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    A hand-rolled Rust browser stack for fingerprint-resistance and anti-bot evasion.

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    See it in action

    You say

    Fetch https://example.com/data using a Stranger identity and report the JA4 fingerprint and any detected anti-bot vendors.

    Your agent does

    Fetched https://example.com/data. Outcome: Served. Vendor: None detected. JA4: t13d0912h2_f91f431d341e_33e34973addd. ALPN: h2. Protocol: TLS 1.3. Identity audit: Coherent.

    What you get

    Bypass Akamai and Cloudflare detection using custom TLS/H2 fingerprints.Generate human-like mouse trajectories to evade behavioral analysis.Identify honeypots and hidden traps in robots.txt and page source.Simulate native YouTube playback via Innertube API integration.safe web browsing and fetching for AIbuilt in tailscale support

    About this skill

    The problem

    Standard automation tools like Playwright and Selenium are easily flagged by modern anti-bot systems because their underlying network stacks and execution patterns leave distinct fingerprints. Even with stealth plugins, inconsistencies between TLS handshakes, HTTP/2 settings, and TCP options often lead to immediate blocks or shadowbans.

    What it does

    • Generates coherent "Stranger" identities where TLS extensions, HTTP/2 frames, and TCP SYN options are cross-audited for consistency.
    • Bypasses detection via a custom Rust network stack built from raw TLS 1.3 bytes, avoiding common fingerprints from OpenSSL or rustls.
    • Simulates human-plausible mouse movement using Egregore trajectories with randomized curvature, jitter, and element-specific aim bias.
    • Classifies server responses to identify specific security vendors like Cloudflare, DataDome, and Akamai while suggesting remediation steps.
    • Automates robots.txt analysis to identify honeypots, hidden links, and crawl budget constraints before interaction.

    Frameworks & tools

    Built with Rust. Interfaces via a multicall binary or SOCKS5/Tor proxies. Integrated with YouTube Innertube API for native playback simulation.

    Why this beats prompting it yourself

    Generic LLM prompts cannot fix a leaked TLS fingerprint or an inconsistent TCP window size. This skill provides a specialized low-level toolkit that handles the complex math of KDF schedules and wire-level protocol enforcement that standard libraries abstract away.

    Use cases

    • Scraping high-security targets that block standard headless browsers.
    • Auditing site security by identifying traps, honeypots, and WAF vendors.
    • Automating native YouTube playback and stream extraction without triggering bot detection.
    • Testing geographic coherence and ASN-based reachability across residential or proxy networks.

    Known limitations

    Requires a local Rust environment to compile the carcosa-rs crate. The Stranger identity does not spoof existing browsers; it presents a unique, coherent identity that some sites may block by default. Updates roll in consistently.

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    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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