llm-evaluation
Skill0 starsChanged 3 months agoMITView on GitHub
Evaluate LLM outputs systematically with a small rubric (correctness, style, safety). 用评分标准系统评估 LLM 输出质量。
19 other projects use a version of this.
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- ControllableModel/SWE-Skills-Benchtheir own version, first seen Nov 8, 2026
- nicholas-greenwood/agentic-engineering-skillstheir own version, first seen Nov 1, 2026
- KMaruthi2002/universal-llm-skillstheir own version, first seen Oct 25, 2026
- KumailQazi/krmaazha-binance-mcptheir own version, first seen Oct 17, 2026
- asmamest/Nexus-Skillstheir own version, first seen Oct 15, 2026
- paulopestanajunior/agents-configtheir own version, first seen Oct 15, 2026
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GoogleSkillcode-review
Adversarial systems, concurrency, memory-model, and architecture code review protocol. Use this skill whenever asked to review code, PRs, diffs, or branches in Filament.
Also in 3 projects that use this
Other versions19 projects use a version of this.
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Wrote their own version · 19
- kumaran-is/claude-code-onboarding★ 35same name
- nimadorostkar/Claude-Skills-collection★ 33same name
- ckorhonen/claude-skills★ 17same name
- xfstudio/skills★ 9same name
- ControllableModel/SWE-Skills-Bench★ 8same name
- KumailQazi/krmaazha-binance-mcp★ 4same name
- nicholas-greenwood/agentic-engineering-skills★ 3same name
- ai4brands-design/claude-skills★ 2same name
- CodeWithBehnam/cc-docs★ 2same name
- Ghosteken/agent-harness★ 2same name
9 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository14 other files it gives its agents.
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The file
Files
LLM Evaluation | LLM 输出评估
Score LLM outputs against a small rubric so「变好了还是变差了」是数字，而不是感觉。
让模型输出的好坏可量化、可比较。
When to use | 适用场景
- 用户在对比两个 prompt / 两个模型 / 前后改动
- 想给输出质量立个可复现的标准
Workflow | 工作流程
- 定义任务 + 评分维度（正确性 / 完整性 / 风格 / 安全，每个 1–5 分）。
- 准备 5–10 个代表性测试输入（含 1–2 个难例）。
- 让评估者对每个输入按维度打分（人或模型皆可，注明是谁评的）。
- 汇总平均分，输出对比表。
- 结论：哪个方案赢、赢在哪一维、下一步改哪里。
Tips | 提示
- 评分标准先写死，再打分，别打分时改标准。
- 有争议的案例单独列出，比平均分更有信息量。
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