SKILLEMALL.ai

BC voc-conversation

客户声音(VOC)会话分析技能。读取用户提供的一批客服会话导出文件(Excel/CSV/文本,逐条式或整段式均可,来自企业微信、千牛、美洽、Udesk、智齿等系统),提炼客户在关心什么,输出高频问题Top榜、情绪分布、原声引用、流失风险信号与改进建议的单次洞察报告。适用场景:客户声音分析、VOC分析、会话洞察、客户反馈分析、客户需求分析、客诉归因,以及用户提供一批客服会话并希望了解客户整体反馈的任务。分析结果供决策参考。本技能面向中文会话设计。反馈与定制联系:zenobiazizi.skills@foxmail.com

ClawHub Agent Skills author: zenobiazizi v1.0.0 MIT-0 5 files body ≈ 538 tokens Open the sourceclawhub.ai analyzed 3 d ago

客户声音(VOC)会话分析技能。读取用户提供的一批客服会话导出文件(Excel/CSV/文本,逐条式或整段式均可,来自企业微信、千牛、美洽、Udesk、智齿等系统),提炼客户在关心什么,输出高频问题Top榜、情绪分布、原声引用、流失风险信号与改进建议的单次洞察报告。适用场景:客户声音分析、VOC分析、会话洞察、客户反…

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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
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. 31 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 538 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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 262: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 31 items
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This skill analyzes user-provided customer service conversations locally and tells the agent to redact sensitive details before reporting quotes.
LLM: benign (high) · VirusTotal: · 12 Aug 2026