ascendc-st-design

Design Ascend C operator tests via YAML-driven test plan and test-case generation.

11|51|Updated Mar 10, 2026
One-click install
npx skills add https://github.com/hw-native-sys/pypto-lib --skill ascendc-st-design
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ascendc-st-design
Source: https://github.com/hw-native-sys/pypto-lib/tree/main/.claude/skills/cannbot-skills/ascendc-st-design
Command: npx skills add https://github.com/hw-native-sys/pypto-lib --skill ascendc-st-design

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, yaml, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill enables systematic Ascend C operator test design by guiding you through calibrating operator data, drafting test plans, analyzing operator parameters and dependencies, extracting test factors, and generating test-case combinations based on aclnn guidelines.

Core Features & Use Cases

  • Supports building complete operator test workflows (calibration, factor extraction, constraint generation, solver config, and test-case synthesis) for aclnn-based ASCEND C operators.
  • Provides templates and scripts to produce plan.yaml, 04/05 factor constraints, and L0/L1 test cases aligned with standard acceptance criteria.
  • Useful in validating operator functionality and precision across diverse parameter spaces.

Quick Start

Run the prescribed scripts under skills/ascendc-st-design/scripts to generate factors, constraints, solver configs, and test cases for a given operator.

Frequently Asked Questions about ascendc-st-design

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

FAQPage Schema
How do I design Ascend C operator tests for aclnn workflows?

Designing Ascend C operator tests involves orchestrating test plan generation, factor extraction, and test-case construction for aclnn workflows. This skill automates YAML-driven test artifact synthesis to validate operator functionality and precision.

What is factor extraction in Ascend C operator testing?

Factor extraction in Ascend C operator testing is the process of analyzing operator parameters and dependencies to derive test factors. These factors are then used to generate 04/05 factor constraints and plan test-case combinations across parameter spaces.

Can I automate test-case generation for Ascend C operators using YAML?

Yes, you can automate test-case generation for Ascend C operators using a YAML-driven workflow. By running provided scripts, the skill synthesizes L0/L1 test cases and solver configs from extracted factors and YAML constraints.

Does this skill support validating operator precision across diverse parameter spaces?

Yes, this skill supports validating Ascend C operator functionality and precision by planning coverage across diverse parameter spaces. It automates test-case synthesis based on aclnn guidelines to ensure comprehensive validation.

What do I need to generate L0 and L1 test cases for aclnn operators?

To generate L0 and L1 test cases for aclnn operators, you run the prescribed scripts provided by the skill. These scripts automate factor derivation, constraint generation, and test-case synthesis from your operator data and YAML configurations.

Why use YAML for Ascend C operator test workflows instead of manual scripting?

Using YAML for Ascend C operator test workflows enables structured, automated factor derivation and test-case synthesis. It standardizes plan generation, solver configs, and constraint creation, replacing manual scripting with reproducible, automated test artifact generation.