BF pugongying-data-skills
蒲公英数据开发工程师Skill套件 - 专为数据开发工程师设计的完整AI Skill生态系统。 包含7个核心模块：需求分析、架构设计、数据建模、SQL开发、ETL Pipeline、数据质量、数据测试。 当用户需要端到端数据开发解决方案、数据仓库建设、ETL开发、SQL优化、数据质量管理时触发。 触发词：数据开发、数据仓库、ETL、SQL优化、数据质量、数据建模、需求分析、架构设计。
As a process F 35/100 · Will not run — References files that are not bundled: references/requirement-standards.md, references/architecture-standards.md, references/data-modeling-standards.md
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: references/requirement-standards.md - warning
missing-refreference to a missing file: references/architecture-standards.md - warning
missing-refreference to a missing file: references/data-modeling-standards.md - warning
missing-refreference to a missing file: references/sql-standards.md - warning
missing-refreference to a missing file: references/etl-standards.md - warning
missing-refreference to a missing file: references/data-quality-standards.md - warning
missing-refreference to a missing file: references/test-standards.md
Process rating: all ten parameters 35/100
- 0Tools and files. 7 referenced file(s) missing: references/requirement-standards.md, references/architecture-standards.md, references/data-modeling-standards.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 100Steps. 35 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1452 tokens
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -214 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 193: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.