MoonshotAI
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Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
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FR-AI / ANALYSISWhy it is worth attention
It packages a fair, teachable scoring rubric as an installable skill, combining observable criteria with auto-fail rules and coaching prompts to make support QA consistent and transparent.
Who it is for
- Customer support QA teams
- Support team leads and managers
- Support agents looking for coaching guidance
- Developers building skill-based evaluation workflows
Use cases
- Scoring support replies against observable quality dimensions
- Automatically flagging unacceptable replies via auto-fails
- Coaching agents with prompt-based feedback
- Integrating a support quality rubric into a skills-based toolchain
Strengths
- Emphasizes fairness in support reply quality assessment
- Uses observable scoring dimensions rather than vague criteria
- Includes examples and coaching prompts to support learning
- Easy installation via npx and permissive MIT license
Considerations
- README provides only a high-level overview; the full rubric lives in SKILL.md
- Requires the npx skills workflow and a local skill directory to use
- Default language is English, with no detailed documented localization beyond the README's Chinese intro
README quick start
Support Quality Rubric
Fair CS reply quality rubric with examples and coaching prompts.
中文简介： 公正客服回复质检量表，含样例与辅导。 — README.zh.md.
What this skill does
- Observable scoring dimensions + auto-fails
Install
npx skills add / --skill support-quality-rubric
Local skills/support-quality-rubric/. See SKILL.md. Default lang=en.
Example
../../output/samples/support-quality-rubric-example-01.md
License
MIT
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