SKILLEMALL.ai

AC stock-summary

Query stock quotes and technical analysis. Triggers on phrases like 查股价, 看股票, 帮我分析XX股票, XX走势. Input: stock code (A-share like 600519, HK like 00700, US like AAPL). Output: real-time quote, RSI/MACD indicators, buy/sell/hold signal, and a trend chart image.

ClawHub Agent Skills author: Dongdong-Bryant v1.0.0 MIT-0 3 files body ≈ 302 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

AnalyzerSoftware developmentInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
50/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 50/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 50Steps. 2 steps
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 302 tokens

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)
  • +3No numbered steps or checklist
  • +1No license
  • +2Single-language instructions
  • +3Description length 256: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Output format is stated explicitly
  • +4Has examples (3 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This skill fetches requested stock data and creates a chart, with no evidence of hidden data access or harmful behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026