BD ai-eval-toolkit
LLM 评测工具链（可运行实现）——把评测方法论变成能直接跑的本地引擎：评测集管理（JSONL 建集/质量检查/规模统计）、幻觉检测引擎（数字一致性/引用校验/否定矛盾/关键论断互证四类规则检测）、RAG 指标计算（RAGAS 四指标的本地简化实现：忠实度/答案相关性/上下文精度/上下文召回）、回归对比（基线 vs 新结果差异判定）、报告生成与上线门禁（分场景得分/门禁判定/报告输出）。零依赖纯标准库，本地闭环不联网。与「LLM 质量评测」（方法论）互补——那个讲怎么做，这个给能跑的实现。面向 AI 工程师、测试与质量负责人。
LLM 评测工具链（可运行实现）——把评测方法论变成能直接跑的本地引擎：评测集管理（JSONL 建集/质量检查/规模统计）、幻觉检测引擎（数字一致性/引用校验/否定矛盾/关键论断互证四类规则检测）、RAG 指标计算（RAGAS 四指标的本地简化实现：忠实度/答案相关性/上下文精度/上下文召回）、回归对比（基线 vs…
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 266 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "title" - note
frontmatter-keyunknown frontmatter key "description_en"
Process rating: all ten parameters 46/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 657 tokens
- 100Running it twice. No mutating operations
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 266: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.