Guixu

name: guixu description: "Dataset discovery, valuation, and acquisition for AI training. Use when: (1) finding datasets for model training or fine-tuning, (2) evaluating dataset quality and relevance, (3) downloading datasets from Kaggle, HuggingFace, IPFS, or other sources, (4) assessing dataset licensing and provenance, (5) acquiring paid or free datasets. NOT for: generic web search (use browser), local file operations (use file tools), or data labeling/annotation tasks." metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["guixu"] }, "install": [ { "id": "brew", "kind": "brew", "formula": "guixu", "bins": ["guixu"], "label": "Install Guixu CLI (brew)", }, ], },

}

Guixu Data Agent

Guixu provides dataset discovery, valuation, and acquisition for AI agents. It searches across Kaggle, HuggingFace, IPFS, arXiv, DBLP, and other sources.

When to Use

✅ USE this skill when:

• User asks to "find a dataset for training..."
• User asks to "download a dataset about..."
• User asks to "evaluate dataset quality..."
• User asks about dataset licensing or provenance
• User mentions specific datasets (Kaggle, HuggingFace, etc.)
• User wants to search for training data

❌ DON'T use this skill when:

• Generic web search → use browser or web search tools
• Local file read/write → use file tools
• Data labeling or annotation → use coding tools
• Code implementation questions → use coding-agent skill

Workflow

Step 1: Parse Intent (REQUIRED first)

Always start with intent_parse to structure the request:

intent_parse(query="find me a cat image dataset for classification", task_type="classification")

This returns a structured QueryProfile with:

• task_type: classification, detection, segmentation, etc.
• keywords: dataset content terms
• target_entity: main subject
• data_standard: sample_unit, budget, schema expectations

Step 2: Search Datasets

Use dataset_search with keywords from intent_parse:

dataset_search(query="cat image", task_type="classification", limit=10)

Supported sources (leave empty to search all):

• kaggle, huggingface, ipfs, bittorrent
• arxiv, dblp, semanticscholar
• defillama, rwa_xyz, guixu-hub
• pansearch (cloud drives)

Filter options:

• filters.max_price: maximum price in USD
• filters.free_only: only free datasets
• filters.license: specific license (e.g., "CC-BY-4.0")

Step 3: Evaluate Candidates

For each promising candidate, call dataset_evaluate:

dataset_evaluate(cid="kaggle:owner/dataset-name", task_description="cat image classification", task_type="classification", required_columns=["image_path", "label"])

This returns:

• tcv_score: -100 (harmful) to +100 (highly valuable)
• schema_fit: compatibility with required columns
• community_signal: reviews and ratings

Step 4: Download

Once a dataset is selected:

dataset_download(cid="kaggle:owner/dataset-name")
# or
dataset_download(cid="hf:owner/dataset-name")

Free sources: uci:, openml:, zenodo:, figshare:, hf: (public), ipfs:, guixu-hub: Requires login: kaggle:

Tool Chaining Examples

Classification Dataset

1. intent_parse(query="find me a dog vs cat classification dataset", task_type="classification")
2. dataset_search(query="cat dog classification", task_type="classification", limit=10)
3. dataset_evaluate(cid="kaggle:username/dataset", task_description="dog cat binary classification", task_type="classification", required_columns=["image_path", "label"])
4. dataset_download(cid="kaggle:username/dataset")

Detection Dataset

1. intent_parse(query="find helmet detection dataset with bounding boxes", task_type="detection")
2. dataset_search(query="helmet detection bounding box", task_type="detection", limit=10)
3. For each candidate: dataset_evaluate(cid, task_description="helmet detection", task_type="detection", required_columns=["image_path", "bbox"])
4. Download best candidate

Tabular Finance Dataset

1. intent_parse(query="find stock price dataset for time series forecasting", task_type="forecasting")
2. dataset_search(query="stock price time series", task_type="forecasting", filters={source: "defillama"})
3. dataset_evaluate(cid, task_description="stock price prediction", task_type="forecasting", required_columns=["timestamp", "price"])
4. dataset_download(cid)

Important Notes

• Always call intent_parse FIRST — it extracts task_type and keywords that improve search quality
• Keywords should be CONTENT only — no task words like "classification" or "detection"
• Check license before purchase — use require_license_review: true in evaluation
• Budget enforcement — set filters.max_price or budget to limit spending
• Free sources first — try guixuhub, huggingface, ipfs before paid sources

Error Handling

If dataset_search returns no results:

• Try broader keywords
• Remove source filters
• Check if the source is spelled correctly

If dataset_evaluate fails:

• Dataset may be unavailable
• Try a different candidate

If dataset_download fails:

• Check if login is required (Kaggle)
• Verify the CID format is correct: source:owner/dataset