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clean-data-xls

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", "this data is messy".

75

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

85%

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 lean, well-sequenced workflow with strong validation checkpoints and good use of tables, but the detection/profiling steps lack executable code, leaving actionability slightly incomplete. Overall a high-quality, compact skill body.

Suggestions

Add a concrete profiling snippet (e.g. an openpyxl loop or Excel JS call) showing how to detect a column's dominant type and flag outliers, so the Scope/Detect steps are executable rather than descriptive.

Fill the 'Propose fixes' table with one or two example rows (e.g. Column B | Number-as-text | 14 | =VALUE(SUBSTITUTE(B2,"$",""))) to make the intermediate step copy-paste ready.

Provide a minimal complete openpyxl read/write example for the standalone-.xlsx path, mirroring the concrete Office JS guidance already given for the in-Excel path.

DimensionReasoningScore

Conciseness

The body is lean: compact tables for issue detection, no re-explanation of concepts Claude already knows, and every line earns its place; not 2 because there is no padding or unnecessary explanation to tighten.

3 / 3

Actionability

Provides concrete copy-paste fix formulas ('=TRIM(A2)', '=VALUE(SUBSTITUTE(B2,"$",""))', '=UPPER(C2)', '=DATEVALUE(D2)') and an Office JS pattern, but the profiling/detection step ('Profile each column: detect its dominant type') and the empty propose-fixes table lack executable code; not 3 because it is not fully copy-paste ready across all steps, not 1 because substantial concrete guidance is present.

2 / 3

Workflow Clarity

A clear 4-step sequence (Scope → Detect → Propose → Apply) with explicit validation checkpoints ('Show a summary table before changing anything', 'confirm with the user first', 'show the user a sample... and get confirmation before moving to the next category'); the destructive/batch feedback loops satisfy the top anchor.

3 / 3

Progressive Disclosure

Under 50 lines with no external references needed and well-organized sections (Environment, Workflow, tables), meeting the simple-skills allowance for a top score without bundle files.

3 / 3

Total

11

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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.

The description is specific, complete, and trigger-rich, naming concrete cleaning actions plus explicit 'Use when' guidance and natural trigger phrases in third-person voice. It is a strong, low-conflict skill description.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions — 'trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns' — matching the top anchor; not 2 because it goes well beyond naming a domain plus a few actions.

3 / 3

Completeness

Clearly answers both what (the cleaning actions) and when ('Use when data is messy, inconsistent, or needs prep before analysis') with explicit triggers, satisfying the top anchor.

3 / 3

Trigger Term Quality

Explicit natural trigger phrases ('clean this data', 'clean up this sheet', 'normalize this data', 'fix formatting', 'dedupe', 'standardize this column', 'this data is messy') give good coverage of terms users would actually say.

3 / 3

Distinctiveness Conflict Risk

Scoped to spreadsheet data cleaning with distinct triggers, giving it a clear niche unlikely to fire for unrelated skills; not 2 because the triggers are specific rather than broadly overlapping.

3 / 3

Total

12

/

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
anthropics/financial-services
Reviewed

Table of Contents

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