Build Model-Agnostic AI Agent Skills That Survive Any Harness Swap
Learn how to build portable AI agent skills and context files that work across Claude Code, Codex, and other harnesses without vendor lock-in.
What does it mean for an AI skill to be “model-agnostic”?
A model-agnostic skill is a reusable instruction file, context document, or plugin that tells an AI agent how to do something, written in a way that doesn’t depend on which underlying model or harness executes it. Instead of writing a skill that only works inside Claude Code’s specific folder structure or Codex’s particular tool-calling conventions, you write it so the instructions, context, and expected outputs translate cleanly no matter which harness reads them. The goal is simple: your accumulated expertise (the “IP” you’ve built into your prompts, workflows, and reference files) keeps working even after you switch which company’s model is doing the thinking.
TL;DR
- The harness matters more than the model because tools like Claude Code and Codex wrap a language model with the ability to read files, edit code, run bash commands, and loop on results, and none of that comes from the model itself.
- Skills, context files, and plugins are the layer worth protecting since they encode your actual expertise, and that layer can be ported across different AI harnesses instead of being tied to one vendor.
- Loyalty should go to your harness and assets, not to a provider, which means treating Claude, Codex, Gemini, or a local open-source model as interchangeable “brains” you plug into the same workflow.
- Skills decay fast, to the point where practitioners in the space have suggested wiping and rewriting them roughly every six months as models and harnesses evolve.
- Local open-source models can already handle a large share of routine tasks, with more capable hosted models reserved for harder planning or “genius level” work.
- Cross-testing a skill against multiple harnesses, converting a skill built for one tool so it also works in another, is a practical way to confirm it’s actually portable rather than accidentally hard-coded to one system.
- Different harnesses have different personalities in practice, with one behaving more like a big-picture planner and another behaving more like a literal instruction-follower, which is a reason to use more than one in the same workflow.
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Why does the harness matter more than the model?
Every large language model is, on its own, just a brain in a jar. It can take text in and produce text out, but it has no hands. It can’t open a file, run a command, or start a local web server unless something outside the model gives it those abilities. That “something” is the harness: the software layer that provides the model with reading, writing and editing files, executing shell commands (bash), and looping on the results of its own actions.
This is easiest to see with a local, harness-free setup. Run an open-source model in a tool like LM Studio and ask it to build a landing page and host it locally, and it can write the HTML, but it cannot actually spin up a server on your machine. It has no limb for that. Give the identical request to a harnessed tool like Claude Code or Codex, and the harness handles the missing steps automatically, because file access and command execution are built into the tool surrounding the model, not into the model’s weights.
This is also why comparing “Claude versus Codex versus Gemini” as if you’re comparing raw intelligence misses most of what’s actually happening. The agentic loop, where the agent takes an action, observes the result (success or error), and decides what to do next, depends heavily on how well the harness structures that feedback for the model. A more sophisticated harness lets even a moderately capable model act like a seasoned expert who knows exactly which tool to reach for and when. A weaker harness can make a strong model flounder.
How do skills and context files fit into this?
If the harness provides the generic capabilities (read, write, execute, loop), skills and context files are where you layer in the specific expertise: your preferred coding patterns, your documentation standards, domain knowledge about your industry, or step-by-step procedures for tasks you repeat often. These act like modular add-ons, similar to plugins, that sit on top of the harness’s out-of-the-box tools.
This layer is where the actual value accumulates over time. The underlying models are a shared commodity that every user has access to. Your skills and context files are not. They encode judgment calls, formatting preferences, and hard-won lessons about what works, which is closer to genuine intellectual property than anything baked into the model itself. The framing that captures this well: you can outsource the thinking to a model, but you can’t outsource the understanding that goes into building a good skill in the first place.
How do you keep skills portable across different harnesses?
Remy doesn't build the plumbing. It inherits it.
Other agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something.
Remy ships with all of it from MindStudio — so every cycle goes into the app you actually want.
