Latent Briefing
技能 活跃This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.
To provide a detailed explanation and implementation guidance for using Latent Briefing to optimize memory sharing and reduce token costs in hierarchical multi-agent systems.
功能
- Explains representation-level memory sharing via KV cache compaction.
- Details the adaptation of Attention Matching for multi-agent inference.
- Discusses three inference-time modifications: task-guided queries, shared masks, and MAD thresholding.
- Outlines infrastructure preconditions and decision frameworks for choosing memory sharing mechanisms.
使用场景
- Designing orchestrator-worker systems needing to share prior state efficiently.
- Evaluating KV cache compaction as an alternative to text summarization for cross-agent state transfer.
- Debugging token explosion in recursive or hierarchical agent graphs.
- Implementing or studying task-conditioned selective retention in LLM inference.
非目标
- Replacing text-based summarization or RAG where those methods are sufficient or preferable.
- Providing a deployable tool; this skill is for conceptual understanding and implementation guidance.
- Working with API-only stacks where KV state is inaccessible.
Practical Utility
- info:Production readinessThe skill outlines a detailed technical approach and use cases, but its readiness for production hinges on the user's ability to control the worker inference runtime for KV state manipulation.
Maintenance
- warning:Commit recencyThe last commit was on 2026-04-14, over 3 months ago, suggesting potential unmaintained status.
Trust
- warning:Issues Attention6 issues opened and 2 closed in the last 90 days, indicating a low closure rate (33%) and potentially slow maintainer response.
安装
请先添加 Marketplace
/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering/plugin install Agent-Skills-for-Context-Engineering@context-engineering-marketplace质量评分
类似扩展
Context Optimization
100This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.
Stream Chain
99Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
Context Compression
100This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
Init
100创建或优化存储库的 AGENTS.md 文件，提供最少、高信号的说明，涵盖代理无法从代码库推断的不可发现的编码约定、工具怪癖、工作流偏好和项目特定规则。在为新存储库设置代理说明或 Claude 配置时，当现有的 AGENTS.md 文件过长、通用或过时，当代理反复犯可避免的错误，或当存储库工作流发生变化且需要修剪代理配置时使用。应用可发现性过滤器—省略 Claude 可从 README、代码、配置或目录结构中学到的任何内容—并应用质量门，以验证每行是否仍然准确且具有操作意义。
External Context
100Invoke parallel document-specialist agents for external web searches and documentation lookup
Swarm Orchestration
100Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.