File size: 2,407 Bytes
c7fe0d5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
---
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