Automate Twitter/X Growth Across Account, Research, and Strategy

Runs Twitter/X automation at scale without official API ($100+/mo), browser detection, or rate-limiting. It uses rnet, a Rust HTTP client with Chrome TLS fingerprint emulation, paired with Twitter's internal GraphQL API accessed through browser cookies, so you get search, posting, engagement analysis, and account health metrics with zero third-party API dependency. This replaces dead tools (Twikit, Nitter) and detectable automation (Playwright, Selenium).

Once set up with rnet and Twitter cookies, you have three workflows: 1. Account Cultivation: run health checks, get unfollow recommendations, and find engagement opportunities. 2. Keyword Intel: search 200+ tweets by keyword, filter by engagement, and analyze trends over time. 3. GTM Strategy: apply hook formulas and thread templates, then draft content matched to viral patterns in your niche. Output: end-to-end account and content automation with research data flowing into strategy.

Full Twitter automation (account growth, research, posting) controlled by Claude without hitting paid API limits or detection systems.

Requires Python 3.8+, rnet >=3.0.0rc20 (Rust-based HTTP client installed via pip), and two cookies (auth_token and ct0) from a logged-in Twitter session saved as JSON. The lightweight async wrapper rnet_twitter.py is included; SearchTimeline API requires POST (not GET), and this is handled automatically. Twitter's endpoint IDs change periodically, so monitor for 404 errors on search.

Cookies expire every ~2 weeks and need manual refresh from Chrome DevTools. GraphQL query IDs rotate periodically; if search breaks with 404, you must extract the new SearchTimeline ID from Twitter's JavaScript bundles and update rnet_twitter.py. Rate limits are ~300 requests per 15 minutes (safe for regular use, but not for 1000s of concurrent searches). Shadowban detection is approximate and requires manual verification.