SKILLEMALL.ai

AD dataquant-connector

对接 DataQuant 量化数据平台,为回测与选股提供 REST API 取数通道,覆盖 A股/港股/美股/加密货币/指数/ETF 六大市场,支持 K线、估值快照、条件筛选与宏观数据。 当用户需要从 DataQuant 查询行情、K线、估值快照、条件选股或宏观数据,或消息中出现 "DataQuant" / "dataquant kline" / "dataquant screen" 等取数指令时启用本 Skill。

ClawHub Agent Skills author: ai-ip v0.1.0 MIT-0 7 files body ≈ 1 194 tokens Open the sourceclawhub.ai analyzed 2 d ago

对接 DataQuant 量化数据平台,为回测与选股提供 REST API 取数通道,覆盖 A股/港股/美股/加密货币/指数/ETF 六大市场,支持 K线、估值快照、条件筛选与宏观数据。 当用户需要从 DataQuant 查询行情、K线、估值快照、条件选股或宏观数据,或消息中出现 "DataQuant" /…

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

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
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. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1194 tokens
  • 100Running it twice. No mutating operations
  • low 12 top-level sections: this looks like several domains in one skill

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 210: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

External checks

ClawHub: suspicious
The skill is a coherent DataQuant market-data connector, but its docs encourage users to paste an API key into chat, which creates avoidable credential exposure.
LLM: suspicious (high) · 6 Aug 2026