BC neurocache-pro
面向 AI Agent 的生物启发式联想记忆系统，直击"向量检索找不到概念关联、记忆矛盾无法自动处理、图谱膨胀性能下降"三大痛点。基于扩散激活（spreading activation）替代关键词/向量搜索，记忆形成神经图谱，神经元经 20 种类型化突触连接。 核心能力包括扩散激活联想检索（通过图遍历找到概念相关记忆，即使无关键词/embedding 重叠）、赫布学习（共访问记忆自动强化连接）、20 种突触类型（时间/因果/语义/情感/冲突）、艾宾浩斯衰减生命周期、矛盾自动检测与降权、深度分级检索（0-3 级速度/深度权衡）、大脑版本快照与回滚、大脑移植跨项目知识迁移。 适用场景：需要因果链推理的复杂查询、跨领域概念关联发现、长期项目知识沉淀、冲突信息自动识别、需要版本化管理的记忆库、跨项目知识复用。 差异化：相比向量检索只能找相似文档，本系统通过图遍历找到概念关联记忆（即使无关键词重叠）；矛盾检测自动识别冲突信息并降权过时记忆；深度分级让用户控制速度/深度权衡；版本快照支持回滚。纯算法实现零 LLM 依赖，降低成本。 触发关键词：联想记忆、神经记忆、扩散激活、赫布学习、知识图谱、矛盾检测、nmem、neural memory、associative
面向 AI Agent 的生物启发式联想记忆系统，直击"向量检索找不到概念关联、记忆矛盾无法自动处理、图谱膨胀性能下降"三大痛点。基于扩散激活（spreading activation）替代关键词/向量搜索，记忆形成神经图谱，神经元经 20 种类型化突触连接。…
As a process C 53/100 · Has gaps — 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: 2. 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 "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 12 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1903 tokens
- 100Running it twice. No mutating operations
- low 14 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
- -5TODO / placeholder text left in the skill
- +2Single-language instructions
- +3Description length 537: enough signal without eating the budget
- +4Structure: 37 headings
- +3Step-by-step instructions: 12 items
- +4Has examples (13 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.