The practical approach is to write and store skills in a harness-neutral format, then actively test them against more than one tool rather than assuming portability. One method demonstrated in practice: build a skill for one harness (say, Claude Code), then run it through a conversion step that reformats and optimizes it for a second harness (Codex), checking that both versions produce equivalent behavior. Keeping a single, centralized set of core assets that every harness can point to (rather than duplicating and forking skills per tool) means you’re not maintaining parallel, drifting copies of the same logic.
The underlying discipline is treating your skills library as the durable asset and your choice of model provider as a swappable component. That means resisting the urge to write instructions that lean on quirks specific to one tool’s syntax or behavior, and instead writing clear, explicit, self-contained instructions that any reasonably capable agent, regardless of vendor, can execute correctly.
Why do skills and agents decay so quickly?
Skills and context files aren’t “set once and forget.” Models update, harnesses add new default behaviors, and instructions that compensated for an old limitation can become redundant or even actively wrong once that limitation is fixed. This is fast-moving enough that the recommendation from people building extensively in this space has been to periodically wipe skills entirely and rebuild them rather than let them accumulate as stale patches on top of stale patches. Treating a skills library as a living document that needs regular pruning, not a permanent asset you write once, is part of what keeps a model-agnostic workflow actually functional rather than a growing pile of dead instructions.
Is running everything locally a realistic alternative to hosted models?
For a meaningful share of day-to-day tasks, yes. Open-source local models have gotten capable enough to handle routine work without needing a frontier hosted model. The practical split that’s emerging: run the bulk of repetitive, well-defined tasks on local models (assuming you have the hardware to run them), and reserve hosted, more capable models for harder problems like planning, architecture decisions, or tasks that need deeper reasoning. This isn’t an all-or-nothing choice. It’s closer to a routing decision, where the “brain” you pick depends on the difficulty of the task, and your skills and workflows stay constant underneath regardless of which brain is currently plugged in.
How should you actually divide work between different harnesses?
In practice, different harnesses tend to exhibit different working styles even when pointed at similar underlying capability. One might behave more like a big-picture collaborator that’s strong at ideation and planning but sometimes drifts from your exact instructions. Another might behave more like a literal, obedient executor that follows instructions precisely but is less inclined to push back or suggest improvements. A workflow that uses one harness to draft a plan and a second harness to critique or stress-test that plan, iterating between the two, can surface gaps that neither would catch alone. The point isn’t to declare one harness universally better. It’s to use their different tendencies deliberately, the same way you’d use a specialist and a generalist together on a real team.
Frequently Asked Questions
What’s the difference between an AI model and an AI harness?
The model is the underlying neural network that processes input and generates output. The harness is the surrounding software that gives the model the ability to read and write files, execute commands, browse the web, or run in a loop reacting to results. A model with no harness can only produce text; it can’t take real-world action.
Remy doesn't write the code. It manages the agents who do.
Remy runs the project. The specialists do the work. You work with the PM, not the implementers.
Can I use the same skill file in Claude Code and Codex?
Yes, if the skill is written in a harness-neutral way rather than relying on tool-specific quirks. It’s common to build a skill for one harness first, then convert and test it for a second harness to confirm both versions behave the same way before relying on it.
How often should I update or rewrite my AI skills?
Skills and agent instructions can go stale within months as underlying models and harnesses change. Periodically reviewing and, where necessary, fully rewriting skills rather than just patching them keeps a workflow from accumulating outdated or contradictory instructions.
Do I need a paid, hosted model for every task?
No. Local open-source models can handle a substantial share of routine tasks if you have adequate hardware, with hosted, more capable models reserved for harder reasoning or planning work.
Why does provider loyalty matter less than harness loyalty?
Models from different providers are increasingly comparable commodities that can be swapped in and out. Your skills, context files, and workflows are the accumulated expertise that took real time to build, so protecting and future-proofing that layer matters more than which company’s model happens to be running underneath it today.
