Agent Skills: flutter-enterprise
Lightweight Flutter enterprise development skill focused on feature-based clean architecture. Use when user asks to: (1) Build enterprise Flutter apps with clean architecture, (2) Implement feature-based modular structure, (3) Set up scalable Flutter project organization, (4) Create maintainable enterprise codebase. Triggers: Flutter enterprise app, clean architecture Flutter, feature-based Flutter, enterprise Flutter structure, modular Flutter architecture
ID: ajianaz/skills-collection/flutter-enterprise
4
Install this agent skill to your local
Skill Files
The files that make up flutter-enterprise.
More agent skills
- hf-cloud-sagemaker-iam-preflightEnsure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.
- prompt-refinementConvert vague requests into compact TCRO prompts with phase-specific constraints. Used by research, specify, plan, and work workflows.
- issue-managementManage GitHub Issues 2.0 relationships with gh CLI: parent issues, sub-issues, blocked-by, and blocking links.
- huashu-image-upload文章配图一键生成并上传图床，自动插入Markdown链接。当用户提到"配图"、"插图"、"上传图片"、"文章配图"时使用。
- tui-testerExpert guidance for testing Gemini CLI behavior and visual output using terminal automation.
- pandas-proPerforms pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.