xint — X Intelligence CLI

General-purpose agentic research over X/Twitter. Decompose any research question into targeted searches, iteratively refine, follow threads, deep-dive linked content, and synthesize into a sourced briefing.

For X API details (endpoints, operators, response format): read references/x-api.md.

Security Considerations

This skill requires sensitive credentials. Follow these guidelines:

Credentials

• X_BEARER_TOKEN: Required for X API. Treat as a secret - prefer exported environment variables (optional project-local .env)
• XAI_API_KEY: Optional, needed for AI analysis. Also a secret
• X_CLIENT_ID: Optional, needed for OAuth. Less sensitive but don't expose publicly
• XAI_MANAGEMENT_API_KEY: Optional, for collections management

File Writes

• This skill writes to its own data/ directory: cache, exports, snapshots, OAuth tokens
• OAuth tokens stored with restrictive permissions (chmod 600)
• Review exported data before sharing - may contain sensitive search queries

Webhooks

• watch and stream can send data to webhook endpoints
• Remote endpoints must use https:// (http:// is accepted only for localhost/loopback)
• Optional host allowlist: XINT_WEBHOOK_ALLOWED_HOSTS=hooks.example.com,*.internal.example
• Avoid sending sensitive search queries or token-bearing URLs to third-party destinations

Runtime Notes

• This file documents usage and safety controls for the CLI only.
• Network listeners are opt-in (mcp --sse) and disabled by default
• Webhook delivery is opt-in (--webhook) and disabled by default

Installation

• For Bun: prefer OS package managers over curl | bash when possible
• Verify any installer scripts before running

MCP Server (Optional)

• bun run xint.ts mcp starts a local MCP server exposing xint commands as tools
• Default mode is stdio/local integration; no inbound web server unless --sse is explicitly enabled
• Respect --policy read_only|engagement|moderation and budget guardrails

CLI Tool

All commands run from the project directory:

# Set your environment variables
export X_BEARER_TOKEN="your-token"

Search

bun run xint.ts search "<query>" [options]

Options:

• --sort likes|impressions|retweets|recent — sort order (default: likes)
• --since 1h|3h|12h|1d|7d — time filter (default: last 7 days). Also accepts minutes (30m) or ISO timestamps.
• --min-likes N — filter by minimum likes
• --min-impressions N — filter by minimum impressions
• --pages N — pages to fetch, 1-5 (default: 1, 100 tweets/page)
• --limit N — max results to display (default: 15)
• --quick — quick mode: 1 page, max 10 results, auto noise filter, 1hr cache, cost summary
• --from <username> — shorthand for from:username in query
• --quality — filter low-engagement tweets (>=10 likes, post-hoc)
• --no-replies — exclude replies
• --sentiment — AI-powered per-tweet sentiment analysis (via Grok). Shows positive/negative/neutral/mixed with scores.
• --save — save results to data/exports/
• --json — raw JSON output
• --jsonl — one JSON object per line (optimized for Unix pipes: | jq, | tee)
• --csv — CSV output for spreadsheet analysis
• --markdown — markdown output for research docs

Auto-adds -is:retweet unless query already includes it. All searches display estimated API cost.

Examples:

bun run xint.ts search "AI agents" --sort likes --limit 10
bun run xint.ts search "from:elonmusk" --sort recent
bun run xint.ts search "(opus 4.6 OR claude) trading" --pages 2 --save
bun run xint.ts search "$BTC (revenue OR fees)" --min-likes 5
bun run xint.ts search "AI agents" --quick
bun run xint.ts search "AI agents" --quality --quick
bun run xint.ts search "solana memecoins" --sentiment --limit 20
bun run xint.ts search "startup funding" --csv > funding.csv
bun run xint.ts search "AI" --jsonl | jq 'select(.metrics.likes > 100)'

Profile

bun run xint.ts profile <username> [--count N] [--replies] [--json]

Fetches recent tweets from a specific user (excludes replies by default).

Thread

bun run xint.ts thread <tweet_id> [--pages N]

Fetches full conversation thread by root tweet ID.

Single Tweet

bun run xint.ts tweet <tweet_id> [--json]

Article (Full Content Fetcher)

bun run xint.ts article <url> [--json] [--full] [--ai <text>]

Fetches and extracts full article content from any URL using xAI's web_search tool (Grok reads the page). Returns clean text with title, author, date, and word count. Requires XAI_API_KEY.

Also supports X tweet URLs — automatically extracts the linked article from the tweet and fetches it.

