Agent-Skills-for-Context-Engineering-compliant

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 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.

Complete state available to a language model at inference time, including system instructions, tool definitions, retrieved documents, and message history. Use when understanding context, explaining context windows, designing agent architecture, or learning progressive disclosure and context budgeting.

Extends effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. Use when optimizing context, reducing token costs, implementing KV-cache optimization, or extending effective context capacity.

Evaluation methods for agent systems that account for non-determinism and multiple valid paths. Use when evaluating agent performance, building test frameworks, measuring quality, creating evaluation rubrics, or implementing LLM-as-judge with multi-dimensional assessment.

Persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Use when implementing agent memory, persisting state across sessions, building knowledge graphs, or tracking entities with temporal validity.

Distributes work across multiple language model instances with isolated context windows. Use when designing multi-agent systems, implementing supervisor or swarm patterns, coordinating multiple agents, or enabling context isolation through parallel agent execution.

Covers principles for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development. Use when starting LLM projects, designing batch pipelines, evaluating task-model fit, or planning agent system architecture.

Contracts between deterministic systems and non-deterministic agents. Use when designing agent tools, creating tool descriptions, reducing tool complexity, implementing MCP tools, or applying tool consolidation and architectural reduction principles.

Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.