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.
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 skills add uphiago/recon-skills --skill humanize-automationgit clone --depth 1 https://github.com/uphiago/recon-skillsWrote 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/uphiago/recon-skills/humanize-automation)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.02322 |
| Opus 5 | $0.00009 | $0.01161 |
| Sonnet 5 | $0.00004 | $0.00464 |
| Haiku 4.5 | $0.00002 | $0.00232 |
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.
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
terminalwith python3.cloakbrowserinstalled:pip install cloakbrowser.- Or standalone Playwright with custom patching: import
patch_pagefrom 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()
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.
- 10d ago First seen · 230 lines · 18 tokens per session scan A c35ba3580402
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.
Other skills, from other repositories
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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.
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.
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.
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.
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.