“agent memory”的搜索结果

Orchestrate persistent agent memory across coding sessions, repos, and tools. Load when the user asks to remember, recall context, save project memory, create a handoff, manage global memory, update memory, compact memory, audit memory, forget memory, continue from prior sessions, or before commit/push/git operations that checkpoint project state.

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Coordinate a team of AI agents by setting direction, assigning responsibilities, allocating resources, and tracking progress. Support hierarchical decisions, group consensus, and emergency control while recording shared state and preparing handovers to keep work moving.

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• Assigning research, implementation, and memory roles to agents
• Adjusting resource allocation and task priorities
• Tracking shared progress and agent responses

Preserve project decisions and work progress across sessions, and share information between agents. Organize stored knowledge into separate spaces, retrieve relevant records, and manage expiration, compression, and cleanup of saved data.

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• Resume work from a previous session
• Look up earlier architecture decisions
• Share task progress between agents

Coordinate decisions across multiple AI agents by synchronizing shared memory, combining knowledge, and weighing input by expertise. The skill also guides workload distribution, conflict handling, and recording decisions with their rationale.

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• Combine agent opinions into a shared decision
• Synchronize knowledge across agents
• Redistribute work when an agent is overloaded

Measure performance in the agentic-flow worker system, including trigger detection, agent selection, caching, and concurrent operations. Run a full suite or individual benchmarks to compare response times and resource usage against configured targets.

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• Checking overall worker system performance
• Measuring trigger detection and agent selection speed
• Examining cache behavior and memory usage

Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

Guides agents in choosing Cherry Studio tools for research, document conversion, memory, notifications, and other tasks. It explains when to use app tools or local runtimes, with checks for availability, configuration, and approval.

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• Choose the right Cherry Studio tool for a task
• Read or convert local Office and PDF files
• Set up scheduled tasks and notifications

Plan memory use and monitor long training jobs on NVIDIA DGX Spark. It guides troubleshooting for out-of-memory failures, tracks temperature and power alongside training logs, and helps assess competition between training and inference workloads.

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• Estimating available memory before training
• Troubleshooting out-of-memory failures
• Investigating slowdowns during long training jobs

Build systems that use Pinecone to find documents for AI answers or retain agent memories across sessions. Guides index setup, data loading, meaning-based and keyword search, memory separation, and end-to-end checks.

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• Build an AI assistant that answers from documents
• Keep agent facts and decisions across sessions
• Combine meaning-based search with exact terminology

Consolidate established lessons from domain memory files into reusable agent instructions. Review proposed additions before merging, preserve details while reducing duplication, and remove merged entries from the memory file afterward.

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• Turn accumulated lessons into reusable instructions
• Consolidate overlapping guidance
• Maintain project-specific instructions

Audit instructions and memory files loaded on every agent turn. Identify repeated rules, contradictions, outdated content, and material better moved into skills, then receive evidence-backed recommendations ranked by estimated token savings and change risk.

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• Reduce the size of instructions loaded on every turn
• Find conflicting rules across instruction and memory files
• Identify workflow instructions to move into separate skills

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

Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

Install or remove memU long-term memory for the agent in use. The skill selects the host-specific guide, configures scheduled memory recording and retrieval before answers, and verifies the setup with local or cloud storage.

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• Adding long-term memory to an AI agent
• Setting up memory stored on the current device
• Connecting an agent to MemU Cloud

Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.

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• Build a chatbot that remembers conversation history
• Create document question answering with source references
• Build an agent that uses custom tools

Save established decisions, project facts, failed approaches, and system architecture to Atlas shared memory. The skill checks existing entries to avoid duplicates and creates self-contained records that other agents and future sessions can use.

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• Carry decisions and their reasons into future sessions
• Share project facts and conventions with other agents
• Record failed approaches to avoid repeating them

Use when adding Memory Events, Memory Tickets, memory-map.json, vault-references.json, PM Soul memory ownership, or Memory Curator routing to an agent repo.

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Use when designing a new multi-agent team, visible agents folder, role boundaries, handoff flow, PM Soul, Memory Curator, Policy Gate, or evaluation role. Use for agent-team repo creation even when the user only says they want a meta-agent or agent operating system.

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Work with Letta agent memory: list agents, read core-memory blocks, list or add archival passages, create blocks, message an agent to record memory. Trigger phrases: letta, agent memory, core memory.

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Write the code for a CopilotKit Channels agent with the Channels SDK (@copilotkit/channels) — a platform-agnostic engine that puts one AG-UI agent into Slack, Microsoft Teams, Discord, Telegram, and WhatsApp with native interactive UI. It's the engine behind OpenTag, the open-source alternative to Claude in Slack. Use this whenever the user wants to create, scaffold, or extend a Channels agent; wire an agent into Slack/Teams/Discord; add a tool, slash command, button, select, modal, or human-in-the-loop confirmation to a chat agent; or asks about createChannel, defineChannelTool, defineChannelComponent, thread.runAgent, awaitChoice, or the channels-ui JSX components. Trigger even when the user says "Slack bot", "Discord bot", or "chat agent" without naming the SDK, as long as the context is CopilotKit / Channels. The Channels API is new and easy to get wrong from memory — always ground code in this skill.

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AGENTS.md / CLAUDE.md 最佳实践顾问。用于用户询问 agents markdown、AGENTS.md、CLAUDE.md、Claude Code memory、AI coding agent 指令文件的格式、结构、最佳实践；也用于审查、诊断、重写、优化或从零创建 AGENTS.md、CLAUDE.md、CLAUDE.local.md、.claude/rules 等 agent 指令文件。不适用于通用 README 写作，除非目标是给 AI coding agent 提供项目上下文。

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Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.

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Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.

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