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概要と使いどころ
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
Install Acontext, Login & Init Acontext Project, Add Skill Memory to Agent.
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。
Recall past facts, preferences, and decisions from ReMe; inspect memory delivery, service health, and the consolidation schedule; or consolidate memory when requested.
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。
Set up and use ReMe as a file-native long-term memory system through the reme CLI. Use when an Agent needs to detect whether ReMe is installed or running, install and configure ReMe, start or verify its local service, retrieve prior context, or write and consolidate durable 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。
Reclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.
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。
Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit 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。
Use this skill for hands-on DOCA GPI programming — wiring a GPU-Packet-Initiator context so a CUDA kernel drives RDMA queues directly from GPU memory without host CPU mediation. Covers picking GPI vs doca-gpunetio, the doca_gpi / domain / channel object model, the GPU-side handle handoff (doca_gpu_gpi_channel*), attaching GPU memory to a GPI domain, the domain and channel attribute objects, and debugging DOCA_ERROR_* from doca_gpi_* calls. Trigger even when the user does not explicitly mention "DOCA GPI" — implicit phrasings include "my CUDA kernel needs to post RDMA directly from GPU memory", "DOCA_ERROR_* from doca_gpi_gpu_channel_get", "how do I hand a GPU handle to my CUDA kernel", "how many channels can a GPI domain hold", or "GPU kernel driving RDMA without the host CPU on the path". Refuse and route elsewhere for the doca-gpunetio Send/Receive surface, the doca-rdma queue lifecycle, DPA-side initiation (doca-rdmi), or the CUDA programming model — those belong to other 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。
Use this skill when the user is doing hands-on DOCA Flow DPA Provider work — exporting a `doca-flow` pipe or external resource (index-selector/memory) into BlueField DPA address space so a DPACC-built kernel can read counters, mutate hash-pipe entries, and update/read memory or index-selector resources inline with Flow. Covers per-port `doca_flow_dpa_ctx`, three queue types (general/resources-write/resources-read), the order-sensitive export handshake (`_export_prepare` → add entries → `_export` → `_get_device_addr`), and DPA-side device API. Trigger even when the user does not say "DOCA Flow DPA Provider" — implicit phrasings include "DPA kernel never sees entries in the exported pipe", "BAD_STATE from `_pipe_export`", "how do I disable a hash entry from a DPA kernel", "DPA memory read returns no value", or "DPA-side post keeps returning AGAIN". Refuse and route elsewhere for `doca-flow` pipe construction, generic host-side DPA (`doca-dpa`), or DPA-side kernel-writing — those belong to other 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。
[omh] Software slowness, memory leaks, or cost spikes: find where a system is actually slow, leaking, or expensive across runtime, memory, token cost, storage, rendering, inference, CI, and query domains, then fix one measured hot path at a time behind a regression budget. Use when the user says: ultraperf, ulw-perf, performance audit, performance bottleneck, find the bottleneck, profile the hot path, memory leak investigation, token cost hotspot.
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 the user asks to "remember project context", review saved findings, initialize runtime memory, archive stale work, reconcile notes, or erase a subject; manages authorized HOT/WARM/COLD working memory across all disciplines while preserving registry event ownership and privacy controls. Not for changing canonical registry facts - route those through the owning registry. 项目记忆/跨会话
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 to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource removal. Triggers: "add memory", "remember across sessions", "call agent from app", "invoke agent from code", "agent auth", "streaming", "VPC", "VPC connectivity", "can't reach from VPC", "multi-agent", "A2A", "A2A auth", "orchestrator not delegating", "specialist not called", "migrate Bedrock Agent", "migration issue", "change model", "browser tool", "code interpreter", "delete agent", "tear down", "agentcore remove", "cross-account memory", "add payments capability to my agent", "wire payments plugin", "integrate x402 payments with the agent I'm building", "add MPP payments", "Machine Payments Protocol". External APIs via Gateway: use agents-connect. New project: use agents-get-started. CLI/dev-server errors: use agents-debug. Runtime x402/MPP payments: use agents-pay. Migration-specific Strands vs LangGraph routes here.
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 Apify Actor performance: crawl speed, memory usage, concurrency, and proxy rotation. Use when Actors are slow, consuming too much memory, or being blocked by target sites, or when a crawl is too expensive per run. Trigger with "apify performance", "optimize apify actor", "apify slow", "crawlee concurrency", "apify memory tuning", "scraper 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。
Memory forensics playbook using Volatility 2/3. Use when analyzing memory dumps for malware analysis, credential extraction, process investigation, code injection detection, and incident response timeline reconstruction.
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 the memmy-memory CLI when an agent needs to check the Memory service, open or close agent sessions, start or complete turns, search, add, read, or delete memories, or call uncommon HTTP routes through raw requests.
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。
Zep memory server integration for long-term conversation memory and user profiling
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。
Draft and review contributions to existing MEX project knowledge in the repo wiki. Use when the user wants to capture a team decision, convention, or architecture note, propose an addition or correction for team review, or invokes /mex-inbox or $mex-inbox. Also supports existing Spec proposals. Keywords: mex, project memory, CLAUDE.md, team memory, drift, inbox. Do not activate for brainstorming alone, routine GROW upkeep, email inboxes, session logs, or handoffs.
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 memory-safe programming with RAII, ownership, smart pointers, and resource management across Rust, C++, and C. Use when writing safe systems code, managing resources, or preventing memory bugs.
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。
Global team and org memory powered by Activeloop. ALWAYS check BOTH built-in memory AND Hivemind memory when recalling information.
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。
Work with Zep's temporal memory: create users and threads, append messages, read distilled facts and history. Trigger phrases: zep, knowledge graph memory, conversation 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。
Use when the user mentions memory leak prevention, code review for memory issues, or proactive leak checking — retain cycles, leaks, and Timer/observer 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。
Use when capturing or analyzing an iOS .memgraph, especially when the task mentions a memory leak, heap growth, persistent memory increase, ownership path, or matched-capture comparison with Apple CLI tools. Covers unambiguous Simulator capture, leaks/heap/vmmap/malloc_history evidence, raw artifact preservation, and same-flow verification. Use debugging-instruments for interactive Xcode Memory Graph, Instruments, generic retain-cycle inspection, or LLDB work.
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。
Prune stale memory entries and consolidate the memory index
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。
Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.
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 persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。