CC alibabacloud-elasticsearch-log-config-generator
Use when the user needs to generate a log collection configuration file (OpenTelemetry Collector or Elastic Beats / Filebeat) that writes logs to Alibaba Cloud Elasticsearch or self-managed Elasticsearch. Covers file logs, log API ingestion (OTLP / HTTP webhook), and Kafka log topics. Also matches natural requests such as: write a filebeat.yml, generate an otelcol config, send logs to ES, collect nginx or Java application logs to Elasticsearch, or sync Kafka logs to ES. Always output one configuration file targeting Elasticsearch; never mix technology stacks or emit non-ES outputs.
Use when the user needs to generate a log collection configuration file (OpenTelemetry Collector or Elastic Beats / Filebeat) that writes logs to Alibaba…
As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.
Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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 · 1
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high Concealment
en-hide-from-userreferences/opentelemetry/28-filterprocessor.md:100Instruction to hide actions from the user- Use the most specific condition you can — broad filters silently delete value.
Files scanned: 66. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 74): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 61/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 11 mutating operations with no state check
- 60Tools and files. Uses tools (web, node) that frontmatter does not declare
- 70Execution cost. Instruction body is 4925 tokens
- 85Steps. 134 steps, 1 vague phrases
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 5 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 588: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 134 items
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.