skill-template

The skill-template serves as the foundational architectural blueprint for developers and AI engineers building custom capabilities within the Agent Skills for Context Engineering ecosystem. It is designed to minimize cognitive load by enforcing a consistent structure for skill definition, which directly optimizes how language models process, retrieve, and execute specialized tasks. By adhering to this template, you ensure that your agent skills remain discoverable, interpretable, and maintainable across various platforms like Claude Code, Cursor, and other agentic environments. This template enforces the principle of progressive disclosure, ensuring that only necessary context is injected into the model's attention window, thereby reducing performance degradation and maximizing token efficiency.

• Standardized header metadata for versioning, authorship, and tracking update history to manage skill evolution.

• Integrated activation triggers that help the system identify when to invoke the skill, preventing hallucination or irrelevant tool execution.

• Explicit core concept definitions that focus on high-signal mental models while assuming baseline intelligence to minimize token waste.

• Modular sectioning for practical guidance, anti-patterns, and error handling, ensuring that developers document both the "what" and the "how."

• Built-in "Gotchas" section to capture non-obvious failure modes, enhancing the robustness of agentic reasoning through experiential learning.

• Cross-skill integration documentation for building complex, multi-agent orchestrations and hierarchical architectures.

• Use this template when extending the context engineering library to ensure your contribution aligns with the project's design philosophy.

• Keep your SKILL.md body under 500 lines by offloading extensive documentation to the references/ folder to keep the LLM prompt context clean and focused.

• Follow the third-person instruction style strictly to ensure compatibility with system prompt injection mechanics.

• When defining guidelines, prioritize actionable, verifiable criteria that an agent can self-evaluate against.

• Always include clear input/output pairs in the examples section to provide the model with a strong few-shot learning anchor for task execution.