By DYAI2025
BDI mental state modeling and cognitive architecture patterns for building rational agents with formal belief-desire-intention representations
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.
This 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.
This skill should be used when the user asks to "diagnose context problems", "fix lost-in-middle issues", "debug agent failures", "understand context poisoning", or mentions context degradation, attention patterns, context clash, context confusion, or agent performance degradation. Provides patterns for recognizing and mitigating context failures.
This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent systems.
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A comprehensive, open collection of Agent Skills focused on context engineering principles for building production-grade AI agent systems. These skills teach the art and science of curating context to maximize agent effectiveness across any agent platform.
Context engineering is the discipline of managing the language model's context window. Unlike prompt engineering, which focuses on crafting effective instructions, context engineering addresses the holistic curation of all information that enters the model's limited attention budget: system prompts, tool definitions, retrieved documents, message history, and tool outputs.
The fundamental challenge is that context windows are constrained not by raw token capacity but by attention mechanics. As context length increases, models exhibit predictable degradation patterns: the "lost-in-the-middle" phenomenon, U-shaped attention curves, and attention scarcity. Effective context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes.
These skills establish the foundational understanding required for all subsequent context engineering work.
| Skill | Description |
|---|---|
| context-fundamentals | Understand what context is, why it matters, and the anatomy of context in agent systems |
| context-degradation | Recognize patterns of context failure: lost-in-middle, poisoning, distraction, and clash |
| context-compression | Design and evaluate compression strategies for long-running sessions |
These skills cover the patterns and structures for building effective agent systems.
| Skill | Description |
|---|---|
| multi-agent-patterns | Master orchestrator, peer-to-peer, and hierarchical multi-agent architectures |
| memory-systems | Design short-term, long-term, and graph-based memory architectures |
| tool-design | Build tools that agents can use effectively |
| filesystem-context | Use filesystems for dynamic context discovery, tool output offloading, and plan persistence |
| hosted-agents | NEW Build background coding agents with sandboxed VMs, pre-built images, multiplayer support, and multi-client interfaces |
These skills address the ongoing operation and optimization of agent systems.
| Skill | Description |
|---|---|
| context-optimization | Apply compaction, masking, and caching strategies |
| evaluation | Build evaluation frameworks for agent systems |
| advanced-evaluation | Master LLM-as-a-Judge techniques: direct scoring, pairwise comparison, rubric generation, and bias mitigation |
These skills cover the meta-level practices for building LLM-powered projects.
| Skill | Description |
|---|---|
| project-development | Design and build LLM projects from ideation through deployment, including task-model fit analysis, pipeline architecture, and structured output design |
These skills cover formal cognitive modeling for rational agent systems.
| Skill | Description |
|---|---|
| bdi-mental-states | NEW Transform external RDF context into agent mental states (beliefs, desires, intentions) using formal BDI ontology patterns for deliberative reasoning and explainability |
Each skill is structured for efficient context use. At startup, agents load only skill names and descriptions. Full content loads only when a skill is activated for relevant tasks.
These skills focus on transferable principles rather than vendor-specific implementations. The patterns work across Claude Code, Cursor, and any agent platform that supports skills or allows custom instructions.
Scripts and examples demonstrate concepts using Python pseudocode that works across environments without requiring specific dependency installations.
This repository is a Claude Code Plugin Marketplace containing context engineering skills that Claude automatically discovers and activates based on your task context.
Step 1: Add the Marketplace
Run this command in Claude Code to register this repository as a plugin source:
/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering
Step 2: Browse and Install
npx claudepluginhub dyai2025/agent-skills-for-context-engineeringHarness-native ECC plugin for engineering teams - 67 agents, 271 skills, 92 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses
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