SKILLEMALL.ai

AC business-domain-payload

业务领域负载物,覆盖任何组织都存在的10大核心业务职能子域,共289个任务。涵盖情报采集、数据分析、内容生产、活动运营、知识管理、流程协同、客户运营、合规风控、资质认证、供应链管理等业务场景。行业差异通过校准任务注入,框架层零修改。触发词:业务领域、组织运营、情报采集、数据分析、内容生产、活动运营、知识管理、流程协同、客户运营、合规风控、资质认证、供应链管理、meta-skill-system。

ClawHub Agent Skills author: 波动几何 v1.0.1 MIT-0 5 files body ≈ 1 006 tokens Open the sourceclawhub.ai analyzed 2 d ago

业务领域负载物,覆盖任何组织都存在的10大核心业务职能子域,共289个任务。涵盖情报采集、数据分析、内容生产、活动运营、知识管理、流程协同、客户运营、合规风控、资质认证、供应链管理等业务场景。行业差异通过校准任务注入,框架层零修改。触发词:业务领域、组织运营、情报采集、数据分析、内容生产、活动运营、知识管理、流程协同…

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

Proceduretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
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: 5. 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. 38 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1006 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 199: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
The skill appears to be a broad Chinese business-analysis reference, but it includes an unsafe rule telling agents not to refuse edits to the skill itself.
LLM: suspicious (medium) · VirusTotal: · 18 Jun 2026