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README.md

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+---
+license: apache-2.0
+library_name: sklearn
+pipeline_tag: text-classification
+tags:
+- ai-systems
+- agents
+- agent-tasks
+- routing
+- automation
+- tool-use
+- multi-agent
+---
+
+# Agent Task Classifier
+
+**Agent Task Classifier** is a lightweight reference model that maps short agent-task descriptions to practical operational categories.
+
+It is published under the **ai-systems** handle as a transparent example for agent routing, task decomposition, and AI-system prototyping.
+
+## Task Labels
+
+- `browser`
+- `coding`
+- `communication`
+- `control`
+- `data-analysis`
+- `memory`
+- `multi-agent`
+- `planning`
+- `research`
+- `retrieval`
+- `tool-use`
+- `verification`
+- `workflow-automation`
+
+## Examples
+
+Input:
+
+```text
+Search the web for recent papers and summarize the findings.
+```
+
+Expected category:
+
+```text
+research
+```
+
+Input:
+
+```text
+Fix a bug in a Python project and run the tests.
+```
+
+Expected category:
+
+```text
+coding
+```
+
+Input:
+
+```text
+Require human approval before sending the final message.
+```
+
+Expected category:
+
+```text
+control
+```
+
+## Usage
+
+```python
+from joblib import load
+
+classifier = load("agent-task-classifier.joblib")
+
+text = "Delegate subtasks to specialized agents and merge their results"
+prediction = classifier.predict([text])[0]
+
+print(prediction)
+```
+
+## Model Architecture
+
+The model uses:
+
+- TF-IDF text features
+- unigram and bigram features
+- logistic regression classification
+
+It is intentionally small and easy to inspect.
+
+## Training Data
+
+The reference model was trained on a small curated set of agent-task descriptions covering:
+
+- research
+- coding
+- browser interaction
+- data analysis
+- retrieval
+- communication
+- workflow automation
+- planning
+- verification
+- memory
+- tool use
+- multi-agent coordination
+- control and approval
+
+## Intended Use
+
+Suitable for:
+
+- agent-routing demos
+- task taxonomies
+- educational examples
+- workflow prototypes
+- lightweight agent orchestration experiments
+- AI-system documentation
+
+## Limitations
+
+This is a **reference model**, not a production-grade task router.
+
+The training set is intentionally small, so ambiguous or out-of-domain requests may be misclassified.
+
+It should not be used to make medical, legal, financial, safety-critical, or other high-impact decisions.
+
+## Related Dataset
+
+`ai-systems/ai-system-patterns`
+
+## Related Models
+
+- `ai-systems/system-router`
+- `ai-systems/capability-classifier`
+
+## License
+
+Apache-2.0

agent-task-classifier.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f828a16fb66e6664185605511ead68765ad988ea231333c1214d4a8f56d91583
+size 90164

classifier_config.json

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+{
+  "model_type": "sklearn-text-classifier",
+  "name": "agent-task-classifier",
+  "owner": "ai-systems",
+  "task": "text-classification",
+  "labels": [
+    "browser",
+    "coding",
+    "communication",
+    "control",
+    "data-analysis",
+    "memory",
+    "multi-agent",
+    "planning",
+    "research",
+    "retrieval",
+    "tool-use",
+    "verification",
+    "workflow-automation"
+  ],
+  "training_examples": 65,
+  "intended_use": "Classify short agent task descriptions into operational task categories.",
+  "limitations": "Small curated reference model; not intended for production-critical routing."
+}

example_usage.py

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+from joblib import load
+
+classifier = load("agent-task-classifier.joblib")
+
+examples = [
+    "Search the web for relevant sources and summarize them",
+    "Fix a failing test in a Python repository",
+    "Require human approval before sending the message"
+]
+
+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

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+[
+  "browser",
+  "coding",
+  "communication",
+  "control",
+  "data-analysis",
+  "memory",
+  "multi-agent",
+  "planning",
+  "research",
+  "retrieval",
+  "tool-use",
+  "verification",
+  "workflow-automation"
+]

requirements.txt

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+scikit-learn>=1.4
+joblib>=1.3