Options:

• --json — structured JSON output (title, content, author, published, wordCount, ttr)
• --full — return full article text without truncation (default truncates to ~5000 chars)
• --model <name> — Grok model (default: grok-4)
• --ai <text> — analyze article with Grok AI (passes content to analyze command)

Examples:

# Fetch article from URL
bun run xint.ts article https://example.com/blog/post

# Auto-extract article from X tweet URL and analyze
bun run xint.ts article "https://x.com/user/status/123456789" --ai "Summarize key takeaways"

# Fetch + analyze with AI
bun run xint.ts article https://techcrunch.com/article --ai "What are the main points?"

# Full content without truncation
bun run xint.ts article https://blog.example.com/deep-dive --full

Agent usage: When search results include tweets with article links, use article to read the full content. Search results now include article titles and descriptions from the X API (shown as 📰 lines), so you can decide which articles are worth a full read. Prioritize articles that:

• Multiple tweets reference
• Come from high-engagement tweets
• Have relevant titles/descriptions from the API metadata

Bookmarks

bun run xint.ts bookmarks [options]       # List bookmarked tweets
bun run xint.ts bookmark <tweet_id>       # Bookmark a tweet
bun run xint.ts unbookmark <tweet_id>     # Remove a bookmark

Bookmark list options:

• --limit N — max bookmarks to display (default: 20)
• --since <dur> — filter by recency (1h, 1d, 7d, etc.)
• --query <text> — client-side text filter
• --json — raw JSON output
• --markdown — markdown output
• --save — save to data/exports/
• --no-cache — skip cache

Requires OAuth. Run auth setup first.

Likes

bun run xint.ts likes [options]           # List your liked tweets
bun run xint.ts like <tweet_id>           # Like a tweet
bun run xint.ts unlike <tweet_id>         # Unlike a tweet

Likes list options: Same as bookmarks (--limit, --since, --query, --json, --no-cache).

Requires OAuth with like.read and like.write scopes.

Following

bun run xint.ts following [username] [--limit N] [--json]

Lists accounts you (or another user) follow. Defaults to the authenticated user.

Requires OAuth with follows.read scope.

Trends

bun run xint.ts trends [location] [options]

Fetches trending topics. Tries the official X API trends endpoint first; falls back to search-based hashtag frequency estimation if unavailable.

Options:

• [location] — location name or WOEID number (default: worldwide)
• --limit N — number of trends to display (default: 20)
• --json — raw JSON output
• --no-cache — bypass the 15-minute cache
• --locations — list all known location names

Examples:

bun run xint.ts trends                    # Worldwide
bun run xint.ts trends us --limit 10      # US top 10
bun run xint.ts trends japan --json       # Japan, JSON output
bun run xint.ts trends --locations        # List all locations

Analyze (Grok AI)

bun run xint.ts analyze "<query>"                              # Ask Grok a question
bun run xint.ts analyze --tweets <file>                        # Analyze tweets from JSON file
bun run xint.ts search "topic" --json | bun run xint.ts analyze --pipe  # Pipe search results

Uses xAI's Grok API (OpenAI-compatible). Requires XAI_API_KEY in env or .env.

Options:

• --model <name> — grok-4, grok-4-1-fast (default), grok-3, grok-3-mini, grok-2
• --tweets <file> — path to JSON file containing tweets
• --pipe — read tweet JSON from stdin

Examples:

bun run xint.ts analyze "What are the top AI agent frameworks right now?"
bun run xint.ts search "AI agents" --json | bun run xint.ts analyze --pipe "Which show product launches?"
bun run xint.ts analyze --model grok-3 "Deep analysis of crypto market sentiment"

xAI X Search (No Cookies/GraphQL)

For “recent sentiment / what X is saying” without using cookies/GraphQL, use xAI’s hosted x_search tool.

Script:

python3 scripts/xai_x_search_scan.py --help

xAI Collections Knowledge Base (Files + Collections)

Store first-party artifacts (reports, logs) in xAI Collections and semantic-search them later.

Script:

python3 scripts/xai_collections.py --help

Env:

• XAI_API_KEY (api.x.ai): file upload + search
• XAI_MANAGEMENT_API_KEY (management-api.x.ai): collections management + attaching documents

Notes:

• Never print keys.
• Prefer --dry-run when wiring new cron jobs.

Reposts

bun run xint.ts reposts <tweet_id> [--limit N] [--json]

Look up users who reposted a specific tweet. Useful for engagement analysis and OSINT.

Examples:

bun run xint.ts reposts 1234567890
bun run xint.ts reposts 1234567890 --limit 50 --json

User Search

bun run xint.ts users "<query>" [--limit N] [--json]

Search for X users by keyword. Uses the /2/users/search endpoint.

