SKILLEMALL.ai

BB alibabacloud-flink-knowledge

阿里云实时计算 Flink 版专家助手。Use when you need expert assistance with Alibaba Cloud Realtime Compute for Flink (阿里云实时计算 Flink 版), including product parameters, engine versions, billing information, general troubleshooting, Flink SQL generation (DDL/pipeline/risk-monitoring scenarios), and explicit file output requests. Covers: parameter/version/billing queries, problem diagnosis, SQL generation, file output.

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 13 files body ≈ 5 555 tokens Open the sourceclawhub.ai analyzed 3 d ago

阿里云实时计算 Flink 版专家助手。Use when you need expert assistance with Alibaba Cloud Realtime Compute for Flink (阿里云实时计算 Flink 版), including product parameters, engine…

As a process B 65/100 · Nearly there — weak spots: when it triggers, progress reporting

ProcedureMySQLInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
B
65/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • 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: 0. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5555 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 65/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Steps. 57 steps, 4 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5555 tokens
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • low The response is described with custom markup (5 tags): a typed call is more reliable

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)
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 402: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 57 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
The skill is mostly a legitimate Alibaba Cloud Flink assistant, but it asks agents to update local aliyun CLI plugins while also claiming no persistent system changes.
LLM: suspicious (high) · VirusTotal: · 16 Jun 2026