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sn-da-large-file-analysis

万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取(iter_rows 支持 10 万行以上)、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**:①数据行数 ≥ 10k(由 sn-da-excel-workflow 的行数评估步骤触发);②用户出现触发词:大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing;③直接使用 pd.read_excel() 导致超时或内存溢出;④用户明确要求对大规模数据集进行高性能处理。仅不用于:小于 10k 行的常规 Excel 分析(使用 sn-da-excel-workflow 即可)。

68

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

A code-heavy, highly actionable skill with a clear scale-based strategy table and complete worked examples, but it is monolithic with no external references and duplicates mandatory rules in a Best Practices section. Batch/write workflows lack explicit validation feedback loops.

Suggestions

Add explicit validation checkpoints and error-recovery feedback loops for batch and write operations — e.g. verify streamed Parquet row count against the inspected total and retry on schema mismatch/OOM — to lift workflow_clarity above 2.

Remove the 'Best Practices' section that restates the 'Mandatory Rules' table and 'Prohibited' list, keeping a single source of truth, to tighten token usage and improve conciseness.

Split the reusable function library (inspect_excel, stream_excel_to_parquet, convert_excel_to_parquet, optimize_dtypes, write_large_excel) into a scripts/ file referenced from SKILL.md so the main file stays an overview, improving progressive_disclosure.

DimensionReasoningScore

Conciseness

The body is mostly executable code and load-bearing tables, but the 'Best Practices' section restates the 'Mandatory Rules' decision table and the 'Prohibited' list, and the CJK font block plus some prose could be tightened. Not level 3 because of this duplication and padding; not level 1 because most tokens are concrete, useful guidance rather than concept explanation.

2 / 3

Actionability

Provides fully executable, copy-paste-ready functions (inspect_excel, stream_excel_to_parquet, convert_excel_to_parquet, optimize_dtypes, write_large_excel) plus two complete end-to-end worked examples. Not level 2 because the code is complete and runnable, not pseudocode or missing key details.

3 / 3

Workflow Clarity

A clear scale→strategy decision table and sequenced Examples 1 and 2 (Steps 1–6) give a recognizable sequence, and memory estimation acts as a pre-check. It is not level 3 because there are no explicit validation/feedback loops (validate output, retry on schema mismatch or OOM) for these batch and write operations, which the rubric caps at 2.

2 / 3

Progressive Disclosure

The skill is a ~365-line monolithic SKILL.md with no bundle files (references/, scripts/, assets/ absent); sections are well-organized but all reusable code and advanced detail are inline. Not level 3 because there are no one-level-deep references for a skill this large; not level 1 because organization is clear, not a disorganized wall of text.

2 / 3

Total

9

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12

Passed

Description

100%

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, information-dense description that states concrete capabilities, provides extensive bilingual trigger terms, and gives explicit when-to-use guidance with a clear exclusion referencing a sibling skill. Voice is third person throughout, so no voice penalty applies.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'openpyxl read_only 流式读取', 'Parquet 转换加速', '内存优化', '分块处理', '大文件写入模式' — matching the 'lists multiple specific concrete actions' anchor. It is not level 2 because the actions are comprehensive and named, not just a domain plus a few actions.

3 / 3

Completeness

Clearly answers both what (高性能分析引擎 for 万行以上 Excel) and when via the explicit '遇到以下任一情况就主动使用本 skill' clause with four numbered conditions plus exclusions. Not level 2 because the 'when' is explicit, not merely implied.

3 / 3

Trigger Term Quality

Extensive natural bilingual triggers users would actually say: '大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing'. Not level 2 because it covers many common variations rather than a few relevant keywords.

3 / 3

Distinctiveness Conflict Risk

Clear niche (≥10k rows) with an explicit exclusion — '仅不用于:小于 10k 行的常规 Excel 分析(使用 sn-da-excel-workflow 即可)' — that names the sibling skill, making wrong-skill triggering unlikely. Not level 2 because the boundary is explicit rather than a vague overlap.

3 / 3

Total

12

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12

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
OpenSenseNova/SenseNova-Skills
Reviewed

Table of Contents

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