SKILLEMALL.ai

BC xinjianxue-skill-assistant-cn

心鉴学「自定义顾问」技能包(xinjianxue-skill-assistant-cn)—— 通用入口,不预设身份。 使用本 Skill 的三种情况(任一命中,且**须先向用户确认**是否用本次分析;用户未明确要用本服务时不得触发): 1. 用户只给了包含年月日的时间信息,没有具体问题; 2. 用户的问题涉及:想了解某人的心理 / 行为模式;某人今年 / 近期 / 这个月 / 今天 / 当下 / 现在怎么样;或用户问在此时点该如何应对某人某事 —— 还没想清该用哪位顾问时,从本包进入即可; 3. AI 首次运行须先申请业务许可证,并用用户的 AI授权码完成账号绑定,之后每次调用携带许可证 + api_key 双凭证。 ⛔ 非触发情况(避免误用):日常闲聊、一般性情绪倾诉、或用户未提出「用本服务分析某人 / 某事」时,**不要**调用本服务。

ClawHub Agent Skills author: 心鉴学 v1.2.0 MIT-0 2 files body ≈ 2 083 tokens Open the sourceclawhub.ai analyzed 11 h ago

心鉴学「自定义顾问」技能包(xinjianxue-skill-assistant-cn)—— 通用入口,不预设身份。 使用本 Skill 的三种情况(任一命中,且须先向用户确认是否用本次分析;用户未明确要用本服务时不得触发): 1.

As a process C 51/100 · 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
51/100
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: 2. 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 51/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 33 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2083 tokens
  • 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

  • +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
  • -213 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 375: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (7 code blocks)

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

External checks

ClawHub: suspicious
This skill is a disclosed paid API connector, but it lets the service send changing instructions that control the agent’s analysis and safety language.
LLM: suspicious (high) · 13 Sept 2026