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。

Quality

81%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

70%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Well-structured orchestration skill with a clear sequenced workflow, a useful checklist, and clean progressive disclosure into real reference files. The main weaknesses are repeated restatement of the same delegation/no-token constraints and command forms that stay at the template/placeholder level rather than being copy-paste ready.

Suggestions

State the delegation chain, the 'Never execute ontology CLI directly' rule, and the 'no token' constraint once each in a single constraints section, then reference it instead of repeating them across the call-chain, dependencies, command-form, and 关系 sections.

Add one concrete end-to-end example with realistic (sample) values filled into the placeholders in SKILL.md or prominently in tool-examples.md, so the command forms are copy-paste ready rather than templates.

Collapse the duplicate KN-source/解析 guidance that appears in both the 主流程 checklist and the 知识网络来源 / 步骤约束 sections into one place.

Dimension	Reasoning	Score
Conciseness	Content is dense and domain-specific (not padded with concepts Claude knows), but the delegation chain (smart-data-analysis→smart-ask-data→ontology-core), the 'Never execute ontology CLI directly' rule, and the 'no token / 不出现 --token' note are each restated multiple times, so it could be tightened — matching the 'mostly efficient but some unnecessary repetition' anchor.	2 / 3
Actionability	Concrete native subcommands and syntax are given (bkn get/object-type, dataview query --sql, --user-id), but they are command-form templates with placeholders (<accountId>, <kn-id>, <dataview-id>) and execution is deliberately delegated to ontology-core/references, so they are not copy-paste ready — the 'some concrete guidance but incomplete' anchor fits rather than the fully-executable anchor.	2 / 3
Workflow Clarity	A clear 5-step sequence with a copyable progress checklist, per-step constraints, and a hard result-display gate (必须同时展示 SQL + 结果) plus empty-result handling; the operation is read-only (SELECT/WITH only), so the missing-feedback-loop cap for destructive/batch ops does not apply.	3 / 3
Progressive Disclosure	SKILL.md is a concise overview with a step→reference table linking four real, one-level-deep reference files (kn-resolve.md, schema-discovery.md, sql-execute.md, tool-examples.md), with content appropriately split and easy navigation — matching the clear-overview-with-well-signaled-references anchor.	3 / 3
	Total	10 / 12 Passed

Description

92%

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, concrete description with explicit trigger guidance covering both what the skill does and when to use it. The only weakness is trigger overlap with the sibling smart-data-analysis routing skill, which blurs distinctiveness.

Suggestions

Tighten the 'Use when' clause to phrasings that distinguish 问数 (SQL/metric retrieval) from the broader smart-data-analysis router, e.g. emphasize '用 SQL/ontology 取数出结果' rather than generic 指标/统计/趋势.

Add a one-line disambiguator noting this is the data-retrieval specialist invoked after smart-data-analysis routing, so Claude does not select it for non-retrieval analysis tasks.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — "选定知识网络", "发现对象类与字段", "生成 SQL", "执行取数", "输出中文结论与口径说明" — matching the anchor for several specific concrete actions rather than vague language.	3 / 3
Completeness	Clearly answers both what (the full KN→schema→SQL→execute→summarize pipeline) and when (explicit "当用户需要...时使用" clause), satisfying the both-what-and-when anchor.	3 / 3
Trigger Term Quality	Explicit trigger terms a user would naturally say — "指标、统计、趋势、SQL 取数或数据查询" — give good coverage of natural phrasings, matching the high-coverage anchor.	3 / 3
Distinctiveness Conflict Risk	The 问数 niche and ontology/bkn/dataview toolchain are distinctive, but broad triggers like 指标/统计/趋势/数据查询 overlap with the sibling smart-data-analysis router, so it could still trigger the wrong skill — the 'somewhat specific but could overlap' anchor fits better than the clear-niche anchor.	2 / 3
	Total	11 / 12 Passed

Validation

87%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
relative_links	Relative link issues: 1 missing, 6 suspicious	Warning

	Total	14 / 16 Passed

Repository: UnicomAI/wanwu
Path: configs/microservice/bff-service/configs/agent-skills/ontology/smart-ask-data/SKILL.md
Commit: f618458

Reviewed: about 12 hours ago

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