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sn-da-excel-workflow

Excel 数据分析多步编排器。覆盖:(1) 读取多 Sheet Excel 文件并统计行数,(2) 大文件检测(≥10k 行自动 Parquet 优化),(3) 数据清洗(缺失值、文本标准化、无效字符),(4) 条件筛选与分类提取,(5) 跨 Sheet 统计聚合,(6) 导出 Excel/CSV 并提供下载链接。覆盖从数据读取到报告生成全流程,按步骤编排 capability 子 skill。**遇到以下任一情况就主动使用本 skill,不要自行写几行 pandas 就回答**:①用户出现触发词:Excel 分析 / 表格分析 / 数据分析 / 数据清洗 / 数据统计 / 数据筛选 / 数据可视化 / 数据导出 / 汇总统计 / 透视表 / 分组统计 / 交叉分析 / 趋势分析 / 对比分析 / 异常值检测 / 去重 / 缺失值处理 / Excel 报告 / 生成报表 / analyze Excel / data analysis / data cleaning / pivot table;②用户上传或指定了 .xlsx / .xls / .csv 文件并要求分析、清洗、统计或可视化;③任务涉及多 Sheet 读取、条件筛选、分类汇总、图表生成中的任意一项;④用户要求导出带格式的 Excel 报告或下载链接。仅不用于:不涉及表格数据的纯文本处理、图片分析(使用 sn-da-image-caption)、单个公式计算的简单问答。

68

Quality

82%

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SKILL.md
Quality
Evals
Security

Quality

Content

68%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, highly actionable workflow with strong gating logic and good progressive disclosure via on-demand sub-skills. The main gaps are a missing output-verification feedback loop (capping workflow clarity) and one category-extraction step that is a comment hint rather than executable code.

Suggestions

Add an explicit verification step after export (e.g., reload the output file and confirm row counts/sheet integrity match expectations) to create a validate→fix→retry feedback loop and lift workflow clarity above the batch-operation cap of 3.

Replace the Step 5 category-extraction comment hint with concrete executable code, or at minimum inline the minimal loop body so the step is copy-paste ready.

Consider moving the large sub-skill catalog into a separate INDEX/reference file referenced from SKILL.md to tighten the overview and improve progressive disclosure.

DimensionReasoningScore

Conciseness

The body is mostly lean concrete code and operational rules with little concept explanation, but the ~100-line inlined sub-skill catalog and some redundancy between workflow steps and the catalog are minor padding that could be trimmed or factored out.

4 / 5

Actionability

Most steps give copy-paste-ready code (openpyxl read_only counting, Parquet caching, cleaning, filtering, export, CJK font block), but Step 5's category extraction is only a comment hint ('Walk rows to find category markers') with no executable code, a minor gap.

4 / 5

Workflow Clarity

The six-step sequence is clear with a strong large-file gate checkpoint at Step 2, but this batch data-processing workflow lacks any explicit output verification/validation feedback loop (e.g., confirm exported file or row-count reconciliation), which caps batch-operation workflow clarity at 3 per the rubric.

3 / 5

Progressive Disclosure

The file is a clear overview that delegates detail to one-level-deep capability sub-skills via well-signaled '→ capability:' references and a navigation catalog, but the large inlined catalog and absence of any actual bundle files in this skill's directory are minor organization gaps.

4 / 5

Total

15

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20

Passed

Description

96%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, highly specific description with comprehensive trigger-term coverage and explicit what/when guidance including exclusions. The only weakness is that the 'any one of these' trigger logic lets broad terms like '数据分析' fire without an Excel context, raising overlap risk slightly above minimal.

Suggestions

Tighten trigger ① so generic terms (数据分析/数据清洗/异常值检测) only fire when paired with a tabular/Excel file context, reducing overlap with non-Excel analytics skills.

Consider requiring trigger ② (an uploaded .xlsx/.xls/.csv file) as a co-condition for the purely keyword-based triggers to lower conflict risk toward minimal.

DimensionReasoningScore

Specificity

The description enumerates six concrete capabilities (multi-sheet row counting, large-file Parquet optimization, data cleaning, conditional filtering/category extraction, cross-sheet aggregation, Excel/CSV export with download links), giving comprehensive concrete coverage rather than vague claims.

5 / 5

Completeness

It explicitly answers 'what' via the six numbered capabilities and 'when' via '遇到以下任一情况就主动使用本 skill' with four numbered trigger conditions plus explicit exclusions, matching the anchor for concrete trigger phrases.

5 / 5

Trigger Term Quality

It lists extensive natural trigger terms in both Chinese and English (Excel 分析 / 数据分析 / 数据清洗 / 透视表 / pivot table / analyze Excel / data cleaning) plus file extensions (.xlsx / .xls / .csv), covering synonyms and extensions comprehensively.

5 / 5

Distinctiveness Conflict Risk

The Excel niche is clear with explicit exclusions (not for plain text, images, simple formulas), but trigger condition ① fires on any single generic term like '数据分析' alone, creating some overlap risk with other analytics skills rather than minimal conflict risk.

4 / 5

Total

19

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20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 suspicious

Warning

Total

15

/

16

Passed

Repository
OpenSenseNova/SenseNova-Skills
Reviewed

Table of Contents

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