AI Agent Skills: Dynamic Context and Memory

The biggest limitation of AI Agents today is not “reasoning” (they reason quite well), but Memory and Context.

A “Skill” (like checking your GitHub issues or reading your code) is only useful if the agent knows when and how to use it. This requires giving the agent the right context at the right time.

🧠 The Context Problem

Imagine you tell an AI: “Fix the bug in the login screen.”

Without context, the AI might hallucinate:

• “Which login screen?”
• “What bug?”
• “Are you using XML or Compose?”

The standard solution is RAG (Retrieval-Augmented Generation), where we fetch relevant code chunks. But for Skills, we need something smarter. We need Dynamic Context.

🔄 Dynamic Context Injection

Instead of stuffing everything into the prompt (Context Window overflow!), we want the Skill itself to declare what context it needs.

Example: The “Android Expert” Skill

If I activate the android-expert skill, it shouldn’t just be a prompt saying “You are an Android expert.” It should dynamically inject:

1. Project Structure: tree -L 2 src/ (output of command).
2. Dependencies: Content of libs.versions.toml.
3. Recent Changes: git diff HEAD~1.

By injecting this context only when the skill is active, we keep the main prompt clean and the agent focused.

🛠️ Implementing Dynamic Context

In a tool like Cline or Cursor, we can use @ symbols to reference context.

• @Codebase: Indexes your files with embeddings.
• @File: Reads a specific file.

But for custom agents (e.g., using LangChain or simple API calls), we can structure our skills like this:

interface Skill {
    val name: String
    val description: String

    // The "magic" part: dynamic context
    suspend fun getContext(query: String): String
}

class GitSkill : Skill {
    override val name = "Git Expert"
    override val description = "Handles git operations and context."

    override suspend fun getContext(query: String): String {
        // Only inject diff if the user asks about "changes" or "commit"
        if (query.contains("change") || query.contains("commit")) {
            return "Current diff:\n" + runCommand("git diff --staged")
        }
        return ""
    }
}

🧩 Context Routing

An advanced pattern is Context Routing. An “Orchestrator” LLM decides which skills (and thus which context) are relevant for the query.

1. User: “Why is the build failing?”
2. Orchestrator: “I need the BuildSkill and LogAnalysisSkill.”
3. BuildSkill Context: Loads build.gradle.kts.
4. LogAnalysisSkill Context: Loads the last 50 lines of build.log.
5. Agent: “The build failed because of dependency conflict in build.gradle.kts (line 45)…”

🏁 Key Takeaway

Static prompts are dead. To build truly intelligent agents, we must move to Dynamic Context.

• Don’t dump your whole wiki into the prompt.
• Let each Skill define its own “mini-context”.
• Inject that context only when relevant.

This approach saves tokens, reduces hallucinations, and makes your agents feel much smarter.