connections, then filters with critics. Only genuinely novel, coherent, and useful insights survive.

Behavior

1. Sample notes from the vault:

   • Call library_list to get all note paths
   • Call library_read on a random sample of 50 notes
   • Weight toward recent notes (by date frontmatter or filename)
   • If [focus] provided: use library_search to find focus-relevant notes, pair them with random notes from outside that cluster

2. Generate random pairs:

   • Create 50 unique note pairs from the sample
   • Check Daydreams/history.json (via library_read) for already-processed pairs
   • Skip duplicates, proceed with remaining pairs

3. Synthesize connections (parallel sub-agents):

   • Launch 10 parallel sub-agents (Sonnet model), each processing 5 pairs
   • Each agent reads both notes and explores:
     • Abstract analogies between concepts
     • Similar problems or solutions in different domains
     • Potential combinations or hybrid ideas
     • Revealing contradictions or tensions
   • Each agent returns structured results: title, connection description, source notes, reasoning

4. Critique and filter (parallel sub-agents):

   • Launch 10 parallel critic sub-agents (Haiku model)
   • Each critic scores connections on three dimensions (1-10 each):
     • Novelty: Is this surprising and non-obvious?
     • Coherence: Is the reasoning logical and well-grounded?
     • Usefulness: Could this lead to new work, insights, or decisions?
   • Accept only connections with average score >= 7.0

5. Write accepted insights to vault:

   • For each accepted insight, call library_write to create Daydreams/<YYYYMMDD>-<slug>.md
   • Frontmatter includes: title, source_notes (paths), scores (novelty/coherence/usefulness), date, tags (daydream, source topics)
   • Body includes: the connection description, reasoning, and source note excerpts
   • library_write auto-wikilinks the insight into the knowledge graph

6. Update history:

   • Read or create Daydreams/history.json via library_read/library_write
   • Add all processed pairs (accepted or rejected) to prevent re-processing
   • Track: pair hashes, dates, accept/reject status

7. Report:

   Daydream complete.
   
   Processed: 42 pairs (8 skipped as duplicates)
   Accepted: 7 insights (17% acceptance rate)
   
   Insights written:
   - Daydreams/20260411-authentication-as-trust-boundary.md
     "Authentication patterns in the auth module mirror trust boundary
      concepts from the game theory notes — both define threshold functions."
   - Daydreams/20260411-vite-hmr-and-neural-plasticity.md
     ...
   
   Run /librarian analyze to see how these integrate into the knowledge graph.
   

Cost

Approximately $0.40-0.50 per run (50 pairs) using Sonnet for synthesis and Haiku for critique.

Tips

• Run weekly or after adding substantial new content to the vault
• Use [focus] to explore connections around a specific topic: /librarian daydream authentication
• Daydream insights compound — they become seeds for future runs, creating a discovery flywheel
• Run /librarian analyze after daydream to see how insights integrate into the graph

status

Purpose: Quick vault health check.

Behavior

1. Call library_stats

2. Report in compact format:

   Vault: The Labyrinth
   Files: 1,247 | Words: 389,102 | Links: 3,891 | Tags: 156
   Orphans: 23 (run /librarian connect to fix)
   Solutions: 45 ingested
   Last daily: 2026-04-11
   

Tool Reference

This skill orchestrates these Librarian MCP tools:

Codex.ai MCP connectors

Local data sources

External CLI tools

Integration with ce:compound

The primary integration pattern:

/ce:compound              # Documents a solution in docs/solutions/
/librarian ingest         # Imports latest solution into vault (auto-wikilinked)

This bridges project-scoped documentation into the persistent, cross-project knowledge graph. The next time you /librarian search for a similar problem, the solution surfaces with full graph context.