BC neural-memory-enhanced
|-。基于扩散激活的联想记忆系统，通过神经图谱实现智能持久化召回。生物学启发的联想记忆系统，采用扩散激活替代关键词检索和向量搜索。记忆以神经图谱形式组织，。Use when 需要SEO优化、关键词分析、排名提升、搜索流量优化时使用。不适用于黑帽SEO手段。适用于独立开发者、企业团队和自动化工作流场景。 功能涵盖: neural, enhanced。 功能涵盖: memory。
|-。基于扩散激活的联想记忆系统，通过神经图谱实现智能持久化召回。生物学启发的联想记忆系统，采用扩散激活替代关键词检索和向量搜索。记忆以神经图谱形式组织，。Use when 需要SEO优化、关键词分析、排名提升、搜索流量优化时使用。不适用于黑帽SEO手段。适用于独立开发者、企业团队和自动化工作流场景。 功能涵盖…
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Block scalar header includes extra characters: |-。基于扩散激活的联想记忆系统，通过神经图谱实现智能持久化召回。生物学启发的联想记忆系统，采用扩散激活替代关键词检索和向量搜索。记忆以神经图谱形式组织，。Use at line 11, column 16: description: |-。基于扩散激活的联想记忆系统，通过神经图谱实现智能持久化召回。生物学启发的联想记忆系统，采用扩散激活替代关键词检索和向量搜索。记… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-long-hermesdescription is 189 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "summary_zh" - note
frontmatter-keyunknown frontmatter key "tools" - note
frontmatter-keyunknown frontmatter key "pricing_tier"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 33 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2530 tokens
- low 13 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
- +2Single-language instructions
- +3Description length 189: enough signal without eating the budget
- +4Structure: 42 headings
- +3Step-by-step instructions: 33 items
- +4Has examples (4 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.