65 件 ・ 関連度順
概要と使いどころ
Hunts for fileless and in-memory threats by correlating PowerShell script-block logs, encoded-command usage, in-memory loading patterns, and registry-resident payloads from Windows telemetry. Activates for requests to hunt fileless malware, detect in-memory execution, or find registry-resident and reflective-loading activity.
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Swiftのactorで共有データへのアクセスを管理し、メモリ内キャッシュとJSONファイル保存を組み合わせて、同時アクセス時のデータ競合を避ける保存処理を設計するスキル。
Detects fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.
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。
Drive feature development using Outside-In TDD with Hexagonal Architecture. Design emerges through inline code, in-memory fakes, interface extraction, and deferred I/O. Use when building features, writing tests, or structuring backend services. Triggers on: TDD, outside-in, hexagonal, ports and adapters, emergent design, acceptance test, component test, walking skeleton, in-memory fakes, component, contract test, adapter, fast tests, sub-second feedback. Language-agnostic (Go, Rust, Python, TypeScript, Java, C#).
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。
Applies data-grid architecture for high-traffic stateful workloads. Use when a single database cannot scale and in-memory partitioning is needed.
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 guide for in-memory Abstract Syntax Tree (AST) code knowledge graphs, real-time symbol dependency mapping, refactoring blast radius calculation, and dead code eradication / Panduan ahli graf pengetahuan AST kode in-memory, pemetaan dependensi simbol real-time, kalkulasi radius dampak refaktor, dan pembersihan kode mati.
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Expert guide for ephemeral WebAssembly (WASM) and isolated micro-runtime sandboxes to securely execute AI-generated code, validate database migrations in-memory, and perform sub-millisecond fuzzing / Panduan ahli sandbox WebAssembly (WASM) efemeral dan micro-runtime terisolasi untuk eksekusi kode AI yang aman, validasi migrasi database in-memory, dan fuzzing sub-milidetik.
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 fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.
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 fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.
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 fileless malware and in-memory attacks that execute entirely in RAM without writing persistent files to disk, evading traditional antivirus. Use when building detections for PowerShell-based attacks, reflective DLL injection, WMI persistence, and registry-resident malware. Activates for requests involving fileless malware detection, in-memory attacks, PowerShell exploitation, or living-off-the-land techniques.
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 Mimikatz credential-dumping activity via command-line pattern matching, LSASS access signatures, binary/hash indicators, and in-memory detection of known Mimikatz modules. Use when threat hunting for T1003 credential access, triaging EDR/SIEM alerts on LSASS access, scoping compromise during incident response, or validating detection coverage in a purple team 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。
Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat 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。
Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Use for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.
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。
Command and Control framework deployment, configuration, and operational tradecraft for red team engagements. Covers Cobalt Strike (malleable C2 profiles, Beacon types HTTP/HTTPS/DNS/SMB, Beacon Object Files for in-memory execution, sleep and jitter tuning, named pipe pivoting), Sliver (implant generation across mTLS/WireGuard/DNS transport, operator multiplayer mode, armory extensions), Mythic (agent ecosystem with Apollo/Poseidon/Medusa, C2 profile configuration, translation containers), Havoc (Demon agent with sleep obfuscation via Ekko/Zilean, indirect syscalls, dotnet inline execution), Metasploit (msfvenom payload generation, multi/handler staging, Meterpreter post-exploitation modules), redirector architecture using Apache mod_rewrite and Nginx, domain fronting through CDN providers, DNS-based C2 for restrictive network egress, and TLS certificate management for infrastructure OPSEC. Tools: Cobalt Strike, Sliver, Mythic, Havoc, Metasploit Framework. Aligns to MITRE ATT&CK T1071 (Application Layer Protocol), T1573 (Encrypted Channel), T1090 (Proxy/Connection Proxy).
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。
Scaffold, extend, and verify reactive local-first JavaScript or TypeScript applications with TinyBase. Use when choosing TinyBase for in-memory tabular or key-value state, generating an app with create-tinybase, adding schemas or UI bindings, resolving TinyBase import paths, configuring browser or database persistence, configuring MergeableStore synchronization over WebSockets or Cloudflare Durable Objects, or diagnosing an existing TinyBase application.
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。
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
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 this skill when integrating NVIDIA NeMo Fabric into a consumer application, service, evaluation harness, or platform through the typed Python SDK — translating the consumer's own application, job, or deployment config into an in-memory FabricConfig, choosing the single-invocation convenience API or an explicitly started runtime, validating with plan and doctor, and consuming normalized results, artifacts, and telemetry.
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。
In-memory caching in Golang using samber/hot — eviction algorithms (LRU, LFU, TinyLFU, W-TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, FIFO), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Apply when using or adopting samber/hot, when the codebase imports github.com/samber/hot, or when the project repeatedly loads the same medium-to-low cardinality resources at high frequency and needs to reduce latency or backend pressure.
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Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.
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。
Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.
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。
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
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。
Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile instead).
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Use when writing agent-relay workflows that must fully validate features end-to-end before merging. Covers the 80-to-100 pattern - going beyond "code compiles" to "feature works, tested E2E locally." Includes repair-before-failure validation gates, review-depth fresh-eyes review/fix loops with test hardening, PGlite for in-memory Postgres testing, mock sandbox patterns, test-fix-rerun loops, verify gates after every edit, and the full lifecycle from implementation through passing tests to 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。
Generate cross-module test harness with mock servers, in-memory stores, and test configuration. Use when testing networking + persistence + business logic together.
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。