Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/phy041/claude-skill-twitter/twitter-intelnpx skills add PHY041/claude-skill-twitter --skill twitter-intelgit clone --depth 1 https://github.com/PHY041/claude-skill-twitterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/phy041/claude-skill-twitter/twitter-intel)<a href="https://agentmods.dev/skills/phy041/claude-skill-twitter/twitter-intel"><img src="https://agentmods.dev/badge/skills/phy041/claude-skill-twitter/twitter-intel.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.01774 |
| Opus 5 | $0.00024 | $0.00887 |
| Sonnet 5 | $0.00010 | $0.00355 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
twitter-intel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Twitter Intel — Keyword Search & Trend Monitor
Search Twitter by keyword, collect high-engagement tweets, analyze trends over time, and generate structured reports. Powered by rnet_twitter.py GraphQL search (no browser automation needed).
Architecture
Phase 1: On-demand Search (user-triggered)
User says "search OpenAI on twitter" -> search -> filter -> report
Phase 2: Keyword Monitoring (cron-driven)
Config defines keywords -> scheduled search -> diff with last run -> alert on new high-engagement tweets
Phase 3: Trend Analysis (on-demand or weekly)
Aggregate saved searches -> group by week -> detect topic shifts -> generate narrative
Prerequisites
# Install rnet
pip install "rnet>=3.0.0rc20" --pre
# Required: rnet_twitter.py + cookies
# - rnet_twitter.py (included in this repo, has search_tweets method)
# - twitter_cookies.json (auth_token + ct0, valid ~2 weeks)
Cookie refresh: When search returns 403, cookies need refresh. Get new auth_token + ct0 from Chrome DevTools -> Application -> Cookies -> x.com.
Phase 1: On-demand Search
When user says "search [keyword] on twitter", "twitter intel [topic]", "find tweets about [X]":
Step 1 — Run Search
import asyncio
from rnet_twitter import RnetTwitterClient
async def search(query, count=200):
client = RnetTwitterClient()
client.load_cookies("twitter_cookies.json")
tweets = await client.search_tweets(query, count=count, product="Top")
return tweets
Search modes:
| Mode | product= |
Use case |
|---|---|---|
| High-engagement | "Top" |
Find influential tweets, content analysis |
| Real-time | "Latest" |
Monitor breaking discussions, live tracking |
Useful Twitter search operators:
| Operator | Example | Effect |
|---|---|---|
lang:en |
OpenAI lang:en |
English only |
since: / until: |
since:2026-01-24 until:2026-02-24 |
Date range |
-filter:replies |
OpenAI -filter:replies |
Original tweets only |
min_faves:N |
min_faves:50 |
Minimum likes (only works with Latest) |
from: |
from:karpathy |
Specific author |
"exact" |
"AI agent" |
Exact phrase |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 241 lines · 49 tokens per session scan A 5bb98608ea19
twitter-intel is a skill published in the GitHub repository PHY041/claude-skill-twitter (5 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 1,774 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
industry-intelligence-radar
行业情报雷达：多源扫描(X/即刻/V2EX/HN) → 关键词过滤 → 趋势检测 → 每日情报简报。触发词：行业情报、竞品监控、热点扫描、情报雷达.
news-monitor
Monitor news feeds, tech blogs, and competitive intelligence sources - track keywords, summarize articles, detect trends, and deliver digests on schedule.
xpoz-social-search
Search Twitter, Instagram, and Reddit posts in real time. Find social media mentions, track hashtags, discover influencers, and analyze engagement, with billions of posts indexed. Social listening, brand monitoring, and competitor research made easy for AI agents.
x-search-tool
X (Twitter) search tool for keyword search and fetching tweets from a specified account. Use when: searching X for topics, reading a user timeline, or tracking trends and posts.
xquik
Xquik X data automation API - Use REST or MCP for tweet search, user lookup, follower exports, media downloads, monitors, webhooks, giveaway draws, and confirmation-gated X actions.
social-trend-monitor
跨平台社交趋势监控，并行监控Reddit、Instagram、TikTok、Twitter热门内容，生成趋势分析报告.