A field guide to model control
Where can you change an AI?
Skills steer a model from above—through context, instructions and tools. Open weights let builders reach below the interface—into generation, parameters and, sometimes, training.
“Lower” means closer to the machinery—not automatically better.
The short version
A skill changes the model’s working conditions.
Open weights let you change more of the machinery.
Picture an excellent chef in someone else’s kitchen. You can hand them a recipe, point out the ingredients and let them use the oven. That is the role of a skill: it gives a capable model a method, relevant material and approved ways to act.
If you own the kitchen, you can also recalibrate the oven, replace the equipment or rebuild the room. That is the extra control that comes with open weights and a self-managed model stack.
Higher-level change
Skills change how the system works on a task
They add instructions, examples, scripts, references and access to tools. They are quick to edit and can work with both open and closed models.
weights stay fixedLower-level change
Open weights expand how the model itself can be run
Builders can host the parameters, inspect intermediate scores, add decoding rules, fine-tune behaviour and choose the infrastructure around the model.
more of the stack is yoursFirst, fix the vocabulary
Three labels that are often collapsed into two.
The everyday phrase “open model” hides an important difference between having the weights and having the whole recipe used to produce them.
Closed model
You send input to a service and receive output. The provider owns the model runtime and chooses which controls the API exposes.
- Fast to adopt
- Little infrastructure to run
- Internals remain behind the service boundary
Open weights
The learned parameters can be downloaded and run. You control the inference stack, within the licence and the limits of the material released.
- Host on your infrastructure
- Control generation code
- Not necessarily open source
Open-source AI
A stronger claim: freedoms to use, study, modify and share, plus access to the preferred form for modification—not just a checkpoint.
- Weights and model code
- Training and data information
- Terms that preserve the relevant freedoms
The distinction follows the Open Source AI Definition 1.0. Source 1
01 Work above the model
A skill is a reusable way of working.
It packages the know-how an agent needs for a particular kind of task, then loads that know-how when it is relevant.
A small package, loaded progressively
The model does not need every manual in its head all the time.
An agent can first see a short catalogue of skill names and descriptions. When the task matches, it loads the relevant instructions and pulls in deeper references or scripts only as needed.
Discover
The task matches a skill’s description.
Load
The method and relevant references enter context.
Work
The model follows the method and calls approved tools.
Act
Files, systems or drafts change within permissions.
The folder anatomy and progressive-loading pattern follow the Agent Skills specification. Source 2
Move the point of control
Same system. Different intervention.
Select a layer to see what a builder can change there. The highlighted line is the intervention point—not a claim that every product exposes the same controls.
Application layer · works with open or closed models
Skill: change the context and available actions
Give the model a procedure, supporting material and approved tools. The model reasons with that temporary context, then the application carries out permitted actions.
- Good fit
- Procedures, current knowledge, integrations and repeatable work.
- Boundary
- The model weights remain unchanged.
Load refund-review → check policy → draft decision → save note
Open weights mostly expand the lower half of this picture. Skills live in the top half—and remain useful no matter which model sits underneath.
02 Work closer to the model
Open weights move the boundary down.
When you can run the model yourself, the score for every possible next token is available inside your own process. That creates new intervention points.
Interactive probability lab
The model proposes. You can move the odds.
Every possible next token receives a score—a logit. Softmax turns those scores into probabilities. Change the intervention and watch the same model arrive at a different distribution.
No extra context and no runtime intervention. The model’s learned association supplies the starting scores.
A concrete example of runtime-level control is a logits processor in Transformers, which receives token scores and returns processed scores. Source 3
Choose the intervention, not the ideology
Use the highest layer that reliably solves the problem.
Higher layers are faster to change and easier to inspect. Lower layers offer deeper control, but usually demand more data, evaluation, infrastructure and care.
Does this knowledge change often?
Keep changing policies and procedures in context, references or tools—not baked into weights.
Must the output obey a hard rule?
Use validation or constrained decoding. A beautifully written instruction is still an instruction.
Do you need to own the boundary?
Open weights matter when deployment, privacy, latency or deep inference control must be yours.
The idea to keep
Skills are editable working methods.
Open weights are editable machinery.
They are not rivals. A strong AI system often uses both: a controllable model underneath, and clear, auditable skills above it that connect intelligence to real work.