x-import

name	x-import
description	Import X (Twitter) bookmarks into the Obsidian vault using the bird CLI. Two-phase architecture: Python script handles fetching/dedup/URL resolution/content fetching/file writing, LLM generates English summaries from pre-fetched content. Creates notes in Resources/X-Bookmarks/ with a .base database view. Use this skill when the user wants to sync, import, or fetch their X/Twitter bookmarks, or mentions bird bookmarks.
argument_hint	[--all | --count N]

X Bookmarks Import

Import X (Twitter) bookmarks into the vault as individual structured Obsidian notes, with an Obsidian Bases database view for browsing, filtering, and sorting.

Trigger

/x-import — fetch last 30 bookmarks (daily incremental) /x-import --all — fetch all bookmarks (full initial sync, slow) /x-import --count N — fetch N bookmarks

Storage

• Individual notes: Resources/X-Bookmarks/<YYYY-MM-DD>-<Slug>.md
• Database view: Bases/X-Bookmarks.base
• Script: .agents/skills/x-import/x_bookmarks.py
• LLM manifest: /tmp/x_bookmarks_manifest.json

Architecture

The import is split into two phases to minimize LLM token usage:

1. Python script (mechanical) — fetches bookmarks via bird CLI, deduplicates, groups threads, resolves t.co URLs, fetches linked page content (title + body text), auto-generates tags, writes all files with {{SUMMARY_PLACEHOLDER}}. Outputs a manifest with pre-fetched content for LLM summarization. Also builds a See Also index of all existing files.
2. LLM (intelligence) — reads the manifest (which already contains fetched page content), generates English summaries, writes ## Linked Content sections, scans vault for wikilinks, and fills ## See Also cross-links using the pre-built index.

Workflow

Phase 1 — Run the Python script

# Incremental (default): last 30
python3 .agents/skills/x-import/x_bookmarks.py

# Full sync
python3 .agents/skills/x-import/x_bookmarks.py --all

# Custom count
python3 .agents/skills/x-import/x_bookmarks.py --count 50

The script:

1. Fetches bookmarks via bird CLI with --json --include-parent --thread-meta
2. Deduplicates by tweet_id against existing files in Resources/X-Bookmarks/
3. Groups tweets by conversationId into threads
4. For each new bookmark/thread (resolves URLs in parallel):
   • Resolves t.co shortened URLs to final destinations
   • Fetches linked page HTML, extracts title + body text (truncated to 5000 chars)
   • Skips media domains (x.com, twitter.com, youtube.com, etc.)
   • Auto-generates tags from tweet text + linked content
   • Writes the .md file with {{SUMMARY_PLACEHOLDER}}
5. Builds a See Also index: {filename: {author, tags}} from all existing files
6. Writes manifest to /tmp/x_bookmarks_manifest.json

Report the script's output to the user, then proceed to Phase 2.

If zero new bookmarks, stop here.

Phase 2 — LLM generates English summaries

Read /tmp/x_bookmarks_manifest.json. It contains:

{
  "manifest": [
    {
      "file": "2026-04-03-Slug-Name.md",
      "tweet_id": "123456",
      "author": "@username",
      "author_name": "Display Name",
      "date": "2026-04-03",
      "text": "tweet content (max 3000 chars)",
      "linked_content": [
        {
          "url": "https://example.com/article",
          "title": "Article Title",
          "content": "extracted page text (max 5000 chars)"
        }
      ],
      "tags": ["ai", "tools"],
      "is_thread": false,
      "tweet_count": 1
    }
  ],
  "see_also_index": {
    "2026-03-05-Some-Bookmark": {"author": "@user", "tags": ["ai", "tech"]}
  }
}

For each manifest entry:

2a — Generate English summary

Using the tweet text and the pre-fetched linked_content, generate an English summary (2-5 sentences) covering: what the tweet/thread is about, why it's interesting, and key insights.

Replace {{SUMMARY_PLACEHOLDER}} in the file with the summary.

2b — Add linked content section

If linked_content is non-empty, insert a ## Linked Content section between ## Summary and ## Media/## Engagement:

## Linked Content

### <title from linked_content>
<English summary of the linked article/resource, based on the pre-fetched content field>
Source: <url>

The page content is already in the manifest — summarize it in English (3-5 sentences). No need to fetch URLs again.

2c — Scan vault for wikilinks

Check vault entities for mentions in the tweet text:

• Projects/*.md, Areas/People/*.md, Areas/Companies/*.md

Replace recognized names with [[Entity-Name]] or [[Entity-Name|alias]] wikilinks in the ## Tweet section.

2d — Fill See Also

Using the see_also_index from the manifest, find related bookmarks for each new note:

1. Same author: other files with matching author field
2. Shared tags (≥2 in common): prioritize 3+ shared tags
3. Subject overlap: use your judgment based on the tweet content

Format:

## See Also

- [[2026-03-05-Some-Bookmark]] — same author (@username)
- [[2026-02-03-Other-Bookmark]] — AI tools

Keep 3-8 links max. Edit the ## See Also section in each file.

Parallelization: Split the manifest into batches of ~30 and spawn one Agent per batch (run_in_background=true, mode=bypassPermissions). Each agent reads its batch, generates summaries from the pre-fetched content, and edits the files.

Phase 3 — Report

Display a summary:

X Import complete.
- Bookmarks fetched: Y
- New: X (Z duplicates skipped)
- Links resolved: N
- Notes created: X

List the created files with their titles.

Note Format

Each note in Resources/X-Bookmarks/<YYYY-MM-DD>-<Slug>.md:

---
type: x-bookmark
tweet_id: "<id>"
author: "@username"
author_name: "Display Name"
date: YYYY-MM-DD
tags: [tag1, tag2]
likes: N
retweets: N
replies: N
source: "tweet_url"
status: draft
created: YYYY-MM-DD
---

## Tweet

<Tweet text — preserved as-is, with wikilinks injected>

> — @username, DD/MM/YYYY

## Summary

<English summary — 2-5 sentences>

## Linked Content

### <Title>
<English summary from pre-fetched content>
Source: <url>

## Media

- ![](image_url)

## Engagement

- Likes: N
- Retweets: N
- Replies: N

## See Also

- [[Related-Bookmark]] — reason

---
↑ [[X-Bookmarks]]

Omit empty sections. The ## See Also section and footer ↑ [[X-Bookmarks]] are always present.

Rules

• The bird CLI reads browser cookies — no API keys needed. If auth fails, tell the user to log into x.com.
• Tweet text is preserved verbatim (original language). Only ## Summary and ## Linked Content are in English.
• For --all mode, the script reports progress periodically.
• File naming uses the tweet date, not today's date.
• The script pre-fetches linked content — the LLM should NOT re-fetch URLs. It summarizes the content field from the manifest.
• If a URL could not be fetched, it won't appear in linked_content — no need to note broken links.

Post-Import — Log

After reporting results, append one line to Areas/Codex/operations-log.md:

| YYYY-MM-DD HH:MM | ingest | x-import | X created, Y duplicates | Resources/X-Bookmarks/ |

Use actual counts from the import. Create the file with header if it doesn't exist.

Examples

Input: /x-import → Runs script for 30 latest, then LLM generates summaries from pre-fetched content

Input: /x-import --all → Full sync of all bookmarks

Input: /x-import --count 10 → Fetches and processes 10 latest bookmarks