An agent's procedural memory (its skills) is analogous to a human's Standard Operating Procedures (SOPs). Storing these "SOPs"—such as in markdown files—inside a database allows them to be selectively retrieved, enabling the agent to scale its capabilities.

To improve an agent's performance on a specific task like prompting the VO3 video model, create a dedicated 'onboarding document'. Use a tool like Perplexity to gather best practices from experts, compile them into a doc, and instruct the agent to reference it. This shortcuts the learning curve and embeds expertise.

AI agents need a multi-faceted memory architecture inspired by human cognition. This includes episodic (time-stamped events), semantic (world knowledge), procedural (workflows and skills), and working memory (immediate context window).

With AI agents, the key to great results is not about crafting complex prompts. Instead, it's about 'context engineering'—loading your agent with rich information via files like 'agents.md'. This allows simple commands like 'write a cold email' to yield highly customized and effective outputs.

"Skills" are markdown files that provide an AI agent with an expert-level instruction manual for a specific task. By encoding best practices, do's/don'ts, and references into a skill, you create a persistent, reusable asset that elevates the AI's performance almost instantly.

The "Agent Skills" format was created by Anthropic to solve a key performance bottleneck. As capabilities were added, system prompts became too large, degrading speed and reliability. Skills use "progressive disclosure," loading only relevant information as needed, which preserves the context window for the task at hand.

Skills and MCP are not competitors but complementary layers in an agent's architecture. Skills provide vertical, domain-specific knowledge (e.g., how to behave as an accountant), while MCP provides the horizontal communication layer to connect the agent to external tools and data sources.

Instead of loading large context files on every turn, use "skills." The agent only sees a skill's name and description initially, loading the full instructions only when needed. This method, called progressive disclosure, drastically saves tokens and improves performance.

A cost-effective AI strategy involves using a powerful, expensive model once to solve a complex task, then using a system like M0 to distill that solution into reusable "experience" and "skill" records. Cheaper models can then leverage this pre-packaged knowledge to execute the same task with higher success rates and significantly lower token costs.