1,486 件 ・ 関連度順
概要と使いどころ
Use when the user needs to update, improve, audit, or maintain documentation in a project. Covers READMEs, changelogs, API docs, inline comments, missing docs, and overall documentation quality. Works well before releases or when documentation has fallen behind the code.
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。
Build incident response timelines and report packs from event logs. Use for detection-to-recovery reporting, phase tracking, and stakeholder-ready incident summaries.
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。
Generic read-only post-deployment verification workflow for confirming service health, artifact activation, routing, logs, and rollback signals after a deployment action.
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 and interpret execution debug logs generated by the EMULATORS framework, correlating each CPU instruction block with its VIC-II, CIA, SID, interrupt, stop, raster, badline, and memory-access cycles. Use for .LOG traces from EMULATORS when diagnosing cycle timing, raster effects, CPU stalls, IRQ/NMI entry, instruction/chip synchronization, or when reconstructing execution around a PC or mapped register.
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 managing Node.js dependencies with pnpm - provides workspace setup, catalogs, CLI commands, overrides, and CI configuration
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。
Structured Domain Modeling Architecture (SDMA) principles. Trigger when: - Designing domain models, building ologs (ontological graphs), verifying protocol equivalence, selecting schemas, or measuring architectural entropy. - Modeling structures using Category Theory, Coalgebra, or Information Theory. - Prompt contains keywords: sdma, olog, Category Theory, Coalgebra, Information Theory, entropy, schema, pullback, functor, monad.
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。
Real-time work context tracking for session continuity. Maintains task/step state across sessions, tracks progress, manages logs, integrates with phase enforcement.
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, revise, and validate publication-ready academic paper figures and tables. Use for LaTeX tables, related-work comparison tables, result tables, notation/dataset/taxonomy tables, precise source-data-driven experiment plots from CSV/JSON/logs, generated conceptual figures such as system overviews/pipelines/architectures/threat models, captions, artifact specs, source-data traceability, and paper-ready PDF/SVG/PNG/LaTeX exports. Do not use for prose-only paper writing, self-review, reviewer response, rebuttal drafting, or external literature-management workflows.
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。
Debug container agent issues. Use when things aren't working, container fails, authentication problems, or to understand how the container system works. Covers logs, environment variables, mounts, and common issues.
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。
Debug container agent issues. Use when things aren't working, container fails, authentication problems, or to understand how the container system works. Covers logs, environment variables, mounts, and common issues.
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 a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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 a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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 adversary lateral movement across networks using Splunk SPL queries against Windows authentication logs, SMB traffic, and remote service abuse.
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。
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.
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。
Deploys and configures Zeek (formerly Bro) network security monitor to passively analyze network traffic, generate structured logs, detect anomalous behavior, and create custom detection scripts for threat hunting and 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。
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。
Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.
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 unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
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 Azure AD/Entra ID environments using Microsoft Graph API audit logs, Azure Sentinel KQL hunting queries, and sign-in anomaly correlation to identify privilege escalation, token theft, and cross-tenant pivoting.
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。
Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach 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。
Detect LSASS credential dumping, SAM database extraction, and NTDS.dit theft using Sysmon Event ID 10, Windows Security logs, and SIEM correlation rules
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。
Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.
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。