Xint Clawdhub Xint KlOE6H
Verified@0xNyk
npx machina-cli add skill @0xNyk/xint --openclawxint — 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 - only set in environment or
.envfile - 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
watchandstreamcan 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 document is descriptive; it does not modify runtime/system prompts
- 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 | bashwhen possible - Verify any installer scripts before running
MCP Server (Optional)
bun run xint.ts mcpstarts a local MCP server exposing xint commands as tools- Default mode is stdio/local integration; no inbound web server unless
--sseis explicitly enabled - Respect
--policy read_only|engagement|moderationand 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 forfrom:usernamein 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 todata/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-3, grok-3-mini (default), 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 + searchXAI_MANAGEMENT_API_KEY(management-api.x.ai): collections management + attaching documents
Notes:
- Never print keys.
- Prefer
--dry-runwhen wiring new cron jobs.
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 totee,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-3-mini)--pages N— search pages to fetch (default: 2)--save— save report todata/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.comorurl:specific domains - Noise reduction:
-is:retweet(auto-added), add-is:replyif 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
threadcommand? - 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/.
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
- Grok AI (sentiment/analyze/report): billed by xAI separately (not X API)
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)
│ ├── report.ts (intelligence report generation)
│ ├── sentiment.ts (AI-powered sentiment analysis via Grok)
│ ├── trends.ts (trending topics — API + search fallback)
│ └── 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)
Overview
Xint is a general-purpose agentic research tool for X/Twitter. It decomposes research questions into targeted searches, iteratively refines results, follows threads, deep-dives linked content, and synthesizes a sourced briefing. It supports bookmarks, likes, following, trending topics, Grok AI analysis, and multiple export formats (JSON, JSONL, CSV, Markdown).
How This Skill Works
Xint uses the X API v2 bearer token to perform searches, profiles, threads, and trends requests. When available, Grok AI analysis via XAI API provides sentiment scoring and article insights; results can be exported in JSON, JSONL, CSV, or Markdown. Operations that affect user context (bookmarks, likes, follows) are enabled through OAuth, and the tool runs from your project directory to decompose queries and iteratively refine findings.
When to Use It
- Research topics on X/Twitter from the terminal (e.g., phrases like 'x research' or 'search x for').
- Monitor real-time X discourse for a topic and watch for trends or spikes.
- Get AI-assisted insights or a Grok-style briefing from recent tweets.
- Dive into threads and linked content for context during a project (new releases, API changes).
- Export results as JSON, JSONL, CSV, or Markdown for reports and sharing.
Quick Start
- Step 1: Configure credentials in your environment (X_BEARER_TOKEN at minimum; XAI_API_KEY optional).
- Step 2: Run an initial search from your project directory, e.g., 'xint x research api changes'.
- Step 3: Export results as JSON/CSV/Markdown and review any Grok analysis if enabled.
Best Practices
- Define a tight search scope before running queries to minimize noise.
- Store credentials securely (ENV or .env with restricted permissions) and avoid hardcoding.
- Use AI analysis sparingly; validate findings with direct sources.
- Keep a reproducible export trail; export intermediate results for audit.
- Iterate: start broad, then refine with targeted follow-ups and threads.
Example Use Cases
- Summarize sentiment around a new API release.
- Monitor discussions about a product launch and compile a briefing.
- Track developer opinions on a library update and compare perspectives.
- Follow threads on a controversial topic for a quick consensus view.
- Create a daily briefing export for a team meeting.