Automate end-to-end smoke testing of the Kimi Code CLI.
npx skills add https://github.com/haomingz/kimi-skills --skill feature-smoke-test
Please help me install this Agent Skill. Skill: feature-smoke-test Source: https://github.com/haomingz/kimi-skills/tree/main/skills/feature-smoke-test Command: npx skills add https://github.com/haomingz/kimi-skills --skill feature-smoke-test
This Skill helps engineering teams validate the behavior and reliability of the Kimi Code CLI by running repeatable, non-interactive smoke tests that exercise core workflows and verify expected outputs. It automates the discovery of regressions across CLI prompts, tool invocations, and session artifacts.
Run the smoke-test workflow in a dedicated session to validate the CLI end-to-end and capture evidence of success or failure.
High-intent search queries and answers about installing and using this skill.
You can automate end-to-end smoke testing for a CLI by running a non-interactive, prompt-driven workflow that validates core functionality, session handling, and artifact generation. This approach ensures deterministic execution and clear reporting of success or failure.
Non-interactive CLI testing executes prompt-driven workflows without manual input to validate stateful and asynchronous scenarios. You need it to discover regressions across CLI prompts, tool invocations, and session artifacts quickly during release cycles.
To verify session management and artifact generation, execute deterministic smoke tests that exercise tool calls and background tasks across runs. This validates expected outputs and generates quick evidence of success or failure for debugging.
Yes, you can run smoke tests for stateful and asynchronous CLI scenarios by applying prompt-driven workflows that exercise tool calls and background tasks across multiple runs. This ensures thorough evidence collection and deterministic execution.
No, this smoke testing approach uses non-interactive execution with deterministic prompts to validate the CLI end-to-end. It automatically captures evidence of success or failure without requiring manual input during the workflow.
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