An Open Source Alternative to Using Agent Skills with Claude API — Run with Any LLM

Agent Skills are becoming a standard building block in modern AI agent systems.

They promise reusable capabilities instead of fragile prompt logic, and that promise is real.

But as soon as agents start running real workflows in production, another layer quietly becomes critical: execution.
Not whether a skill can run, but who controls it, how visible it is, and what happens when things go wrong.

That's where most skills abstractions begin to break down.

What “Using Agent Skills with the Claude API” Really Means

Before we deep dive into transparent execution, it helps to clarify what using agent skills with the Claude API↗ actually means.

With the Claude API, agent skills are executed inside Claude’s own runtime. Developers declare skills in a Messages API request (via the container parameter), and Claude runs them in a managed code execution environment, returning results or file artifacts through the API.

This makes agent skills real and usable, but it also means execution happens inside a Claude-controlled environment largely opaque to the developer.

That execution model is what this post builds on.

Why Agent Skills Break Down in Production

Claude Skills API ↗is a real step forward. It introduced a model-managed execution layer for running skills, making agent skills tangible: running code, managing files, and invoking tools.

But once agents move beyond demos, especially for teams building production agents, the problem changes.

It's no longer about whether a tool can run.
It's about who controls the execution, and whether engineers can see what actually happened when things fail.

The Problem with LLM-Managed Execution

Claude Skills API follows an LLM-managed execution model.

The model proposes a skill, the platform executes it internally, and the developer receives the result.

That works well for experimentation and fast iteration.

But in production environments, execution remains a black box.

When something goes wrong, engineers see the outcome, but not the execution path that led to it.

The issue isn't skills themselves. It's the lack of transparent, observable execution boundaries.

Acontext's Approach: Developer-Owned Execution

Acontext Sandbox↗ takes a different approach, built for advanced agent builders.

It does not redefine what a skill is. It changes how skills are executed, observed, and controlled.

The model still proposes tool or skill calls, but execution is transparent and developer-owned.
Nothing runs unless your code runs it.

Skills in Acontex↗t are uploaded and managed visibly, then executed inside a secure sandbox you control.

Every run produces observable artifacts, such as logs, files, and execution traces, that engineers can inspect, debug, and replay.

This shifts agent skills from a model feature into execution infrastructure.

Any LLM + Managed Skills + Sandbox Execution

With Acontext, teams building production agents get a clean separation of concerns:

• Any LLM
  OpenAI, Claude, Gemini, or OpenRouter — skills are not tied to a single model.
• Managed agent skills
  Skills in Acontext are organized, developer-managed artifacts, not logic hidden inside prompts. Developers can upload, version, and reuse skills, including importing existing skills from Agent Skills Directory ↗or Anthropic Skills↗. Once imported, these skills are mounted and executed through the sandbox, with full visibility into their execution behavior.
• Sandboxed execution
  Every skill runs in a secure, isolated, and fully observable environment.

This combination is what makes execution predictable and auditable in real systems.

Who Acontext Sandbox Is For

Acontext Sandbox is designed for teams building agents that:

• run real workflows
• touch real data
• require debugging, audits, or replayability
• need solid control over execution behavior

If execution transparency↗ matters to you, it's not a nice-to-have. It is the foundation.

Get Started

→ Explore how Acontext enables transparent, developer-owned skill execution. https://acontext.io/product/sandbox

FAQ

• Is Acontext Sandbox an alternative to Claude Skills?

No. Acontext does not replace the concept of Claude Skills or agent skills ecosystems.
Acontext defines how agent skills are executed, observed, and controlled.

It made agent skills out-of-box for agent products: not just a code-running sandbox, but an environment with expertise.

Acontext Sandbox sits below skills ecosystems. You can import existing agent skills from Anthropic's public skills repository or agentskills.io and run them inside Acontext with explicit execution control.

• What makes this obviously different from Claude Skills API?

Claude Skills API uses an LLM-managed execution model. (https://platform.claude.com/docs/en/build-with-claude/skills-guide)

Acontext provides a developer-owned execution layer.

The difference is not in skill definition, but in execution ownership and transparency.

• Who should use Acontext Sandbox?

Teams building production agents where execution needs to be debugged, audited, replayed, or trusted over time.

For simple prompt-based chatbots or demos, it's likely unnecessary.