BC localmemo-pro
面向隐私敏感与离线场景的本地向量记忆系统。基于 LanceDB + 纯本地 embedding（Ollama/nomic-embed-text），实现零外部 API 调用、零数据出域、完全离线可用的语义记忆检索。 核心能力包括本地 embedding 引擎（Ollama nomic-embed-text，毫秒级延迟）、LanceDB 向量库（本地 SQLite 存储）、embedding 结果缓存（避免重复计算）、WAL 写前日志、三层冷热分层、资源占用控制（内存上限/压缩/清理）、一键初始化与维护命令。 适用场景：隐私敏感行业（医疗/金融/法律）、离线/弱网环境、个人知识库、合规要求数据不出域的企业 Agent、希望零 API 成本运行的独立开发者。 差异化：相比云端 embedding 方案，本系统完全本地运行零 API 费用、数据永不离开本机、离线可用；相比简单文件记忆，提供向量语义检索召回更准；新增 embedding 缓存避免重复计算、资源占用控制防止内存膨胀、模型选择指南平衡质量与速度。指令精简分层，降低 token 消耗。 触发关键词：本地记忆、向量记忆、离线记忆、隐私记忆、embedding、LanceDB、Ollama、nomic、本地向量、local memory
面向隐私敏感与离线场景的本地向量记忆系统。基于 LanceDB + 纯本地 embedding（Ollama/nomic-embed-text），实现零外部 API 调用、零数据出域、完全离线可用的语义记忆检索。 核心能力包括本地 embedding 引擎（Ollama…
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: 0. 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. 24 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2001 tokens
- 100Running it twice. No mutating operations
- low 15 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
- -2localhost URLs: will not work for another user
- +2Single-language instructions
- +3Description length 554: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 24 items
- +4Has examples (12 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.