xlsx

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Quality

86%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

No findings from the security scan

Quality

Content

77%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 highly actionable skill body with a clear, validated workflow and executable code throughout. Its main weakness is conciseness (some redundant explanation) and progressive disclosure: the financial-modeling standards and detailed checklists are inlined in SKILL.md rather than split into reference files, and the empty references/ dir means there is no layered navigation to detailed material.

Suggestions

Move the financial-model color-coding, number-formatting, and hardcoded-source documentation standards into a separate references/ file (e.g. references/financial_models.md) and link to it from SKILL.md to improve progressive disclosure and reduce top-level tokens.

Trim redundant prose such as the inline comments restating what df.head()/df.info() do and the repeated descriptions of scripts/recalc.py's behavior across the Common Workflow and Recalculating sections.

Add a one-line pointer to the scripts/office/ validators and OOXML schema bundle from the relevant section so the deeper tooling is discoverable without being inlined.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with executable examples, but includes some unnecessary explanation Claude already knows (e.g., "# Preview data" / "df.head()" comments, restating openpyxl purpose, and restating the recalc.py behavior across multiple sections) that could be tightened.	3 / 5
Actionability	Fully executable copy-paste-ready code throughout (pandas read/write, openpyxl create/edit, recalc.py commands) with concrete error-handling JSON output and a formula verification checklist covering the common cases.	5 / 5
Workflow Clarity	Common Workflow is a clearly numbered sequence with an explicit mandatory recalculation checkpoint and a validate/fix/recalculate feedback loop; the recalc.py JSON error_summary drives error recovery.	5 / 5
Progressive Disclosure	SKILL.md inlines substantial material (financial-model color/number-format standards, formula checklists, openpyxl/pandas best practices) that belongs in separate reference files, and the bundle's references/ dir is empty despite the scripts/ tree being referenced only at a high level.	3 / 5
	Total	16 / 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.

An excellent description: specific actions, comprehensive trigger terms, explicit what/when guidance, and a strong negative-boundary clause distinguishing it from adjacent skills. The only minor gap is residual overlap with broad data-analysis skills, which keeps distinctiveness at 4 rather than 5.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ("open, read, edit, or fix", "adding columns, computing formulas, formatting, charting, cleaning messy data", "create a new spreadsheet", "convert between tabular file formats") with comprehensive coverage.	5 / 5
Completeness	Explicitly answers both what ("open, read, edit, or fix... create a new spreadsheet... convert between tabular file formats") and when ("Trigger especially when the user references a spreadsheet file by name or path") with concrete trigger phrases and a negative-boundary clause.	5 / 5
Trigger Term Quality	Comprehensive natural terms including synonyms and file extensions ("spreadsheet", ".xlsx, .xlsm, .csv, or .tsv", "the xlsx in my downloads", "messy tabular data"), covering casual phrasings users actually say.	5 / 5
Distinctiveness Conflict Risk	Clear spreadsheet niche with explicit conflict avoidance ("Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script..."), though overlap with general data-analysis skills is only mildly residual.	4 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

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

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
referenced_paths_exist	Referenced path issues: 1 deeper-than-1-level	Warning

	Total	14 / 16 Passed

Repository: agentscope-ai/QwenPaw
Path: src/qwenpaw/agents/skills/xlsx-en/SKILL.md
Commit: 2094e07

Reviewed: 5 days ago

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