AC alibabacloud-sls-data-agent
Invoke SLS DataAgent to autonomously perform data acquisition, processing, analysis, and visualization for Alibaba Cloud SLS (Simple Log Service). Acts as a fully automated data analyst — ask a question in natural language, get structured conclusions and charts. Use when the user asks about: 数据分析, 取数, 数据查询, 日志分析, SLS, 可视化, 图表, 数据洞察, data analysis, DataAgent, 全自动数据分析师.
Invoke SLS DataAgent to autonomously perform data acquisition, processing, analysis, and visualization for Alibaba Cloud SLS (Simple Log Service).
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- 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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 55/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 3 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 22 steps, 2 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1731 tokens
- low 10 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
- +1No license
- +2Single-language instructions
- +3Description length 370: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (2 of 3)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.