AC data-hub
数字货币量化交易系统的内存数据中枢，为多 Agent 提供统一的数据共享层。管理行情(market_state)、指标(indicators)、情报(intelligence)、风控(risk_audit)四个命名空间。Use when: Agent 需要读写共享数据、查询行情/指标/研报/风控状态。NOT for: 持久化存储、历史数据查询、直接下单交易。
As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
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: 20. 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 54/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 886 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
- +3Output format is not stated: the model decides each time
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 180: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.
External checks
ClawHub: suspicious
The skill mostly matches its data-sharing purpose, but it contradicts its “in-memory only” claims by persisting sensitive risk-audit data to arbitrary filesystem paths.
LLM: suspicious (high) · VirusTotal: · 29 May 2026