「memory」の検索結果

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

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。

Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.

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。

Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM 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。

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing 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。

Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.

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。

Write, append, or read desk journal entries. The journal is persistent memory — what survives session boundaries. A good entry has: what was done, current state, next step.

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。

Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational guidance could improve reliability. Do not use for casual chat or simple factual questions.

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 reusable project knowledge and decide what belongs in project memory, notepad, or durable docs

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Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project memory optimization".

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Runs Inspector UI work on the standalone Threads state lab so the agent can see the overlay. Use when a CopilotKit employee asks an agent to fix, polish, add, or debug Inspector UI, chrome, panes, overlay actions, launcher, Home, Threads, Playground, Memory, or any visual behavior in @copilotkit/web-inspector. Don't use for docs-only Callout updates (load inspector-docs), runtime or core changes with no Inspector UI, showcase cell work, or reading Inspector as a consumer inside an app.

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Use for any CopilotKit question — adding it to an app, chat UI, frontend or server tools, generative UI, shared state, human-in-the-loop, agent frameworks (LangGraph, CrewAI, Mastra, ADK, PydanticAI, and others), the runtime, Intelligence, threads, voice, or diagnosing something that is not working. Do not answer from memory: this skill exists to point you at the current documentation and source, both of which you can read.

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Maps topics and keywords from user questions to QwenPaw official documentation paths and common source code entry points, reducing blind searching. Intended for the built-in QA Agent to quickly identify which files to read when answering questions about installation, configuration, skills, MCP, multi-agent, memory, CLI, etc.

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Detect OS credential dumping (MITRE T1003) targeting LSASS memory, the SAM database, NTDS.dit, and cached credentials by correlating EDR telemetry, Sysmon process-access events, and Windows security event logs. Use when hunting for Mimikatz-style credential theft, triaging an EDR alert on LSASS access, or scoping an incident after suspected credential dumping.

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Detect process injection techniques (T1055) - including DLL injection, process hollowing, and APC injection - by analyzing Sysmon Event IDs 1, 7, 8, 10, and 25 for cross-process memory operations, remote thread creation, and anomalous DLL loads. Use when hunting defense-evasion activity that hides code inside legitimate processes, investigating an EDR alert on suspicious cross-process access, or validating Sysmon coverage for injection detection.

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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.

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Detect process hollowing (MITRE T1055.012) by analyzing memory-mapped sections, hollowed process indicators, and parent-child anomalies using EDR telemetry, Volatility's malfind plugin, pe-sieve, Hollows Hunter, and Sysmon Event ID 25. Use when investigating a legitimate-looking process (svchost.exe, explorer.exe, rundll32.exe) suspected of hosting injected code via NtUnmapViewOfSection.

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Perform Pass-the-Ticket (PtT) lateral movement by extracting Kerberos TGT/TGS tickets from LSASS memory on a compromised host and injecting them into another session to impersonate the ticket owner without knowing their password. Use during authorized post-exploitation to move laterally or escalate access via stolen Kerberos tickets, or to validate detections for Kerberos ticket theft and reuse.

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Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

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Extract and analyze Cobalt Strike beacon configuration from PE files and memory dumps to identify C2 infrastructure, malleable profiles, and operator tradecraft.

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。

Write efficient C code with proper memory management, pointer

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Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

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。

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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。

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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。