Examples:

bun run xint.ts users "AI researcher"
bun run xint.ts users "solana developer" --limit 10 --json

Watch (Real-Time Monitoring)

bun run xint.ts watch "<query>" [options]

Polls a search query on an interval, shows only new tweets. Great for monitoring topics during catalysts, tracking mentions, or feeding live data into downstream tools.

Options:

• --interval <dur> / -i — poll interval: 30s, 1m, 5m, 15m (default: 5m)
• --webhook <url> — POST new tweets as JSON to this URL (https:// required for remote hosts)
• --jsonl — output as JSONL instead of formatted text (for piping to tee, jq, etc.)
• --quiet — suppress per-poll headers (just show tweets)
• --limit N — max tweets to show per poll
• --sort likes|impressions|retweets|recent — sort order

Press Ctrl+C to stop — prints session stats (duration, total polls, new tweets found, total cost).

Examples:

bun run xint.ts watch "solana memecoins" --interval 5m
bun run xint.ts watch "@vitalikbuterin" --interval 1m
bun run xint.ts watch "AI agents" -i 30s --webhook https://hooks.example.com/ingest
bun run xint.ts watch "breaking news" --jsonl | tee -a feed.jsonl

Agent usage: Use watch when you need continuous monitoring of a topic. For one-off checks, use search instead. The watch command auto-stops if the daily budget is exceeded.

Diff (Follower Tracking)

bun run xint.ts diff <@username> [options]

Tracks follower/following changes over time using local snapshots. First run creates a baseline; subsequent runs show who followed/unfollowed since last check.

Options:

• --following — track who the user follows (instead of their followers)
• --history — view all saved snapshots for this user
• --json — structured JSON output
• --pages N — pages of followers to fetch (default: 5, 1000 per page)

Requires OAuth (auth setup first). Snapshots stored in data/snapshots/.

Examples:

bun run xint.ts diff @vitalikbuterin          # First run: create snapshot
bun run xint.ts diff @vitalikbuterin          # Later: show changes
bun run xint.ts diff @0xNyk --following       # Track who you follow
bun run xint.ts diff @solana --history        # View snapshot history

Agent usage: Use diff to detect notable follower changes for monitored accounts. Combine with watch for comprehensive account monitoring. Run periodically (e.g., daily) to build a history of follower changes.

Report (Intelligence Reports)

bun run xint.ts report "<topic>" [options]

Generates comprehensive markdown intelligence reports combining search results, optional sentiment analysis, and AI-powered summary via Grok.

Options:

• --sentiment — include per-tweet sentiment analysis
• --accounts @user1,@user2 — include per-account activity sections
• --model <name> — Grok model for AI summary (default: grok-4-1-fast)
• --pages N — search pages to fetch (default: 2)
• --save — save report to data/exports/

Examples:

bun run xint.ts report "AI agents"
bun run xint.ts report "solana" --sentiment --accounts @aaboronkov,@rajgokal --save
bun run xint.ts report "crypto market" --model grok-3 --sentiment --save

Agent usage: Use report when the user wants a comprehensive briefing on a topic. This is the highest-level command — it runs search, sentiment, and analysis in one pass and produces a structured markdown report. For quick pulse checks, use search --quick instead.

Costs

bun run xint.ts costs                     # Today's costs
bun run xint.ts costs week                # Last 7 days
bun run xint.ts costs month               # Last 30 days
bun run xint.ts costs all                 # All time
bun run xint.ts costs budget              # Show budget info
bun run xint.ts costs budget set 2.00     # Set daily limit to $2
bun run xint.ts costs reset               # Reset today's data

Tracks per-call API costs with daily aggregates and configurable budget limits.

Watchlist

bun run xint.ts watchlist                       # Show all
bun run xint.ts watchlist add <user> [note]     # Add account
bun run xint.ts watchlist remove <user>         # Remove account
bun run xint.ts watchlist check                 # Check recent from all

Auth

bun run xint.ts auth setup [--manual]    # Set up OAuth 2.0 (PKCE)
bun run xint.ts auth status              # Check token status
bun run xint.ts auth refresh             # Manually refresh tokens

Required scopes: bookmark.read bookmark.write tweet.read users.read like.read like.write follows.read offline.access

Cache

bun run xint.ts cache clear    # Clear all cached results

15-minute TTL. Avoids re-fetching identical queries.

