SKILLEMALL.ai

CD FTShare-etf-data

A 股 ETF 数据技能集(market.ft.tech)。覆盖单只 ETF 详情、ETF 分页列表(排序/筛选)、ETF K 线(日/周/月/年线)、ETF 分钟级分时、ETF PCF 列表与下载。用户询问某只 ETF 行情、ETF 列表、ETF K 线、分时或 PCF 申购赎回清单时使用。

ClawHub Agent Skills author: Shawn92 v1.0.1 MIT-0 21 files · 1 script body ≈ 1 103 tokens Open the sourceclawhub.ai↗ analyzed 10 d ago

A 股 ETF 数据技能集(market.ft.tech)。覆盖单只 ETF 详情、ETF 分页列表(排序/筛选)、ETF K 线(日/周/月/年线)、ETF 分钟级分时、ETF PCF 列表与下载。用户询问某只 ETF 行情、ETF 列表、ETF K 线、分时或 PCF 申购赎回清单时使用。

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

ProcedureSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • 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. 18 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1103 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (4 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 147: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

In the sandbox Скрипты не запустились

The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

Запущено 1 скрипт; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

Ни один не дошёл до работы — им не хватило зависимостей, аргументов или файлов. Это не отзыв о поведении: наблюдать было не за чем.

run.pyне запустился: 可用 subskill:

25 Oct 2026

External checks

ClawHub: suspicious
This ETF data skill is mostly coherent, but its PCF download helper can overwrite files through symlinks despite claiming local-path containment, so it should be reviewed before installation.
LLM: suspicious (high) · VirusTotal: · 12 Sept 2026