BD data-analyst-cn
|-。数据清洗、统计分析、时间序列分析、可视化代码生成与分析报告自动生成。数据分析师——快速进行数据清洗、统计分析和可视化，适合数据分析师、产品经理、运营人员。Use when 需要数据分析、报表生成、统计洞察、数据可视化时使用。不适用于实时流数据处理。适用于独立开发者、企业团队和自动化工作流场景。 功能涵盖: analyst。
|-。数据清洗、统计分析、时间序列分析、可视化代码生成与分析报告自动生成。数据分析师——快速进行数据清洗、统计分析和可视化，适合数据分析师、产品经理、运营人员。Use when 需要数据分析、报表生成、统计洞察、数据可视化时使用。不适用于实时流数据处理。适用于独立开发者、企业团队和自动化工作流场景。 功能涵盖…
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
The same skill appears in 1 more place: ClawHub
How to improve
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Block scalar header includes extra characters: |-。数据清洗、统计分析、时间序列分析、可视化代码生成与分析报告自动生成。数据分析师——快速进行数据清洗、统计分析和可视化，适合数据分析师、产品经理、运营人员。Use at line 11, column 16: description: |-。数据清洗、统计分析、时间序列分析、可视化代码生成与分析报告自动生成。数据分析师——快速进行数据清洗、统计分析和可视化，适合数据… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-long-hermesdescription is 165 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "summary_zh" - note
frontmatter-keyunknown frontmatter key "tools" - note
frontmatter-keyunknown frontmatter key "pricing_tier"
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
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (data-analyst-cn) differs from the folder (data-analyst-chinese)
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 19 steps
- 100Execution cost. Instruction body is 2962 tokens
- low 17 top-level sections: this looks like several domains in one skill
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 165: enough signal without eating the budget
- +4Structure: 42 headings
- +3Step-by-step instructions: 19 items
- +4Has examples (18 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.