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smart-ask-data

问数端到端编排(native CLI 版):从候选 KN 选定知识网络,用 bkn object-type 发现对象类与字段, 由编排层 LLM 生成 SQL,再由 ontology dataview query 执行取数; 最后输出中文结论与口径说明。 当用户需要指标、统计、趋势、SQL 取数或数据查询时使用。 指标管理(Metric)相关的 CRUD / 搜索 / 校验 / 查询数据 / 试运行由 smart-data-analysis 路由到 ontology-core 的 `metric` 命令组, 本 skill 不承担指标定义管理;若用户要管理 KN 级指标定义,应回到 smart-data-analysis 路由到 `ontology metric` 命令组。

67

Quality

81%

Does it follow best practices?

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

71%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: clear step sequencing with routing checkpoints, concrete (if placeholder-heavy) command forms, and exemplary progressive disclosure across verified reference files. Its main weaknesses are systematic repetition of the delegation and routing constraints, which inflate token cost without adding information, and the absence of error-recovery guidance for failed query executions.

Suggestions

State the ontology-core delegation rule once (e.g., in the 调用方式 section with the call-chain diagram) and remove the repeated 'Never 直接执行' formulations from the sub-skill table, command-shape section, and elsewhere; the duplicated no-token/Metric-routing passages (lines 59/132 and description/line 73) can likewise be consolidated.

Include one complete, copy-paste-ready example inline — e.g., a full `bkn object-type query` call with a realistic '<filter-json>' value and a populated `properties` payload — so the common single-table path is executable without opening a reference file.

Add a brief error-recovery step after step 4 (e.g., what to do when `dataview query` returns an error or an unexpected schema — re-run schema discovery, adjust SQL, re-execute) to close the workflow's feedback loop.

DimensionReasoningScore

Conciseness

The body is domain-specific throughout (no concepts Claude already knows), but key constraints are repeated systematically: the ontology-core delegation rule appears in the call-chain diagram, the sub-skill table, the command-shape section, and again in references; the no-token statement ("不出现 --token / auth.token / Authorization") appears twice (lines 59 and 132), as does the Metric-routing exclusion. This fits 'mostly efficient but includes some unnecessary explanation or could be tightened'; not 4 because the redundancy is structural (whole duplicated passages), not minor.

3 / 5

Actionability

Concrete command forms are given — "bkn object-type list <kn-id>", "dataview query <dataview-id> --sql \"...\"", and the full properties payload shape '{"_instance_identities":[{...}],"properties":[...]}' — plus the mandatory "--user-id <accountId>" placement rule. Not 5 because '<filter-json>' and '{...}' elide actual JSON bodies and no complete worked command with realistic values appears inline (those are deferred to references).

4 / 5

Workflow Clarity

The 主流程 checklist sequences 5 steps with an explicit either/or routing checkpoint (3a 逻辑属性 vs 3b SQL, "不可混用"), a schema-before-SQL anti-hallucination gate ("Schema 发现先于取数…防止 SQL 幻觉"), SELECT/WITH-only enforcement, an empty-result rule, and hard output constraints. Not 5 because there is no error-recovery feedback loop for a failing or malformed dataview query result.

4 / 5

Progressive Disclosure

The body is a true overview: a per-step table maps each pipeline step to one reference file, and all four referenced files (kn-resolve.md, schema-discovery.md, sql-execute.md, tool-examples.md) plus config.json exist and are one level deep with no further nesting. Detailed command syntax and end-to-end examples are correctly split out of SKILL.md, matching the 'clear overview with well-signaled one-level-deep references' anchor.

5 / 5

Total

16

/

20

Passed

Description

92%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 specifies the full action pipeline in third person, gives an explicit 'use when' clause with natural trigger terms, and explicitly delimits its boundary against the sibling metric-management skill. The only weakness is slightly thin synonym coverage in its trigger terms.

DimensionReasoningScore

Specificity

The description enumerates the complete concrete pipeline — "从候选 KN 选定知识网络,用 bkn object-type 发现对象类与字段,由编排层 LLM 生成 SQL,再由 ontology dataview query 执行取数;最后输出中文结论与口径说明" — covering every stage from KN selection to output. This matches the anchor 'multiple specific concrete actions; comprehensive coverage'; it is not 4 because there are no coverage gaps in the domain's action list.

5 / 5

Completeness

It explicitly answers both questions: the 'what' is the full end-to-end pipeline described above, and the 'when' is the explicit trigger clause "当用户需要指标、统计、趋势、SQL 取数或数据查询时使用" with concrete trigger phrases. Not 4 because the when-guidance is explicit and specific rather than merely present.

5 / 5

Trigger Term Quality

The when-clause names natural user terms — "指标、统计、趋势、SQL 取数或数据查询" — which a user asking for data would plausibly say. It is not 5 because common synonyms and phrasings (e.g., 报表, 数据分析, "多少/占比" style questions) are absent; not 3 because the present keywords are natural and multi-word rather than generic single terms.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche (KN/ontology 问数 — SQL/逻辑属性取数) and actively de-conflicts with the sibling skill: "指标管理…由 smart-data-analysis 路由到 ontology-core 的 metric 命令组,本 skill 不承担指标定义管理". Not 4 because the overlap with the closely related smart-data-analysis entry point is addressed head-on with an explicit routing boundary, leaving minimal conflict risk.

5 / 5

Total

19

/

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

Table of Contents

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