Capability Classifier
Capability Classifier is a lightweight reference model that maps short AI task descriptions to practical capability categories.
It is published under the ai-systems handle as a transparent demonstration model for AI-system analysis.
Capability Labels
adaptationagentscodingmemorymultimodalplanningreasoningreliabilitysciencetool-useverificationworld-modeling
Examples
Input:
Break a complex objective into subtasks and replan after failure.
Expected category:
planning
Input:
Predict how the environment will change before acting.
Expected category:
world-modeling
Input:
Run tests before accepting generated code.
Expected category:
verification
Usage
from joblib import load
classifier = load("capability-classifier.joblib")
text = "Use a browser and API to complete the task"
prediction = classifier.predict([text])[0]
print(prediction)
Model Architecture
The reference model uses:
- TF-IDF text features
- unigram and bigram features
- logistic regression classification
The model is intentionally small so the classification approach remains easy to inspect and reproduce.
Training Data
The model was trained on a small curated set of short AI-task descriptions covering capability areas such as:
- reasoning
- coding
- planning
- memory
- tool use
- agents
- multimodal understanding
- world modeling
- verification
- reliability
- adaptation
- science
Intended Use
Suitable for:
- capability explorers
- educational tools
- lightweight taxonomy experiments
- AI-system documentation
- prototyping
- simple routing demos
Limitations
This is a reference model, not a benchmark and not a production-grade classifier.
It was trained on a small curated dataset. Predictions outside the covered task descriptions may be unreliable.
The model should not be used for medical, legal, financial, safety-critical, or other high-impact decisions.
Related Dataset
ai-systems/ai-system-patterns
Related Model
ai-systems/system-router
License
Apache-2.0
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