humanize-automation

humanize-automation is a skill for Claude Code, Codex from uphiago/recon-skills. It costs 18 tokens per session (2,322 once invoked), scanned A, original, MIT.

A browser automation option that makes mouse movement, typing, and scrolling resemble human actions when using Playwright.

In plain words
What is it for?
Simulating browsing, form entry, login, or account creation flows where behavioral bot checks affect automated browser sessions.
Why use it?
It helps test or operate sites that react differently to very fast, perfectly regular automated input.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Simulating browsing, form entry, login, or account creation flows where behavioral bot checks affect automated browser sessions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/uphiago/recon-skills/humanize-automation
About the project

Recon Skills is a pack of security-testing skills covering reconnaissance, web applications, APIs, authentication, vulnerability validation, cloud infrastructure, and reporting. Security professionals use it for authorized assessments of systems they own or have written permission to test. The catalogue entries are individual skills from the pack.

uphiago/recon-skills · 1,251 stars · on GitHub · hiago.sh

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 uphiago/recon-skills --skill humanize-automation
Clone the repo
git clone --depth 1 https://github.com/uphiago/recon-skills

Made for: Claude Code, Codex.

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.

agentmods badge for humanize-automation

README.md
[![agentmods](https://agentmods.dev/badge/skills/uphiago/recon-skills/humanize-automation/github.svg)](https://agentmods.dev/skills/uphiago/recon-skills/humanize-automation)
Your own site
<a href="https://agentmods.dev/skills/uphiago/recon-skills/humanize-automation"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/humanize-automation/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.

agentmods 80×15 button for humanize-automation

Your own site · 80×15
<a href="https://agentmods.dev/skills/uphiago/recon-skills/humanize-automation"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/humanize-automation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,322 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00018 $0.02322
Opus 5 $0.00009 $0.01161
Sonnet 5 $0.00004 $0.00464
Haiku 4.5 $0.00002 $0.00232

Measured 10d ago against content hash c35ba3580402, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

humanize-automation 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 10d 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.

recon/humanize-automation/SKILL.md · 230 lines

How it starts

The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Humanize Automation

Replace instant programmatic interactions with human-like mouse movements, keyboard typing, and scroll patterns. Patches Playwright's API at the class level — page.click(), page.type(), page.fill(), and Locator methods are automatically replaced with Bézier-curved mouse paths, per-character typing with mistypes, and multi-phase scroll acceleration. One flag (humanize=True) enables all behavioral patches. No code changes required.

When to Use

  • Target uses behavioral bot detection (mouse trajectory analysis, typing speed profiling).
  • reCAPTCHA v3 scores are low (<0.3) despite correct browser fingerprint.
  • Target times out or challenges after rapid form submissions.
  • Need to simulate a real user browsing session for login or account creation.
  • Target uses requestAnimationFrame-based mouse movement tracking.

Prerequisites

  • terminal with python3.
  • cloakbrowser installed: pip install cloakbrowser.
  • Or standalone Playwright with custom patching: import patch_page from the humanize module.

Quick Start

from cloakbrowser import launch

browser = launch(humanize=True)
page = browser.new_page()
page.goto("https://target.com/login")

# All interactions are automatically humanized
page.locator("#email").fill("[email protected]")     # per-character timing
page.locator("#password").fill("password123")       # with thinking pauses
page.locator("button[type=submit]").click()         # Bézier curve movement
browser.close()

Procedure

Phase 1 — Default Humanization

One flag enables all behavior patches:

browser = launch(
    headless=False,
    proxy="http://residential-proxy:port",
    geoip=True,
    humanize=True,  # enables all behavioral patches
)
page = browser.new_page()
page.goto("https://target.com")

# All Playwright interactions are replaced with human-like equivalents
page.locator("input[name='search']").fill("restricted query")
page.locator("button[type='submit']").click()

Read the full file on GitHub · 230 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.

  1. 10d ago First seen · 230 lines · 18 tokens per session scan A c35ba3580402

Subscribe to this mod's changes

humanize-automation is a skill published in the GitHub repository uphiago/recon-skills (1,251 stars, last pushed 8d ago), licensed MIT. It adds 18 tokens to every session and 2,322 once invoked, about $0.0001 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 2026-08-30.

Related

Other skills, from other repositories

implementing-cloud-dlp-for-data-protection

Implementing Cloud Data Loss Prevention (DLP) using Amazon Macie, Azure Information Protection, and Google Cloud DLP API to discover, classify, and protect sensitive data across cloud storage, databases, and data pipelines.

xalgorix/xalgorix · 54 tokens

auditing-gcp-iam-permissions

Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.

xalgorix/xalgorix · 51 tokens

auditing-terraform-infrastructure-for-security

Auditing Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment.

xalgorix/xalgorix · 59 tokens

detecting-compromised-cloud-credentials

Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.

xalgorix/xalgorix · 55 tokens

detecting-misconfigured-azure-storage

Detecting misconfigured Azure Storage accounts including publicly accessible blob containers, missing encryption settings, overly permissive SAS tokens, disabled logging, and network access violations using Azure CLI, PowerShell, and Microsoft Defender for Storage.

xalgorix/xalgorix · 52 tokens

detecting-s3-data-exfiltration-attempts

Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

xalgorix/xalgorix · 63 tokens