SKILLEMALL.ai

CD crypto-market-analysis

此技能由Tradingbase团队开发,旨在提供全面的加密货币市场分析技能。从币安获取所有币种的实时行情数据,计算多种技术指标(MACD, KDJ, RSI, CCI, BOLL, WR, PSY, BRAR, DMI),并利用用户连接的LLM进行市场分析。支持短期(默认1小时K线)和中长期(默认1天K线)分析,用户可自由选择时间周期,并以结构化报告格式输出分析结果。当用户需要对加密货币市场进行技术分析、获取行情数据或生成分析报告时使用此技能。

ClawHub Agent Skills author: Tradingbase.AI v1.0.1 MIT-0 7 files · 3 scripts body ≈ 229 tokens Open the sourceclawhub.ai↗ analyzed 7 d ago

此技能由Tradingbase团队开发,旨在提供全面的加密货币市场分析技能。从币安获取所有币种的实时行情数据,计算多种技术指标(MACD, KDJ, RSI, CCI, BOLL, WR, PSY, BRAR…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 7. 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 46/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 10 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 229 tokens
  • 100Running it twice. No mutating operations

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
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • -33 of 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 224: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 10 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: suspicious
The skill mostly does what it says, but it can automatically install an unpinned Python package and run helper scripts with broad inherited environment access, so it needs human review before installation.
LLM: suspicious (high) · VirusTotal: · 12 Sept 2026