SKILLEMALL.ai

BC industry-research-report

依托东方财富数据库,为指定行业生成深度研究报告。 当用户问题中出现可识别的行业/产业/领域主体,且意图属于行业认知、产业剖析或撰写研究报告时,应触发本 Skill。 常见表述如「XX 行业研究」「XX 行业报告」「帮我分析 XX 行业」「XX 产业深度研究」「XX 领域市场分析」等。 即使用户未明说「报告」,只要以某一行业为核心、要求系统性或深度的研究/分析,同样适用。

ClawHub Agent Skills author: Financial AI Analyst v1.0.3 MIT-0 3 files · 1 script body ≈ 545 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

依托东方财富数据库,为指定行业生成深度研究报告。 当用户问题中出现可识别的行业/产业/领域主体,且意图属于行业认知、产业剖析或撰写研究报告时,应触发本 Skill。 常见表述如「XX 行业研究」「XX 行业报告」「帮我分析 XX 行业」「XX 产业深度研究」「XX 领域市场分析」等。…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
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 description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 6 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 545 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 186: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 6 items
  • +4Has examples (6 code blocks)
  • +3All 1 scripts are documented

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

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.

scripts/get_data.pyне запустился: RuntimeError:

29 Oct 2026

External checks

ClawHub: suspicious
This skill appears to do what it says, but it downloads and stores remote report files and installs an unpinned dependency in ways that need careful review before use.
LLM: suspicious (high) · VirusTotal: · 15 Sept 2026