Research Loop (Agentic)

When doing deep research (not just a quick search), follow this loop:

1. Decompose the Question into Queries

Turn the research question into 3-5 keyword queries using X search operators:

• Core query: Direct keywords for the topic
• Expert voices: from: specific known experts
• Pain points: Keywords like (broken OR bug OR issue OR migration)
• Positive signal: Keywords like (shipped OR love OR fast OR benchmark)
• Links: url:github.com or url: specific domains
• Noise reduction: -is:retweet (auto-added), add -is:reply if needed

2. Search and Extract

Run each query via CLI. After each, assess:

• Signal or noise? Adjust operators.
• Key voices worth searching from: specifically?
• Threads worth following via thread command?
• Linked resources worth deep-diving?

3. Follow Threads

When a tweet has high engagement or is a thread starter:

bun run xint.ts thread <tweet_id>

4. Deep-Dive Linked Content

Search results now include article titles and descriptions from the X API (shown as 📰 in output). Use these to decide which links are worth a full read, then fetch with xint article:

bun run xint.ts article <url>               # terminal display
bun run xint.ts article <url> --json         # structured output
bun run xint.ts article <url> --full         # no truncation

Prioritize links that:

• Multiple tweets reference
• Come from high-engagement tweets
• Have titles/descriptions suggesting depth (not just link aggregators)
• Point to technical resources directly relevant to the question

5. Analyze with Grok

For complex research, pipe search results into Grok for synthesis:

bun run xint.ts search "topic" --json | bun run xint.ts analyze --pipe "Summarize themes and sentiment"

6. Synthesize

Group findings by theme, not by query:

### [Theme/Finding Title]

[1-2 sentence summary]

- @username: "[key quote]" (NL, NI) [Tweet](url)
- @username2: "[another perspective]" (NL, NI) [Tweet](url)

Resources shared:
- [Resource title](url) — [what it is]

7. Save

Use --save flag to save to data/exports/.

Obsidian Bookmark Sync (Optional)

Only activate when user explicitly asks to sync bookmarks to Obsidian (e.g., "sync bookmarks", "capture bookmarks", "bookmark research", "save my bookmarks to obsidian").

Fetches recent X bookmarks, analyzes article content, and saves as structured research notes in the Obsidian inbox. Requires OAuth + Obsidian vault path (~/obsidian/nyk/inbox/).

Pipeline

Step 1 — Fetch bookmarks:

xint bookmarks --limit {count} --json --policy engagement {--since flag if provided} {--query flag if provided}

Parse JSON output. Each bookmark has: id, text, username, name, created_at, metrics, urls, tweet_url.

Step 2 — Classify: For each bookmark, determine type:

• article: Contains X article URL (x.com/i/article/...) or thread with 3+ linked tweets
• thread: Multi-tweet thread (conversation_id, reply chains)
• standalone: Single tweet with insight/opinion/announcement
• link: Tweet primarily sharing an external URL

Step 3 — Analyze content:

• For article/thread: Use Agent tool (subagent_type: "general-purpose") to fetch + analyze full content — run analyses in parallel (one agent per article)
• For standalone/link: Analyze directly from tweet text + WebFetch for external links

Step 4 — Deduplicate: Before creating files, check for existing notes:

grep -rl "{tweet_id}" ~/obsidian/nyk/inbox/ 2>/dev/null

Skip bookmarks that already have notes.

Step 5 — Generate research notes at ~/obsidian/nyk/inbox/research-{slug}.md:

---
id: research-{slug}
created: {today's date}
type: research
status: inbox
tags: [{auto-detected tags}]
source: x-bookmarks
tweet_id: "{tweet_id}"
description: {one-line summary}
---

Content sections: Signal (author, engagement, tweet URL) → Core Thesis → Key Findings (bullets) → Why It Resonated (engagement analysis) → Actionable Takeaways (checklist) → Related (wikilinks). Apply 2-4 tags per note.

Step 6 — Summary report: Output a table of processed bookmarks (author, topic, engagement, file), counts of new/skipped/total.

Tag Detection Rules

Sync Heuristics

• Bookmark-to-like ratio >2:1 = reference material, >3:1 = textbook-grade
• Articles with >1K bookmarks are almost always worth full analysis
• Standalone tweets with <100 likes can still be high-signal if from domain experts
• All notes go to inbox/ — promotion to knowledge/graph/ happens via knowledge-doctor pipeline
• Use [[wikilinks]] for internal cross-references (never standard markdown links)

Cost Management

All API calls are tracked in data/api-costs.json. The budget system warns when approaching limits but does not block calls (passive).

