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pdf-analysis

PDF 文档解析。自动区分文字型 PDF 与扫描型 PDF,覆盖:文本/表格提取、多页全量扫描、嵌入图表 caption、单位感知数值计算。

56

Quality

62%

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-non-spreadsheet-analysis/capability/pdf-analysis/SKILL.md
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.

The body is highly actionable with executable code and a sensible type-detection-first routing, but leans monolithic and would benefit from tighter validation checkpoints and some content split into reference files.

Suggestions

Promote the 'verify table alignment' and detect-type checks into explicit validate→fix→retry steps with clear pass/fail gating for the batch and OCR methods.

Split longer patterns (scanned-OCR captioning, unit-aware numeric extraction) into reference files and link them one level deep from SKILL.md.

Trim debug print statements and redundant narration to tighten token efficiency toward the lean anchor.

DimensionReasoningScore

Conciseness

Mostly efficient, code-forward and free of concept explanations, but carries numerous debug print statements and inline comments that could be tightened; not quite the lean anchor at level 3.

2 / 3

Actionability

Provides complete, executable Python functions (fitz, pdfplumber, PIL, subprocess) with concrete examples and copy-paste-ready patterns, matching the fully-executable anchor.

3 / 3

Workflow Clarity

Has a clear first checkpoint (Step 0 detect type) and some verification notes (verify table alignment), but validation is implicit (comments) rather than explicit validate→fix→retry checkpoints across the batch methods.

2 / 3

Progressive Disclosure

Well-organized into clear sections with no nested references, but it is a ~300-line monolith with all content inline and no bundle files; some patterns (OCR captioning, unit-aware parsing) could be split out.

2 / 3

Total

9

/

12

Passed

Description

60%

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 strong, specific feature set for PDF analysis but lacks an explicit 'when to use' trigger clause, which caps completeness and distinctiveness.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers, e.g. 'Use when the user mentions PDFs, scanned documents, invoices, or needs text/table/chart extraction from PDF files.'

Include common natural terms users would say (forms, .pdf, OCR, invoices) to broaden trigger coverage.

Keep the third-person descriptive voice but lead with the trigger context so the skill is distinguishable from generic document skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions: '文本/表格提取、多页全量扫描、嵌入图表 caption、单位感知数值计算' plus '自动区分文字型 PDF 与扫描型 PDF', matching the multiple-specific-actions anchor.

3 / 3

Completeness

Answers 'what' clearly but there is no explicit 'Use when...' trigger clause, so per guidelines completeness is capped at 2.

2 / 3

Trigger Term Quality

Contains relevant terms ('PDF', 'PDF 文档', '文字型/扫描型 PDF', '表格', '图表') but misses common natural variations a user might say (e.g. forms, .pdf, document extraction, OCR); phrasing reads as a technical feature list rather than natural triggers.

2 / 3

Distinctiveness Conflict Risk

The text-vs-scanned detection and unit-aware computation niche is fairly specific, but without explicit triggers it could still overlap with general document/PDF skills.

2 / 3

Total

9

/

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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