Upload 6 files

README.md

@@ -0,0 +1,145 @@
+---
+license: apache-2.0
+library_name: sklearn
+pipeline_tag: text-classification
+tags:
+- ai-systems
+- capabilities
+- reasoning
+- planning
+- memory
+- agents
+- world-models
+- verification
+- reliability
+---
+
+# 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
+
+- `adaptation`
+- `agents`
+- `coding`
+- `memory`
+- `multimodal`
+- `planning`
+- `reasoning`
+- `reliability`
+- `science`
+- `tool-use`
+- `verification`
+- `world-modeling`
+
+## Examples
+
+Input:
+
+```text
+Break a complex objective into subtasks and replan after failure.
+```
+
+Expected category:
+
+```text
+planning
+```
+
+Input:
+
+```text
+Predict how the environment will change before acting.
+```
+
+Expected category:
+
+```text
+world-modeling
+```
+
+Input:
+
+```text
+Run tests before accepting generated code.
+```
+
+Expected category:
+
+```text
+verification
+```
+
+## Usage
+
+```python
+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

capability-classifier.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e119d523364fdf087300eebb8816aace8660bf7ca2a2cb2e24b3fb0179287b20
+size 62108

classifier_config.json

@@ -0,0 +1,23 @@
+{
+  "model_type": "sklearn-text-classifier",
+  "name": "capability-classifier",
+  "owner": "ai-systems",
+  "task": "text-classification",
+  "labels": [
+    "adaptation",
+    "agents",
+    "coding",
+    "memory",
+    "multimodal",
+    "planning",
+    "reasoning",
+    "reliability",
+    "science",
+    "tool-use",
+    "verification",
+    "world-modeling"
+  ],
+  "training_examples": 48,
+  "intended_use": "Classify short AI task descriptions into capability categories.",
+  "limitations": "Small curated reference model; not intended for production-critical classification."
+}

example_usage.py

@@ -0,0 +1,14 @@
+from joblib import load
+
+classifier = load("capability-classifier.joblib")
+
+examples = [
+    "Break a complex objective into subtasks and replan after failure",
+    "Use a browser and API to complete the task",
+    "Predict how the environment will change before acting"
+]
+
+for text in examples:
+    label = classifier.predict([text])[0]
+    confidence = classifier.predict_proba([text])[0].max()
+    print(f"{text}\n  -> {label} ({confidence:.2f})")

labels.json

@@ -0,0 +1,14 @@
+[
+  "adaptation",
+  "agents",
+  "coding",
+  "memory",
+  "multimodal",
+  "planning",
+  "reasoning",
+  "reliability",
+  "science",
+  "tool-use",
+  "verification",
+  "world-modeling"
+]

requirements.txt

@@ -0,0 +1,2 @@
+scikit-learn>=1.4
+joblib>=1.3