BD morgana-anti-infinite-loop-v2-zh
面向 LLM 智能体的轻量级反无限循环守护器 —— 治愈优于终止、可预测、五大保护、零依赖。基于 Python 标准库 + numpy 可选实现,可与任何 LLM(Claude/GPT/文心/通义/智谱)及任何框架(Hermes/LangChain/AutoGen/自定义)配合使用。v1 版本反响平平;v2.0 为全球中文社区完全重写。注:12.8K 下载量是指我们整个 kofna3369 ClawHub 账户,而非 v1 专属。
面向 LLM 智能体的轻量级反无限循环守护器 —— 治愈优于终止、可预测、五大保护、零依赖。基于 Python 标准库 + numpy 可选实现,可与任何 LLM(Claude/GPT/文心/通义/智谱)及任何框架(Hermes/LangChain/AutoGen/自定义)配合使用。v1 版本反响平平;v2.0…
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 20. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "status" - note
frontmatter-keyunknown frontmatter key "date" - note
frontmatter-keyunknown frontmatter key "clawhub_id" - note
frontmatter-keyunknown frontmatter key "language" - note
frontmatter-keyunknown frontmatter key "python" - note
frontmatter-keyunknown frontmatter key "dependencies" - note
frontmatter-keyunknown frontmatter key "optional_dependencies"
Process rating: all ten parameters 44/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Execution cost. Instruction body is 4250 tokens
- 100Steps. 67 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 19 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
- -255 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 217: enough signal without eating the budget
- +4Structure: 47 headings
- +3Step-by-step instructions: 67 items
- +4Has examples (29 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.