“memory”的搜索结果

Inspect CPU, memory, disk, and accelerator information alongside container and scheduler limits. Produce a JSON snapshot with identifying details removed and conservative worker and memory estimates for planning local workloads, without running stress tests.

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• Choosing worker counts before a large local job
• Checking CPU and memory limits inside a container
• Comparing resource snapshots before and after changes

Answer questions about past decisions, usual practices, and changes over time by reasoning across Hindsight memories. When information spans multiple memory banks, combine the answers and identify their sources, while acknowledging gaps or contradictions.

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• Check what was decided in previous discussions
• Review how the team usually handles a task
• Trace how a practice changed over time

Save facts, decisions, preferences, and task outcomes to Hindsight long-term memory. The skill turns what was learned into short notes with context and reasons, choosing either a Bot’s own memory store or a shared store.

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• Saving a completed task’s outcome and reasoning
• Remembering a user’s stated preferences
• Recording decisions for future work

Carry context between scheduled or event-triggered routine runs using Hindsight memory. Add recall and recording steps to routine instructions so each run can use previous findings, resume unfinished work, compare changes, and leave guidance for the next run.

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• Add memory steps when creating or editing a routine
• Skip work already handled in a previous run
• Resume unfinished tasks and compare changes

Retrieve relevant Hindsight memories before planning or answering a request. Searches the agent’s own and shared memory stores, plus other relevant stores when needed, to bring past decisions, user preferences, and project context into the task.

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• Checking past decisions before planning work
• Answering with previously recorded user preferences
• Finding context saved by other Bots or AI tools

Design agent memory that preserves facts, preferences, and conversation history across sessions. Plan storage, retrieval, and retention separately for each organization or user, with checks for unauthorized access, outdated information, conflicting records, and retrieval quality.

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• Designing memory that remembers preferences across sessions
• Separating conversation history by organization and user
• Investigating retrieval of outdated or conflicting records

Guides users through removing a stored fact with Codewhale’s Context Lens memory controls. It identifies the intended fact, explains the forget action, and distinguishes confirmed deletion from an action that has not yet completed.

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• Removing a fact from stored memory
• Finding the Context Lens forget action
• Checking whether memory deletion has completed

Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.

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• Choosing generation settings for a specific model and GPU
• Reducing GPU memory use to fit a memory budget
• Comparing performance settings across multiple GPUs

Investigate signs of Mimikatz credential theft using command lines, access to Windows authentication memory, file indicators, and known modules in memory. Correlate endpoint and security logs to assess suspicious alerts, document evidence, and recommend response actions.

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• Investigating alerts about Windows authentication memory access
• Hunting for signs of credential theft
• Assessing compromise scope during incident response

Investigate process hollowing, where malicious code replaces a legitimate process in memory. Compare memory with disk files, examine process relationships, and correlate network activity to document suspicious processes and guide containment.

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• Investigating process injection alerts from EDR
• Examining suspicious behavior in legitimate Windows processes
• Finding threats that run in memory

Write and improve C code for systems and embedded projects, with explicit memory ownership, pointer handling, and error checks. The skill also covers build files, unit tests, debugging, and performance measurement.

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• Reviewing memory allocation and cleanup in C code
• Writing embedded code with limited memory
• Implementing threading with pthreads

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

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• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Fine-tune large language models with LoRA and QLoRA while limiting GPU memory use. Guides training settings and adapter management, using small sets of task-specific weights that can be saved, switched, or merged into a base model.

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• Fine-tuning an LLM with limited GPU memory
• Choosing LoRA settings and training layers
• Switching task-specific adapters on one base model

Build collaborating AI teams with CrewAI by defining roles, tasks, and shared context. Configure ordered execution or manager-led delegation, add memory and tools, and create workflows that branch based on events or results.

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• Build a research-to-writing agent workflow
• Coordinate specialists through a manager agent
• Create workflows with conditional branches

Ask questions about your journal, Obsidian vault, or other text notes, and continue conversations that remember earlier chats. The companion uses cognee memory with local databases, reads your notes without changing them, and saves conversations for later reference.

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• Asking questions about journal entries
• Finding information in an Obsidian vault
• Continuing a conversation with earlier chats in memory

Add GBrain memory to an existing agent or connect a hosted brain while preserving current instructions and identity. Start with storage and keyword search that need no API key, then verify saving, retrieval, and recall across conversations.

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• Add memory to an existing Claude Code or Codex agent
• Connect an agent to a hosted brain
• Diagnose an incomplete installation while preserving memory

Compare assigned work from connected trackers with TASKS.md, review stale tasks, and fill gaps in stored context about people and projects. An optional comprehensive scan finds possible missed commitments in recent activity, with changes confirmed by the user.

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• Review assigned tracker tasks against TASKS.md
• Triage overdue tasks and long-standing active items
• Fill missing context about people, projects, and acronyms

Finish a NanoClaw v1-to-v2 migration after the migration script completes. The skill checks real message routing, assigns owner access, coordinates legacy memory migration, reviews container settings, and helps decide how to carry over customizations.

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• Finish follow-up work after the v2 migration script
• Review owner permissions and sender access
• Migrate legacy memory and check container settings

Save agent memory and outputs in Cua Volume, shared storage that persists beyond a Space, the working environment. Hand files to the user, read shared references, and check synchronization, version history, and conflicting edits.

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• Keep outputs after a working environment ends
• Save agent memory for later work
• Deliver files to the user

Shows recent work sessions for the current project as a timeline, newest first. It includes session identifiers, start times, status, recorded observation counts, and available highlights or decisions, using only history returned by the memory tool.

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• Checking what happened in the previous session
• Reviewing recent work in one project
• Finding decisions recorded in past sessions

Resume work using the latest recorded session matching the current project directory. The skill surfaces unanswered questions first, summarizes relevant decisions and files, and provides a concrete next step while checking that records belong to the selected project.

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• Resuming work interrupted in a previous session
• Checking an unanswered question before continuing
• Reviewing key decisions and related files

Investigate slow Node.js, Python, and Go applications by measuring CPU use, memory, database queries, and data transfer. Identify bottlenecks, inspect frontend bundle sizes, run load tests, and compare measurements before and after changes to verify improvements.

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• Investigating slow API responses
• Tracking memory growth and suspected leaks
• Analyzing bundle growth after adding dependencies

Reconcile project documentation, agent rules, authorized memory, and workspace leftovers with current code and runtime behavior. Prepare a clear handoff by correcting stale information, identifying authoritative explanations, and reporting what is verified, pending, or awaiting cleanup approval.

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• Synchronizing documentation after development
• Resolving stale or conflicting agent rule files
• Preparing a handoff to another person or a fresh session

Investigate crashes, out-of-memory errors, and device initialization failures on GKE TPU v6e nodes. The skill checks logs and custom metrics collection for conflicting operations, then guides temporary mitigation by disabling an identified collector.

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• Investigate vbar_control_agent crashes and memory exhaustion
• Check tpu-device-plugin metrics checksum errors
• Inspect conflicts with custom TPU metrics collectors