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pipeline-model-interpreter

解释 BK-CI 流水线或创作流的 Model JSON,帮助 AI 理解 Model -> Stage -> Container(Job) -> Element 的含义、启动参数、矩阵、Finally、审核与常见插件语义。用户贴出流水线 JSON、创作流 JSON、编排数据,或询问某个 Stage/Job/插件/atomCode/stepId 含义时使用。

Quality

94%

Does it follow best practices?

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

88%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 lean, well-structured instruction-only skill body: domain rules are specific and non-redundant, the analysis workflow is clearly sequenced with a pitfalls checklist, and detail is correctly pushed to reference files. The main gaps are the absence of a worked JSON example and reference paths that are not clickable links and could not be verified against the bundle.

Suggestions

Add a minimal worked example in the body: a short Model JSON snippet with a 3-4 line walk-through showing how it resolves into Model -> Stage -> Container -> Element, to make the 快速指导 steps fully concrete.

Convert the reference pointers into markdown links (e.g., [1-json-analysis-playbook.md](references/1-json-analysis-playbook.md)) and confirm the directory name matches the actual bundle layout (`references/` is the convention; the body uses `reference/`).

State the expected output shape for interpretations (e.g., "answer with 主流程 → 关键 Job → 启动参数 → 产出") so the workflow ends in a verifiable, consistent deliverable.

DimensionReasoningScore

Conciseness

The body is dense and every rule is domain-specific knowledge Claude would not already know (e.g., "`additionalOptions.enable=false`、`jobControlOption.enable=false`、`stageControlOption.enable=false` 要明确标注为已禁用"); there is no padding or explanation of generic concepts.

5 / 5

Actionability

Guidance is concrete — specific field paths ("`@type`、`atomCode`、`name`、`data.input`、`data.output`"), explicit type enumerations, and a conservative policy for unknown atomCodes — but there is no worked example (e.g., a small JSON fragment walked through the four-layer structure) to make the interpretation steps fully copy-paste executable. Fits anchor 4 rather than 5.

4 / 5

Workflow Clarity

The 快速指导 section gives a clear 8-step sequence (restore the four-layer structure → identify trigger params → classify container → classify plugin → inspect fields → scope the answer → handle unknown atomCodes → defer to references), with error handling for the unknown-atomCode case and a 关键陷阱 checklist. This is read-only analysis, so no destructive/batch validation cap applies.

5 / 5

Progressive Disclosure

The body is a well-organized overview with clearly labeled one-level-deep references ("详细分析步骤:`reference/1-json-analysis-playbook.md`" etc.). It falls short of 5 because the reference paths are backticked text rather than links, the directory is named `reference/` instead of the conventional `references/`, and no bundle files were provided to verify the referenced paths resolve.

4 / 5

Total

18

/

20

Passed

Description

96%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: third-person, concise, with an explicit use-when clause and concrete trigger phrases covering pipeline JSON, creation-stream JSON, orchestration data, and specific identifiers. The only gap is that it doesn't self-distinguish from closely related BK-CI skills at the description level.

DimensionReasoningScore

Specificity

The description lists multiple concrete capabilities — "解释 BK-CI 流水线或创作流的 Model JSON", "Model -> Stage -> Container(Job) -> Element 的含义、启动参数、矩阵、Finally、审核与常见插件语义" — giving comprehensive, specific coverage of the interpretation domain rather than generic actions.

5 / 5

Completeness

It explicitly answers both questions: what it does ("解释…Model JSON,帮助 AI 理解…") and when to use it ("用户贴出…或询问…含义时使用"), with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

It covers natural user phrasings and synonyms — "流水线 JSON、创作流 JSON、编排数据" — plus concrete identifiers users would actually cite ("Stage/Job/插件/atomCode/stepId"). Not below 5 since coverage includes both the colloquial and technical variants users would say.

5 / 5

Distinctiveness Conflict Risk

The BK-CI Model JSON niche is clear and unlikely to trigger generic skills, but the description alone does not draw boundaries against closely related sibling skills (e.g., pipeline-model-architecture or managing-devops-pipeline, which the body itself names as adjacent), fitting the "mostly distinct; minor overlap risk" anchor rather than 5.

4 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

TencentBlueKing/bk-ci

Path

ai/skills/pipeline-model-interpreter/SKILL.md

Reviewed

Table of Contents

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