“agent memory”的搜索结果

在Claude Code中设计多名专业代理协作的工作流程，将进度、决策和日志保存到文件，以便跨会话继续工作，并把重复任务整理为可复用命令和定期执行计划。

• 协调开发、写作和调研分工
• 跨会话保留项目背景
• 定期执行晨间状态检查

帮助将智能体的历史对话和资料存入Pinecone，检索相关内容并用于回答，构建跨会话长期记忆，按用户管理记忆，并试作基于语义的搜索。

• 构建跨会话保留的记忆
• 将检索到的信息用于回答
• 按用户分别管理记忆

Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.

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• Run API, interface, and test work in parallel
• Build a design-to-review workflow with ordered stages
• Share specifications and state across agents

Initialize a group of collaborating AI agents by choosing how they connect, allocating resources, and setting up communication. Establish shared-memory rules so agents record progress, share outputs, check dependencies, and signal completion.

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• Setting up an agent group for REST API development
• Configuring eight agents in a hierarchy for feature development
• Preparing a mesh of agents for distributed code analysis

Store useful patterns and solutions in AgentDB, then find them through semantic search, which matches meaning rather than exact wording. Use saved knowledge before starting work and record successful approaches and new lessons afterward.

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• Saving successful approaches for reuse
• Finding solutions from past work
• Searching stored knowledge by meaning

Set up and operate Ruflo to coordinate multiple AI coding agents. This skill guides project initialization, shared memory, task assignment, health checks, and plugin discovery for work that needs coordinated or parallel execution.

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• Initializing a multi-agent development environment
• Coordinating parallel work across agents
• Keeping searchable memory across sessions

Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.

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• Save architecture decisions for later reference
• Search previously recorded implementation patterns
• Retrieve design notes by key

Write and review Rust, C++, and C code with safer memory and resource management. Covers ownership, automatic cleanup tied to scope, and smart pointers to help prevent leaks, double frees, and access to freed memory.

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• Design safer memory management in systems code
• Review cleanup of files, sockets, and memory
• Investigate memory leaks and access to freed memory

Extend a NanoClaw agent's own environment with packages, external tools, or specialist agents. The skill chooses the workflow for each change, edits memory and standing instructions, and delegates source changes to a builder for review.

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• Adding an external tool for reading RSS feeds
• Installing persistent system or global npm packages
• Updating memory or standing behavior instructions

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

Review an agent's saved context and unread coordination messages at the start of a task. Summarize relevant assignments, move handled messages aside, and prompt the agent to preserve lasting decisions and outcomes before finishing.

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• Resume work using context saved in a previous session
• Check new messages and assignments for an agent
• Preserve decisions and outcomes for the next session

Review an agent’s saved memory alongside project and personal instructions. Receive proposed destinations for useful entries and a cleanup report covering duplicates, outdated facts, conflicts, and unclear items, with approval required before changes.

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• Turning saved memory into shared project instructions
• Separating personal instructions from project conventions
• Finding duplicate or outdated memory

Review the file-based memory store via the Agent Monitor Config Explorer API: the user and project CLAUDE.md plus per-project auto-memory files under ~/.claude/projects/<slug>/memory/*.md. Groups by project, shows the index (MEMORY.md) vs per-fact files, and flags stale or oversized facts. Reads /api/cc-config/memory and /api/cc-config/file?path=. Use when curating agent memory.

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Agent Memory Systems workflow skill. Use this skill when the user needs "Memory is the cornerstone of intelligent agents. Without it, every and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Use when modifying agentic contextual building blocks like skills (SKILL.md), slash commands, agents, hooks, plugins, marketplaces, and instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, QWEN.md, Cursor, Copilot, Cline, or Kiro rules), tool configuration such as an OpenCode `opencode.json`, Muse Code hooks (`.muse/hooks.json`), a Grok Build `.grok/` project layer or a Google Antigravity customization root (`.agents/`, `.agent/`, `_agents/`, `_agent/` — hooks, MCP config, rules, registries and plugins), and committed project memory (`.agents/memory/`). Run skillsaw on the files you touched, apply autofixes, resolve remaining violations with `skillsaw explain` guidance, and re-lint until clean before considering the work complete.

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Design Agentforce conversations that span multiple turns without losing context: session variable scoping, conversation memory, clarifying-question patterns, topic-to-topic (now subagent) handoff, and the right abstractions for accumulating state across turns. NOT for deciding the topic boundaries themselves or out-of-scope behavior — use agentforce/agent-topic-design. NOT for single-turn agent actions and their input/output contracts — use agentforce/agent-actions.

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Use ruLake as the agent's working memory. Auto-invoke /rulake-memory:memory-recall when the agent needs to look up something it might already know; auto-invoke /rulake-memory:memory-remember after the agent learns or decides something worth pinning. Closes the self-learning loop without requiring the agent author to wire it explicitly.

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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