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smart-search-tables

找表/找数端到端编排:在元数据型知识网络下用 ontology bkn object-type query 检索表/视图实例, 再在职责型知识网络下检索相关部门职责与治理边界,最后汇总为中文结论 (候选表 + 职责要点 + 下一步)。当用户问「表在哪、哪个视图、数据资产归属、谁负责这类数据」时使用。 所有 ontology CLI 执行均委托 ontology-core 完成;本 skill 不直接执行 CLI。

68

Quality

83%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

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 well-structured orchestration skill: the two-step retrieval workflow is unambiguous with checklists and explicit fallback behavior, and detail is correctly pushed to three clearly-signaled, one-level-deep reference files. The main weakness is redundancy — the delegation-to-ontology-core and no-token rules are repeated many times in both the body and every reference file, which costs tokens without adding information.

Suggestions

State the CLI-delegation rule once in a prominent callout and remove the repeated 「Never 直接执行」 restatements from the intro, the dependency table, and the 与 smart-data-analysis 的关系 section.

Consolidate the no-token/gateway notes (currently in both 委托命令形态 and 与 smart-data-analysis 的关系) into the single delegation callout or the runtime_contract pointer.

Inline one minimal complete condition-json example in the body so the primary command form is copy-paste ready without opening metadata-search.md.

DimensionReasoningScore

Conciseness

The body is dense with domain-specific constraints rather than concepts Claude already knows, but the same rule — 「Never 由本 skill 直接执行 ontology CLI,委托 ontology-core」 — is restated at least five times (intro, 调用方式, 子技能依赖 table, 委托命令形态 section, and again in 与 smart-data-analysis 的关系), and the no-token/gateway note appears twice. It fits the 'mostly efficient but could be tightened' anchor rather than the 'minor instances' of the 4 anchor.

3 / 5

Actionability

Concrete command forms are given (`ontology --user-id <accountId> bkn object-type query <kn_id> <ot-id> '<condition-json>' [--limit n]`, `bkn object-type list <kn-id>`) plus explicit search/condition/limit rules and a full worked example deferred to references/tool-examples.md with a real condition-json. It is not a 5 because the body itself only carries placeholder templates; fully copy-paste-ready invocations require the reader to assemble pieces from the reference files.

4 / 5

Workflow Clarity

The sequence is fixed and explicit (metadata KN query → duty KN query derived from step-1 clues → summary), with a copyable progress checklist, and explicit error-recovery/feedback paths: 空结果 → 「放宽 search / 换 KN / 二次澄清」, 命中过少 → rewrite search or loosen condition, 过宽 → lower limit, no clues → 简要反问, missing duty_kn_id → skip and note in summary. This matches the 5 anchor (checklist + feedback loops for recovery) and clearly exceeds the 4 anchor's 'minor validation gaps'.

5 / 5

Progressive Disclosure

The body is a true overview: a per-step reference table clearly signals [references/metadata-search.md], [references/duty-search.md], and [references/tool-examples.md] (all real files), config.json is pointed to for the pipeline/runtime contract, and the references are one level deep with no nested indirection (verified: they only cross-link sibling skills). This matches the 5 anchor's well-signaled, one-level-deep structure.

5 / 5

Total

17

/

20

Passed

Description

88%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: it lays out a concrete, complete 3-step pipeline and includes an explicit use-when clause with natural Chinese trigger phrases. Trigger coverage is good but misses a few common synonyms, and the boundary against sibling data-lookup skills is only implied.

Suggestions

Add one or two more natural trigger variants (e.g., 「数据在哪」「哪个库/哪张表」) to broaden keyword coverage.

Add a brief disambiguation clause (e.g., 『取数/SQL 指标请用 smart-ask-data』) to reduce overlap risk with sibling lookup skills.

DimensionReasoningScore

Specificity

The description enumerates the full pipeline concretely: 「在元数据型知识网络下用 ontology bkn object-type query 检索表/视图实例」「检索相关部门职责与治理边界」「汇总为中文结论(候选表 + 职责要点 + 下一步)」 — multiple specific actions with comprehensive coverage of what the skill does. It even states the execution boundary (CLI delegated to ontology-core), so it is not below the 5 anchor's comprehensiveness.

5 / 5

Completeness

Both halves are explicit: the 'what' is the detailed 3-step pipeline (metadata KN retrieval → duty KN retrieval → Chinese summary), and the 'when' is the explicit clause 「当用户问『表在哪、哪个视图、数据资产归属、谁负责这类数据』时使用」 with concrete trigger phrases. It clearly matches the 5 anchor and exceeds the 4 anchor, whose 'when' is less specific.

5 / 5

Trigger Term Quality

Natural user phrasings are present: 「表在哪」「哪个视图」「数据资产归属」「谁负责这类数据」「找表/找数」 — good keyword coverage a user would actually say. Not a 5 because common variants like 「数据在哪」「哪个库」「字段含义」 or asset-location synonyms are missing, leaving a few natural terms uncovered.

4 / 5

Distinctiveness Conflict Risk

The niche is clear (table/data-asset location in ontology knowledge networks, Chinese 找表/找数 questions) with triggers distinct from sibling data-retrieval skills, so it is mostly distinct. Minor overlap risk remains with closely related lookup skills (e.g., smart-ask-data for 指标/SQL 取数) whose questions could co-occur with 「数据资产归属」 phrasing — not a 5 because that boundary is not drawn in the description itself.

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 missing, 7 suspicious

Warning

Total

14

/

16

Passed

Repository
UnicomAI/wanwu
Reviewed

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