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A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
This skill should be used when the user asks to "fine-tune on books", "create SFT dataset", "train style model", "extract ePub text", or mentions style transfer, LoRA training, book segmentation, or author voice replication.
This skill should be used when the user asks to "write a post", "check my voice", "look up contact", "prepare for meeting", "weekly review", "track goals", or mentions personal brand, content creation, network management, or voice consistency.
Production-grade techniques for evaluating LLM outputs using LLMs as judges. Use when implementing LLM-as-judge, comparing model outputs, creating evaluation rubrics, mitigating evaluation bias, or building automated quality assessment pipelines.
Transforms external RDF context into agent mental states (beliefs, desires, intentions) using formal BDI ontology patterns. Use when modeling agent mental states, implementing BDI architecture, transforming RDF to beliefs, or building cognitive agents with neuro-symbolic AI integration.
Strategies for compressing context when agent sessions exceed limits. Use when compressing context, summarizing conversation history, implementing compaction, reducing token usage, or optimizing tokens-per-task for long-running agent sessions.
Predictable degradation patterns as context length increases, including lost-in-middle phenomena and context poisoning. Use when diagnosing context problems, fixing lost-in-middle issues, debugging agent failures, or understanding attention patterns and context confusion.
| name | agent-skills-context-engineering |
| description | A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management. |
This collection provides structured guidance for building production-grade AI agent systems through effective context engineering.
Activate these skills when:
Understanding Context Fundamentals Context is not just prompt text—it is the complete state available to the language model at inference time, including system instructions, tool definitions, retrieved documents, message history, and tool outputs. Effective context engineering means understanding what information truly matters for the task at hand and curating that information for maximum signal-to-noise ratio.
Recognizing Context Degradation Language models exhibit predictable degradation patterns as context grows: the "lost-in-middle" phenomenon where information in the center of context receives less attention; U-shaped attention curves that prioritize beginning and end; context poisoning when errors compound; and context distraction when irrelevant information overwhelms relevant content.
Multi-Agent Coordination Production multi-agent systems converge on three dominant patterns: supervisor/orchestrator architectures with centralized control, peer-to-peer swarm architectures for flexible handoffs, and hierarchical structures for complex task decomposition. The critical insight is that sub-agents exist primarily to isolate context rather than to simulate organizational roles.
Memory System Design Memory architectures range from simple scratchpads to sophisticated temporal knowledge graphs. Vector RAG provides semantic retrieval but loses relationship information. Knowledge graphs preserve structure but require more engineering investment. The file-system-as-memory pattern enables just-in-time context loading without stuffing context windows.
Tool Design Principles Tools are contracts between deterministic systems and non-deterministic agents. Effective tool design follows the consolidation principle (prefer single comprehensive tools over multiple narrow ones), returns contextual information in errors, supports response format options for token efficiency, and uses clear namespacing.
Context Compression When agent sessions exhaust memory, compression becomes mandatory. The correct optimization target is tokens-per-task, not tokens-per-request. Structured summarization with explicit sections for files, decisions, and next steps preserves more useful information than aggressive compression. Artifact trail integrity remains the weakest dimension across all compression methods.
Context Optimization Techniques include compaction (summarizing context near limits), observation masking (replacing verbose tool outputs with references), prefix caching (reusing KV blocks across requests), and strategic context partitioning (splitting work across sub-agents with isolated contexts).
Evaluation Frameworks Production agent evaluation requires multi-dimensional rubrics covering factual accuracy, completeness, tool efficiency, and process quality. Effective patterns include LLM-as-judge for scalability, human evaluation for edge cases, and end-state evaluation for agents that mutate persistent state.
Project Development Effective LLM project development begins with task-model fit analysis: validating through manual prototyping that a task is well-suited for LLM processing before building automation. Production pipelines follow staged, idempotent architectures (acquire, prepare, process, parse, render) with file system state management for debugging and caching. Structured output design with explicit format specifications enables reliable parsing. Start with minimal architecture and add complexity only when proven necessary.
The collection is organized around three core themes. First, context fundamentals establish what context is, how attention mechanisms work, and why context quality matters more than quantity. Second, architectural patterns cover the structures and coordination mechanisms that enable effective agent systems. Third, operational excellence addresses the ongoing work of optimizing and evaluating production systems.
Each skill can be used independently or in combination. Start with fundamentals to establish context management mental models. Branch into architectural patterns based on your system requirements. Reference operational skills when optimizing production systems.
The skills are platform-agnostic and work with Claude Code, Cursor, or any agent framework that supports custom instructions or skill-like constructs.
This collection integrates with itself—skills reference each other and build on shared concepts. The fundamentals skill provides context for all other skills. Architectural skills (multi-agent, memory, tools) can be combined for complex systems. Operational skills (optimization, evaluation) apply to any system built using the foundational and architectural skills.
Internal skills in this collection:
External resources on context engineering:
Created: 2025-12-20 Last Updated: 2025-12-25 Author: Agent Skills for Context Engineering Contributors Version: 1.2.0
