SKILLEMALL.ai

BD rule-pasta-zoo

规则怪谈 - 规则怪谈是一种近年来在中文网络文学与亚文化语境中兴起的叙事类型或恐怖文学子类。其核心特征在于:通过一系列看似合理、逻辑自洽但实则隐含矛盾、悖论或超自然暗示的“规则”来构建恐怖氛围,并引导读者在解读规则的过程中逐步感知异常与不安。这类文本通常以第一人称或公告体形式呈现,如“员工守则”“宿舍管理条例”“地铁安全须知”等,表面是日常生活的规范说明,实则暗藏对现实逻辑的颠覆与对未知威胁的预警。规则怪谈的本质在于利用认知失调与语义模糊性制造心理恐怖

Not recommendedcritical or high security findings
ClawHub Agent Skills author: DreamPig v1.0.0 MIT-0 6 files body ≈ 957 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
82
Quality 40%
79
Run on models
none yet
Process rating
D
49/100
Unfinished process
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

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Obfuscation
If you install

The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 1

  • high Obfuscation uni-zero-width SKILL.md:72
    Zero-width / invisible characters (possible hidden text) (41 occurrences)
    - ␀狮子园区␀:

Files scanned: 6. 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 49/100

  • 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
  • 40Consistency. Frontmatter name (rule-pasta-zoo) differs from the folder (rule-pasta-zoo-game)
  • 100Tools and files. No external tools needed
  • 100Steps. 51 steps
  • 100Execution cost. Instruction body is 957 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -253 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 228: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 51 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This is a fictional text-adventure skill that edits a local game save file, with messy copied game content but no evidence of hidden execution, credential use, exfiltration, or real-world destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026