Autonomous audit-to-fix pipeline — find issues, classify, fix, test, commit
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Autonomous audit-to-fix pipeline — find issues, classify, fix, test, commit
日本語の概要は準備中です。原文の説明を表示しています。
Add an idea to the backlog parking lot (999.x numbering)
日本語の概要は準備中です。原文の説明を表示しています。
Create a clean PR branch by filtering out .planning/ commits — ready for code review
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Archive accumulated phase directories from completed milestones
日本語の概要は準備中です。原文の説明を表示しています。
Retroactively audit an executed AI phase's evaluation coverage — scores each eval dimension as COVERED/PARTIAL/MISSING and produces an actionable EVAL-REVIEW.md with remediation plan
日本語の概要は準備中です。原文の説明を表示しています。
Archive completed milestone and prepare for next version
日本語の概要は準備中です。原文の説明を表示しています。
Capture idea or task as todo from current conversation context
日本語の概要は準備中です。原文の説明を表示しています。
Audit milestone completion against original intent before archiving
日本語の概要は準備中です。原文の説明を表示しています。
Run all remaining phases autonomously — discuss→plan→execute per phase
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Route freeform text to the right GSD command automatically
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Auto-fix issues found by code review in REVIEW.md. Spawns fixer agent, commits each fix atomically, produces REVIEW-FIX.md summary.
日本語の概要は準備中です。原文の説明を表示しています。
Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build systems.
日本語の概要は準備中です。原文の説明を表示しています。
Exploit the noPac vulnerability chain (CVE-2021-42278 sAMAccountName spoofing and CVE-2021-42287 KDC PAC confusion) to escalate from standard domain user to Domain Admin in Active Directory environments.
日本語の概要は準備中です。原文の説明を表示しています。
Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in locations, mail forwarding rules, and unusual API access patterns via Microsoft Graph and audit logs.
日本語の概要は準備中です。原文の説明を表示しています。
Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.
日本語の概要は準備中です。原文の説明を表示しています。
Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Detect data exfiltration through DNS tunneling by analyzing query entropy, subdomain length, query volume, TXT record abuse, and response payload sizes using passive DNS monitoring.
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Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.
日本語の概要は準備中です。原文の説明を表示しています。
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking.
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Deploy Cisco Duo multi-factor authentication across enterprise applications, VPN, RDP, and SSH access points. This skill covers Duo integration methods, adaptive authentication policies, device trust
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Deploys deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting detection (honeyroasting), decoy GPOs with cpassword traps, and fake BloodHound paths. Monitors Windows Security Event IDs 4769, 4625, 4662, 5136 for honeytoken interaction. Use when implementing AD deception defenses for detecting lateral movement, credential theft, and reconnaissance.
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Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.
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Deploys and monitors ransomware canary files across critical directories using Python's watchdog library for real-time filesystem event detection. Places strategically named decoy files that mimic high-value targets (financial records, credentials, database exports) in locations ransomware typically enumerates first. Monitors for any read, modify, rename, or delete operations on canary files and triggers immediate alerts via email, Slack webhook, or syslog when interaction is detected, providing early warning before full encryption begins.
日本語の概要は準備中です。原文の説明を表示しています。
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
日本語の概要は準備中です。原文の説明を表示しています。