「detection」の検索結果

Builds a structured ransomware incident response playbook aligned with the CISA StopRansomware Guide and NIST Cybersecurity Framework, covering preparation, detection, containment, eradication, recovery, and post-incident phases with actionable checklists. Use when creating or updating a ransomware playbook, running a CISA-aligned readiness assessment, or validating response steps during a tabletop exercise.

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。

Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident 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。

Analyzes UEFI bootkit persistence (SPI flash implants, ESP modifications, Secure Boot bypass, UEFI variable manipulation) using chipsec for firmware integrity verification, detecting known families like BlackLotus, LoJax, and MoonBounce. Use for UEFI malware analysis, firmware persistence investigation, or Secure Boot bypass detection.

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。

Analyzes Windows Security, System, and Sysmon event logs in Splunk to detect authentication attacks, privilege escalation, persistence mechanisms, and lateral movement using SPL queries mapped to MITRE ATT&CK techniques. Use when SOC analysts need to investigate Windows-based threats, build detection queries, or perform forensic timeline analysis of Windows endpoints and domain controllers.

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。

Parse Windows PowerShell Script Block Logs (Event ID 4104) from EVTX files to detect obfuscated commands, encoded payloads, and living-off-the-land techniques. Uses python-evtx to extract and reconstruct multi-block scripts, applies entropy analysis and pattern matching for Base64-encoded commands, Invoke-Expression abuse, download cradles, and AMSI bypass attempts.

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。

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.

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。

Analyzes bootkit and advanced rootkit malware infecting the Master Boot Record (MBR), Volume Boot Record (VBR), or UEFI firmware for below-OS persistence, covering boot sector analysis, UEFI module inspection, and anti-rootkit detection. Use when compromise survives OS reinstallation or antivirus/EDR fails to detect malware despite clear infection signs.

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。

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware 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。

Query the Malpedia API to look up malware family aliases and naming (platform.family_name), pull community/vendor YARA rules, link families to threat actors, and map family relationships such as loader-payload chains and shared authorship. Use when researching a malware family's aliases, lineage, or actor attribution, or when sourcing YARA rules for detection.

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。

Use Scapy to craft, send, sniff, and dissect TCP/UDP/ICMP/DNS packets, analyze pcap files, implement SYN scans, and detect anomalous traffic such as fragmented or malformed packets. Use when performing authorized network reconnaissance, protocol-level forensic analysis, or building traffic anomaly detection during security testing.

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 and analyze covert communication channels used by malware, including DNS tunneling, ICMP exfiltration, steganographic HTTP, and other protocol abuse used for C2 and data exfiltration. Use when investigating suspicious DNS/ICMP/HTTP traffic patterns, hunting for hidden C2 channels in network captures, or attributing exfiltration traffic to a known tunneling toolset.

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。

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain framework to identify which phases an adversary has completed, where defenses succeeded or failed, and what controls would have interrupted the attack at earlier phases. Use when conducting post-incident analysis, building prevention-focused security controls, or mapping detection gaps to kill chain phases. Activates for requests involving kill chain analysis, intrusion kill chain, attack phase mapping, or Lockheed Martin kill chain framework.

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。

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops). Use after reverse engineering reveals network traffic needing protocol analysis or when building detection signatures for a framework like Cobalt Strike, Metasploit, or Sliver.

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。

Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF threats.

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。

Examine Linux system artifacts (auth logs, cron/systemd persistence, shell history, SSH keys, and system configuration) to uncover evidence of compromise, detect rootkits or backdoors, and reconstruct user/attacker activity. Use when investigating a compromised Linux server or workstation, hunting for persistence mechanisms, or scoping a Linux-based breach during incident response.

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。

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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。

Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.

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。

Perform a structured code review using change detection and impact

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 which gbrain engine is in use (PGLite vs Postgres), prefer Postgres for agent-harness installs, install/provision Postgres (Supabase discovery via SUPABASE_ACCESS_TOKEN, local Postgres, opt-in Docker), and move an existing PGLite brain to Postgres with its history (plan, ask, run). Detection is one engine-free command; the install ladder is one flag.

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。

Filing gate for ALL brain writes. Consulted before creating any new brain page to determine the correct path. Reads the ACTIVE schema pack via `gbrain schema show --json` — no hardcoded directory table. Also runs periodic taxonomy drift detection via `gbrain schema review-orphans`.

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。

Brain health checks: back-link enforcement, citation audit, filing validation, stale info detection, orphan pages, and benchmarks. Use when asked to check brain health, run maintenance, or audit quality.

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。

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

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。

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

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。

Open the context-mode Insight dashboard in your default browser. Insight is the hosted analytics layer for AI-assisted engineering teams — per-engineer productive rate, retry waste, blocker detection, role-narrowed views. Trigger: /context-mode:ctx-insight

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。