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openakita/skills@baidu-famou

Creates algorithm experiments and runs hyperparameter optimization via Baidu Qianfan AppBuilder.

2.0k|274|Updated Jan 30, 2026
Search Tags:#hyperparameter tuning#algorithm optimization#qianfan#Machine learning#Experiment Management#baidu appbuilder
One-click install
npx skills add https://github.com/openakita/openakita --skill openakita-skills-baidu-famou
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openakita/skills@baidu-famou
Source: https://github.com/openakita/openakita/tree/main/skills/baidu-famou
Command: npx skills add https://github.com/openakita/openakita --skill openakita-skills-baidu-famou

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Tuning complex algorithms and managing machine learning experiments requires structured experiment design, baseline configuration, and systematic hyperparameter search, which is time-consuming to set up manually.

Core Features & Use Cases

  • Experiment Management: Create algorithm experiments with defined model types, datasets, baselines, and evaluation metrics through Baidu Qianfan AppBuilder.
  • Hyperparameter Optimization: Run parameter search using grid, random, Bayesian, or Hyperband methods with recommended configurations.
  • Use Case: A machine learning engineer wants to tune a ResNet50 image classification model. They create an experiment specifying the model type and dataset, then request a Bayesian hyperparameter search to receive recommended configurations and expected performance gains.

Quick Start

Ask the assistant to create an algorithm experiment for image classification tuning or to optimize hyperparameters for your model using the baidu-famou skill.

Frequently Asked Questions about openakita/skills@baidu-famou

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a machine learning experiment with Baidu AppBuilder?▼

Run the famou.py script with the experiment command followed by a description, optionally specifying model type and dataset. The script builds a structured query requesting experiment design, baseline configuration, and evaluation metrics from Baidu Qianfan AppBuilder.

How to optimize hyperparameters for a deep learning model?▼

Use the optimize command with a task description and optionally a search space and method. Supported methods include grid, random, Bayesian, and Hyperband search, returning recommended hyperparameter combinations and expected improvement.

What credentials does the Baidu FaMou skill require?▼

The skill requires the APPBUILDER_TOKEN environment variable to authenticate with Baidu Qianfan AppBuilder. Without this token set, the script cannot send queries to the platform.

Which model types are supported for algorithm experiments?▼

The experiment command accepts classification, detection, segmentation, and NLP model types via the --model-type flag. You can also specify a dataset name to include in the experiment design request.

What are the limitations of this algorithm optimization approach?▼

The skill delegates all computation to the Baidu Qianfan AppBuilder API rather than running optimization locally. Results depend on the platform's responses, and an active internet connection plus valid token are mandatory.

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