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antinet-doc-parse

软件开发工程师与数据科学家在构建RAG系统时,当需处理PDF/Word/Excel等多格式复杂文档,用此技能可自动触发三级解析降级,一键输出高置信度结构化Markdown与元数据,免去繁琐清洗,直接夯实企业知识库数据底座!

62

Quality

74%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./skills/antinet-doc-parse/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

78%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 lean, well-structured overview that gives executable entry points, a concrete I/O contract, and a clear fallback workflow with error-recovery loops. It appropriately uses a single real bundle script and avoids explaining concepts Claude already knows.

DimensionReasoningScore

Conciseness

The body is efficiently organized into tight labeled sections (输入/输出/依赖/失败处理) with no over-explanation of concepts Claude already knows; only the '复用价值' section drifts slightly into promotional language that could be trimmed.

4 / 5

Actionability

Provides concrete, copy-pasteable execution guidance — the entry script 'scripts/run_doc_parse.py' (verified present), the run command, the equivalent runtime call, and a defined output path — with only a minor gap of no inline Python API usage example.

4 / 5

Workflow Clarity

Clear sequence (security-scan pass -> MinerU -> PyMuPDF -> pdfplumber fallback) with an explicit error-recovery feedback loop (degrade on failure; all-fail -> 人工介入/BLOCKED; chunk-level low-confidence marking); only a minor validation gap since output correctness is signaled by confidence but no explicit validate step.

4 / 5

Progressive Disclosure

Under 50 lines, well-organized into clear sections, with a single one-level-deep reference to a real bundle file (scripts/run_doc_parse.py, verified to exist) and no nested references — meeting the simple-skill exception for a top score.

5 / 5

Total

17

/

20

Passed

Description

70%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 niche (multi-format document parsing with graceful degradation) and states both capabilities and a trigger condition, but it is weighed down by promotional, role-targeted language that dilutes clarity. It is above the midpoint but short of the crisp, action-first anchors.

Suggestions

Rewrite in third-person action-first voice with a clean 'Use when...' clause, e.g. 'Parses PDF/Word/Excel documents into structured Markdown and metadata via a three-level fallback. Use when ingesting multi-format documents into RAG pipelines or knowledge bases.'

Add file extensions and synonyms (.pdf, .docx, .xlsx, document extraction) to broaden natural trigger-term coverage.

Trim marketing fluff ('一键', '免去繁琐清洗', '夯实企业知识库数据底座', role-targeting of '软件开发工程师与数据科学家') so concrete capabilities stand out.

DimensionReasoningScore

Specificity

Names the domain (PDF/Word/Excel multi-format documents) and 1-2 concrete actions ('自动触发三级解析降级', '输出高置信度结构化Markdown与元数据'), but coverage is not comprehensive and the action list is padded with marketing fluff like '夯实企业知识库数据底座'.

3 / 5

Completeness

Both what (structured Markdown + metadata via three-level fallback) and when ('当需处理PDF/Word/Excel等多格式复杂文档') are explicitly present, but the trigger is buried in role-targeted marketing framing rather than a clean 'Use when...' clause.

4 / 5

Trigger Term Quality

Good keyword coverage with natural format names users say ('PDF/Word/Excel', '多格式复杂文档', 'RAG系统', '解析'), but missing common synonyms and file extensions like .pdf, .docx, .xlsx.

4 / 5

Distinctiveness Conflict Risk

The three-level-fallback document-parsing niche is mostly distinct with clear format triggers, with only minor overlap risk against a generic single-format PDF skill.

4 / 5

Total

15

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
anbeime/skill
Reviewed

Table of Contents

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