What is twitter-intel?

A Twitter/X search and monitoring tool for finding tweets about chosen keywords and analyzing how topics change over time.

Searching for tweets, monitoring keywords for new high-engagement posts, and tracking topic shifts.

It turns scattered tweet searches into saved results, engagement comparisons, alerts, and trend reports.

twitter-intel — Skill for Claude Code by PHY041/claude-skill-twitter

Install

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.

Any agent

npx skills add PHY041/claude-skill-twitter --skill twitter-intel

Clone the repo

git clone --depth 1 https://github.com/PHY041/claude-skill-twitter

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/phy041/claude-skill-twitter/twitter-intel/github.svg)](https://agentmods.dev/skills/phy041/claude-skill-twitter/twitter-intel)

Your own site

<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/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<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="Reviewed on agentmods" width="80" height="20"></a>

Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,774 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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 5.5	$0.00020	$0.00710
Sonnet 5.5	$0.00010	$0.00355
Haiku 4.5	$0.00005	$0.00177

Measured 2mo ago against content hash 5bb98608ea19, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/skills/twitter-intel/SKILL.md · 241 lines

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

Read the full file on GitHub · 241 lines

Changes

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.

2mo ago First seen · 241 lines · 49 tokens per session scan A 5bb98608ea19

Subscribe to this mod's changes

twitter-intel is a skill published in the GitHub repository PHY041/claude-skill-twitter (5 stars, last pushed 8mo ago), licensed MIT. It adds 49 tokens to every session and 1,774 once invoked, about $0.0002 per session on Opus 5.5. 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.

Analytics and metrics Marketing and SEO intelligence monitoring search trends twitter

twitter-intel next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
twitter-intelPHY041/claude-skill-twitter	Skill	5	8mo ago	MIT	A	49	Claude Code
news-monitorchainlesschain/chainlesschain	Skill	11	yesterday	none	A	30	Claude Code
xpoz-social-searchXPOZpublic/xpoz-clawhub-skills	Skill	6	1mo ago	none	A	53	Claude Code, Codex
x-search-toolxuiltul/animaworks	Skill	269	2d ago	Apache-2.0	A	48	Claude Code, Codex
xquikbobmatnyc/claude-mpm-skills	Skill	78	3mo ago	MIT	A	44	Claude Code
social-trend-monitorrefly-ai/refly-skills	Skill	204	5d ago	none	A	32	Claude Code, Codex
x-mcpkandotrun/x-mcp	Skill	0	25d ago	MIT	A	70	Claude Code, Codex