Add 10 new cybersecurity skills with full folder anatomy

Skills added:
- implementing-privileged-access-workstation (IAM, PAW hardening)
- detecting-suspicious-oauth-application-consent (cloud security, Graph API)
- performing-hardware-security-module-integration (cryptography, PKCS#11)
- analyzing-android-malware-with-apktool (malware analysis, androguard)
- hunting-for-unusual-service-installations (threat hunting, T1543.003)
- detecting-shadow-it-cloud-usage (cloud security, proxy/DNS log analysis)
- performing-active-directory-forest-trust-attack (red team, impacket)
- implementing-deception-based-detection-with-canarytoken (deception, Canary API)
- analyzing-office365-audit-logs-for-compromise (cloud security, BEC detection)
- hunting-for-startup-folder-persistence (threat hunting, T1547.001)

Each skill includes SKILL.md, LICENSE, scripts/agent.py, references/api-reference.md

skills/detecting-shadow-it-cloud-usage/SKILL.md

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+---
+name: detecting-shadow-it-cloud-usage
+description: Detect unauthorized SaaS and cloud service usage (shadow IT) by analyzing proxy logs, DNS query logs, and netflow data using Python pandas for traffic pattern analysis and domain classification.
+domain: cybersecurity
+subdomain: cloud-security
+tags: [shadow-IT, SaaS-discovery, proxy-logs, DNS-analysis, netflow, cloud-security, pandas]
+version: "1.0"
+author: mahipal
+license: Apache-2.0
+---
+
+# Detecting Shadow IT Cloud Usage
+
+## Overview
+
+Shadow IT refers to unauthorized SaaS applications and cloud services used without IT approval. This skill analyzes proxy logs, DNS query logs, and firewall/netflow data to identify unauthorized cloud service usage, classify discovered domains against known SaaS categories, measure data transfer volumes, and flag high-risk services based on security posture and compliance requirements.
+
+## Prerequisites
+
+- Python 3.9+ with `pandas`, `tldextract`
+- Proxy logs (Squid, Zscaler, or Palo Alto format) or DNS query logs
+- SaaS application catalog/blocklist for classification
+- Network firewall logs with FQDN resolution (optional)
+
+## Steps
+
+1. Parse proxy access logs and extract destination domains with traffic volumes
+2. Parse DNS query logs to identify resolved cloud service domains
+3. Aggregate traffic by domain using pandas — total bytes, request counts, unique users
+4. Classify domains against known SaaS categories (storage, email, dev tools, AI)
+5. Flag unauthorized services not on the approved application list
+6. Calculate risk scores based on data volume, user count, and service category
+7. Generate shadow IT discovery report with remediation recommendations
+
+## Expected Output
+
+- JSON report listing discovered cloud services with traffic volumes, user counts, risk scores, and approval status
+- Top unauthorized services ranked by data exfiltration risk