「detect」の検索結果

Use when working with Aws Idle Resources — detect unused and idle AWS resources that incur cost without providing value. Covers detached EBS volumes, idle load balancers, unused Elastic IPs, stopped EC2 instances, idle NAT Gateways, old snapshots, and unused ENIs. Includes estimated monthly waste per resource and anti-hallucination rules for safe 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 when working with Aws Guardduty — aWS GuardDuty finding analysis, detector management, suppression rule review, and member account oversight. Covers threat detection summary, finding severity distribution, finding type breakdown, trusted IP lists, and organization-wide security posture.

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 when working with Aws Billing — analyze, break down, and report AWS costs and bills. Covers cost breakdown by service, account, or usage type; monthly/daily billing trends; cost anomaly detection; RI/SP utilization; cost forecasting; credit/discount analysis; and multi-account cost comparison. Uses anti-hallucination rules, mandatory currency/credit detection workflow, and reusable Cost Explorer functions.

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 when working with Aws Cost Optimization — aWS native cost optimization using Compute Optimizer, Trusted Advisor cost checks, Savings Plan analysis, Cost Explorer, and resource rightsizing. Covers EC2 rightsizing, idle resource detection, Savings Plan and Reserved Instance recommendations, and cost anomaly detection. Use when optimizing AWS infrastructure costs with native AWS tools.

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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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。

Elderly Care Tech Specialist IA — Expert en technologies pour personnes âgées (fall detection, medication reminders, social connection, accessibility)

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。

Expert en technologies de soin aux personnes âgées avancées (fall detection, medication reminders, social connection, accessibility)

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。

Computer Vision Specialist IA — Expert en vision par ordinateur (YOLO, OpenCV, object detection, OCR, segmentation)

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。

Implement computer vision features including text recognition (OCR), face detection, barcode scanning, image segmentation, object tracking, and document scanning in iOS apps. Covers both the modern Swift-native Vision API (iOS 16+) and legacy VNRequest patterns, VisionKit DataScannerViewController for live camera scanning, and VNCoreMLRequest for custom model inference. Use when adding OCR, barcode scanning, face detection, or custom Core ML model inference with Vision.

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, load, and apply skills before ANY code-related 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。

Detect when user is teaching best practices and persist as skills

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。

Android Accessibility Service security analysis and pentesting. Use this skill whenever the user mentions Android security testing, accessibility service abuse, RAT detection, malware analysis, ClayRat, PlayPraetor, overlay phishing, credential harvesting, or any Android app security assessment involving AccessibilityService APIs. This skill helps detect malicious accessibility services, analyze abuse patterns, and harden apps against accessibility-based attacks.

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。

Vendor (copy-import) a single external Claude Code skill into your own skills repo correctly — with license compliance and full provenance/attribution. Deterministic, idempotent primitive: given an upstream ref (owner/repo/path@sha or a GitHub URL) it fetches the skill, detects and gates on the license, copies the folder, injects a provenance frontmatter block (license + metadata: author/source/upstream_commit/vendored_at/provenance), ships the upstream LICENSE, adds a ## Credits section, and records a CREDITS.md ledger entry. Use when the user asks to "vendor a skill", "import a skill", "clone a skill", "borrow someone's skill", "add an external skill with attribution/credit", or スキルを取り込む / vendoring / 他人のスキルを自分のリポに取り込む. This is the import primitive that a skill-scout/curator orchestrator calls; it does NOT discover or evaluate — it correctly imports ONE skill you already chose.

Use when the user wants all 16 personality types (16-minds plugin) to vote on a proposal — yes/no/abstain — to see stakeholder distribution, detect hidden objections, or simulate organizational consensus. Triggers on Japanese phrases like 「全人格で投票」「16人格に賛否を聞いて」「ステークホルダーシミュ」「これ通せそう？」, English equivalents like "vote with all 16 minds" / "stakeholder simulation", or `--vote` flag on 16-minds commands. Different from /minds (which is for opinion divergence) — this is for binary judgment aggregation.

Detect Kerberos Golden Ticket forgery by analyzing Windows Event ID 4769 for RC4 encryption downgrades (0x17), abnormal ticket lifetimes, and krbtgt account anomalies in Splunk and Elastic SIEM

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 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.

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 Kerberos Pass-the-Ticket (PtT) attacks by analyzing Windows Event IDs 4768, 4769, and 4771 for anomalous ticket usage patterns in Splunk and Elastic SIEM

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。

Configures Fail2ban with custom filters and actions to detect port scanning activity, SSH brute force attempts, and network reconnaissance, automatically banning offending IP addresses and alerting security teams to suspicious network probing.

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 DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains and anomalous query patterns

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 and prevent QR code phishing (quishing) attacks that bypass traditional email security by embedding malicious URLs in QR code images within emails.

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 Golden Ticket attacks in Active Directory by analyzing Kerberos TGT anomalies including mismatched encryption types, impossible ticket lifetimes, non-existent accounts, and forged PAC signatures in domain controller event 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 lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, Pass-the-Hash activity, remote service execution, and anomalous internal connections.

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 abuse of legitimate Windows binaries (LOLBins) used for living off the land attacks. Monitors process creation, command-line arguments, and parent-child relationships to identify suspicious LOLBin execution patterns.

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。