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smart-data-collection

智能数据采集技能,用于从图片或文档(PDF、Word、Excel)中提取结构化数据,基于知识网络完成字段映射,生成SQL并写入数据库。当用户提到"数据采集"、"从文档提取数据"、"图片转数据"、"数据导入"、"文档数据入库"、"批量数据提取"或需要从非结构化文件中提取结构化数据并存储时,自动使用此技能。

73

Quality

91%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

92%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.

An excellent operational skill body: fully executable commands with real examples and failure receipts, a rigorously sequenced write workflow with validation and confirmation checkpoints, and clean one-level-deep reference structure. The only weakness is redundant restatement of the same safety rules across the IRON RULE, 注意事项, and Phase 0 sections.

Suggestions

Consolidate the duplicated safety rules: make 注意事项 reference the IRON RULE by number instead of restating rules 1–3 and 8, and trim the Phase 0 boundary list that repeats them again.

Move the 真实链路速记 worked example into a reference file (e.g., a new references/example-run.md) to further slim the always-loaded body.

DimensionReasoningScore

Conciseness

The body is dense with deployment-specific operational detail Claude could not know (e.g., the `product_entity\ LIMIT 50` sqlglot incident, `Error 1062` receipt shape, quote-stripping rules for `meta_table_name`) and explains no general concepts, fitting the 'efficient, minor trims possible' anchor. It falls short of lean/5 because the safety rules are restated three times — IRON RULE, 注意事项 items 1–3/8, and the Phase 0 sections repeat the same prohibitions.

4 / 5

Actionability

Commands are fully executable and copy-paste ready with real IDs and expected outputs, e.g., `ontology ds import-csv 374c4c1b-8836-4e60-8099-572a1f0367c5 --table-name product_entity --file test_dedup.csv --batch-size 100` plus the exact failure receipt JSON and the expected HTTP endpoint, covering the common success and duplicate-key cases.

5 / 5

Workflow Clarity

The 10-step ordered checklist (0a–10) with a copyable progress block has explicit validation checkpoints for a batch database write: three-stage target resolution, primary-key pre-check SQL, client-side diff-set removal, mandatory user confirmation of kn/object_type/dataview/datasource/table/rows, and receipt verification with per-row failure reporting and no blind retries — matching the anchor-5 feedback-loop pattern.

5 / 5

Progressive Disclosure

The body is an overview that offloads per-step detail via a step→reference table; all five referenced files (parse-input.md, map-fields.md, resolve-target.md, csv-prepare.md, import-csv.md) exist and are one level deep, with their outbound links pointing to sibling skill contracts rather than nested detail. Navigation by step is easy and clearly signaled.

5 / 5

Total

19

/

20

Passed

Description

91%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.

A strong description that clearly states both capability and trigger conditions with multiple natural Chinese trigger phrases. Its main flaw is the "生成SQL并写入数据库" over-claim, which contradicts the skill body's IRON RULE that SQL generation and direct DB writes are forbidden.

Suggestions

Replace "生成SQL并写入数据库" with an accurate phrase like "通过平台 data-flow 通道写入数据库" to match the body's IRON RULE (no SQL generation, `ds import-csv` is the only write path).

Narrow the broad trigger "数据导入" (e.g., "表格/文档数据导入数据库") to reduce overlap with generic file-import skills and improve distinctiveness.

DimensionReasoningScore

Specificity

The description lists several concrete pipeline actions ("从图片或文档(PDF、Word、Excel)中提取结构化数据", "基于知识网络完成字段映射", "写入数据库") with named input formats, matching the 'several specific actions, minor gaps' anchor. It is not a 5 because "生成SQL并写入数据库" over-claims: the skill body's IRON RULE explicitly forbids generating SQL and only permits writes via `ontology ds import-csv`.

4 / 5

Completeness

It explicitly answers both what ("提取结构化数据…完成字段映射…写入数据库") and when ("当用户提到…或需要从非结构化文件中提取结构化数据并存储时,自动使用此技能") with concrete trigger phrases, exactly matching the anchor-5 example pattern.

5 / 5

Trigger Term Quality

It enumerates six natural trigger phrases users would actually say ("数据采集"、"从文档提取数据"、"图片转数据"、"数据导入"、"文档数据入库"、"批量数据提取") plus a catch-all condition and named formats (PDF、Word、Excel), matching the comprehensive-synonyms anchor rather than the 'a few natural terms missing' anchor at 4.

5 / 5

Distinctiveness Conflict Risk

The unstructured-to-database niche is distinct with dedicated triggers, but "数据导入" is a broad term that could overlap with generic import/ETL skills, so minor overlap risk keeps it at anchor 4 rather than the minimal-conflict anchor 5.

4 / 5

Total

18

/

20

Passed

Validation

87%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 1 suspicious

Warning

Total

14

/

16

Passed

Repository
UnicomAI/wanwu
Reviewed

Table of Contents

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