Create and optimize AIASys built-in skills with Python 3.12+ scripts.
npx skills add https://github.com/AIAsys/AIASys --skill skill-aiasys
Please help me install this Agent Skill. Skill: Skill 开发工作台 Source: https://github.com/AIAsys/AIASys/tree/main/apps/backend/capability_sources/builtin/skill/skill-creator-skill Command: npx skills add https://github.com/AIAsys/AIASys --skill skill-aiasys
Creating and optimizing AIASys built-in skills involves managing file structure, validating frontmatter, testing trigger accuracy, and iterating on descriptions. Without dedicated tooling, this process relies on manual checks and external CLIs, leading to inconsistent quality and slow iteration cycles.
Use the Skill 开发工作台 to create a new skill by describing your desired workflow, then run the built-in trigger test to validate that the skill activates correctly for your intended use cases.
High-intent search queries and answers about installing and using this skill.
AIASys built-in skills are created and packaged through structured workflows for requirement analysis, structure design, and test case authoring. Native scripts handle version management and benchmark aggregation without external CLI dependencies.
Trigger testing uses LLM-based evaluation to measure how well a skill description matches user queries. Native scripts run locally to evaluate description accuracy and quantify activation rates against intended use cases.
Description optimization is automated using LLM-powered scripts that generate refined descriptions based on trigger test results. This iterative refinement increases activation rates by improving how well descriptions match user queries.
AIASys skill development requires Python 3.12+ but operates entirely through native scripts without external CLI dependencies. Workspace management, version control, and benchmark aggregation run locally using built-in tooling.
Skills fail to activate when descriptions do not adequately match user queries. Running the built-in trigger test evaluates description accuracy quantitatively, then automated refinement generates improved descriptions to resolve activation failures.
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