X API v2 pay-per-use rates:

• Tweet reads (search, bookmarks, likes, profile): ~$0.005/tweet
• Full-archive search: ~$0.01/tweet
• Write operations (like, unlike, bookmark, unbookmark): ~$0.01/action
• Profile lookups: ~$0.005/lookup
• Follower/following lookups: ~$0.01/page
• Trends: ~$0.10/request
• User search: ~$0.01/page
• Reposts lookup: ~$0.01/page
• Grok AI (sentiment/analyze/report): billed by xAI separately (not X API)
  • grok-4-1-fast: $0.20/$0.50 per 1M tokens (default for analysis)
  • grok-4: $3.00/$15.00 per 1M tokens (used for article/x-search)
  • xAI tool invocations: max $5/1K calls (50% cheaper than 2025 rates)

Default daily budget: $1.00 (adjustable via costs budget set <N>).

Refinement Heuristics

• Too much noise? Add -is:reply, use --sort likes, narrow keywords
• Too few results? Broaden with OR, remove restrictive operators
• Crypto spam? Add -$ -airdrop -giveaway -whitelist
• Expert takes only? Use from: or --min-likes 50
• Substance over hot takes? Search with has:links

File Structure

xint/
├── SKILL.md           (this file — agent instructions)
├── xint.ts            (CLI entry point)
├── lib/
│   ├── api.ts         (X API wrapper: search, thread, profile, tweet)
│   ├── article.ts     (full article content fetcher via xAI web_search)
│   ├── bookmarks.ts   (bookmark read — OAuth)
│   ├── cache.ts       (file-based cache, 15min TTL)
│   ├── costs.ts       (API cost tracking & budget)
│   ├── engagement.ts  (likes, like/unlike, following, bookmark write — OAuth)
│   ├── followers.ts   (follower/following tracking + snapshot diffs)
│   ├── format.ts      (terminal, markdown, CSV, JSONL formatters)
│   ├── grok.ts        (xAI Grok analysis integration)
│   ├── oauth.ts       (OAuth 2.0 PKCE auth + token refresh)
│   ├── reposts.ts     (repost/retweet lookup)
│   ├── report.ts      (intelligence report generation)
│   ├── sentiment.ts   (AI-powered sentiment analysis via Grok)
│   ├── trends.ts      (trending topics — API + search fallback)
│   ├── users.ts       (user search by keyword)
│   └── watch.ts       (real-time monitoring with polling)
├── data/
│   ├── api-costs.json  (cost tracking data)
│   ├── oauth-tokens.json (OAuth tokens — chmod 600)
│   ├── watchlist.json  (accounts to monitor)
│   ├── exports/        (saved research)
│   ├── snapshots/      (follower/following snapshots for diff)
│   └── cache/          (auto-managed)
└── references/
    └── x-api.md        (X API endpoint reference)

Package API Tools

The Package API provides agent memory package management:

Workflow:

1. xint_package_create -> creates package with topic query + sources
2. xint_package_status -> poll until status is "ready"
3. xint_package_query -> retrieve claims with citations
4. xint_package_refresh -> trigger re-ingest when data is stale
5. xint_package_publish -> share to catalog when quality is confirmed

Agent Patterns

Token Budget Awareness

• Use --quick flag for initial discovery (1 page, 1hr cache, noise filter)
• Use --fields id,text,metrics.likes to reduce response size
• Prefer xint_search with limit: 5 for quick checks
• Use xint_costs to check budget before expensive operations

Batch Operations

• Search + profile in sequence, not parallel (rate limit: 350ms between requests)
• Use xint_watch for polling instead of repeated searches
• Combine xint_report for topic intelligence instead of multiple searches

Context Window Management

• xint_search with limit=15: ~3KB response
• xint_profile with count=20: ~4KB response
• xint_article: 1-10KB depending on article length
• Bookmark sync pipeline: ~2-8KB per bookmark (depends on article analysis)
• xint_trends: ~2KB response
• Use --fields flag to reduce output to only needed fields

Error Recovery Matrix

Fallback Chain

When a tool fails, try the next option:

1. xint_search (X API v2, fast, real-time)
2. xint_xsearch (xAI Grok search via grok-4-1-fast, AI-enhanced, requires XAI_API_KEY)
3. Cached results from previous searches (15min TTL)

For article fetching:

1. xint_article with tweet URL (extracts inline X Article)
2. xint_article with article URL (web fetch via grok-4)
3. xint_search for tweets about the topic

For user discovery:

1. xint_users (search by keyword, new /2/users/search endpoint)
2. xint_search with from: operator for known usernames
3. xint_reposts to find engaged users on specific tweets