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range-reading-and-large-file-analysis

读取多 Sheet Excel 文件,根据数据量动态选择处理策略,支持特定区域数据提取、大文件 Parquet 转换、统计分析及可视化图表生成。

55

Quality

61%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/sn-da-excel-workflow/capability/excel-reading/range-reading/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%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.

The body is a concise, largely executable code walkthrough of a three-step Excel analysis pipeline, but it lacks validation checkpoints for its batch/destructive write operations and keeps all content inlined rather than splitting the steps into referenced files. It earns high marks for actionability and token efficiency but is held back by workflow and structure gaps.

Suggestions

Add explicit validation/verification checkpoints after each write operation (e.g. assert the Parquet/CSV/PNG files exist and are non-empty, with a fix-and-retry loop on failure) to lift workflow clarity above 3.

Define or initialize the placeholder variables (is_large_file, file_path, target_col) at the top of the snippets, or show how they are derived, so the code is fully copy-paste runnable.

Split the three steps into separate reference files (e.g. references/cleaning.md, references/large-file.md, references/visualization.md) and keep SKILL.md as a concise overview with clearly signaled links to improve progressive disclosure.

DimensionReasoningScore

Conciseness

Lean, code-forward body that does not explain concepts Claude already knows; only minor over-explanation like '# 清洗列名:去除首尾空格' restating the adjacent code could be trimmed.

4 / 5

Actionability

Provides concrete, mostly executable Python covering cleaning, large-file Parquet conversion, range extraction, regression, plotting and export; a few placeholders (is_large_file, file_path, target_col) are undefined, keeping it just short of fully copy-paste ready.

4 / 5

Workflow Clarity

Step1/Step2/Step3 give a clear sequence with empty-data guards, but batch/write operations (to_parquet, to_csv, savefig) lack explicit validation checkpoints or error-recovery feedback loops, which the rubric caps at 3 for such operations.

3 / 5

Progressive Disclosure

Has step-based section headers giving some structure, but everything is inlined into a single ~110-line file with no file splitting or clearly signaled references, and the three steps could reasonably live in separate reference files.

3 / 5

Total

14

/

20

Passed

Description

58%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.

The description conveys a clear, fairly specific set of capabilities but omits any 'when to use' trigger guidance and is missing common natural keyword variations and file extensions. It is third-person and mostly concrete, but completeness is capped by the absent Use-when clause.

Suggestions

Add an explicit 'Use when...' clause naming the trigger scenarios (e.g. analyzing multi-sheet Excel files, extracting data from specific cell ranges, converting large spreadsheets to Parquet).

Include natural user-facing terms and file extensions such as .xlsx, 'spreadsheets', and 'tabular data' to improve trigger-term coverage.

Tighten the abstract phrases ('动态选择处理策略', '统计分析') into concrete actions to push specificity toward 5.

DimensionReasoningScore

Specificity

Lists several concrete actions — '读取多 Sheet Excel 文件', '特定区域数据提取', '大文件 Parquet 转换', '可视化图表生成' — with only the '动态选择处理策略' / '统计分析' phrases being slightly abstract.

4 / 5

Completeness

Has a clear 'what' describing the capabilities, but no 'Use when...' clause or equivalent trigger guidance, which per the rubric caps completeness at 3.

3 / 5

Trigger Term Quality

Includes relevant keywords ('Excel 文件', 'Sheet', 'Parquet', '可视化图表') but lacks common natural variations and file extensions users would say (e.g. .xlsx, 'spreadsheets', '多表格').

3 / 5

Distinctiveness Conflict Risk

The multi-sheet Excel + Parquet + region-extraction niche is mostly distinct, with only minor overlap risk from the generic '统计分析及可视化' tail.

4 / 5

Total

14

/

20

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

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