SKILLEMALL.ai

BC entropy-intimate-therapy

婚姻家庭咨询里用"熵 + 交叉熵"做理论解释、案例诊断、干预方案设计。基于 ~/entropy/ent3.md 把 Shannon 熵、KL 散度、交叉熵 H(p,q) 映射到 Miller《亲密关系》第 1-14 章及 Bowlby、Gottman、Karney & Bradbury、Rusbult、Murray、Swann、Reis、Drigotas、Finkel 等。三个核心维度:认知熵 / 行为熵 / 意义熵;8 组关键 (p, q) 配对:CL vs 实际、IWM vs 真实伴侣、意图 vs 解读、理想伴侣 vs 候选人、自我概念 vs 他人评价、信任预测 vs 实际行为、米开朗基罗塑造 vs 理想自我、过往 q vs 当下 p。触发关键词:关系熵 / 认知熵 / 行为熵 / 意义熵 / 关系第二定律 / 共享密码本 / 内部工作模型更新 / KL 散度 / 模型与现实失配 / 期望-现实交叉熵 / 高交叉熵 / 怀疑性 q 循环 / 历史回溯重写;自我证实悖论;米开朗基罗 vs 窒息;过度自信代价;治疗 = q 重训练;earned security;自由能;预测大脑。也用于把咨询技术 (EFT/CBT/Gottman/IBCT/PREP) 翻译成熵语言、对夫妻做"熵剖面读数"、定位 q 失配源。使用渐进式披露,按需读取 references/ 下的细节。

ClawHub Agent Skills author: John Do v1.0.0 MIT-0 9 files body ≈ 1 033 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
55/100
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: 9. 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 55/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 39 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1033 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress

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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 593: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 39 items
  • +4Reference files are cited in the instructions (7 of 7)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: clean
The visible skills are clear workflow guides with disclosed command use and no evidence of hidden data theft, persistence, or destructive automation.
LLM: benign (medium) · VirusTotal: · 29 May 2026