BD Get笔记
Get笔记 - 个人笔记和知识库管理工具。 ## 核心能力 **1. 一键保存任意内容为笔记** - 发一个链接 → 帮你保存原文并生成摘要，支持所有公开网页链接 - 发一张图片 → OCR 识别文字、AI 分析图片内容 - 写一段话 → 直接保存为文本笔记 - 触发词：「记一下」「存到笔记」「保存这个链接」「保存这张图」「帮我记录」 **2. 查询和获取笔记** - 支持图片笔记、链接笔记、语音笔记（录音转写） - 详情接口返回完整原文（网页正文、音频转写、图片 OCR） - 触发词：「查我的笔记」「看看我存了什么」「找一下笔记」「原文是什么」 **3. 知识库和标签管理** - 创建知识库、将笔记加入/移出知识库 - 添加/删除标签，批量管理 - 查看知识库订阅的博主和直播内容 - 触发词：「查知识库」「建知识库」「加标签」「删标签」 **4. 语义搜索（无需拉取全部数据）** - 全局搜索：在所有笔记中语义召回 - 知识库搜索：在指定知识库范围内搜索 - 触发词：「搜一下」「找找我哪些笔记提到了 XX」「在 XX 知识库搜 XX」 ## 触发场景 - 用户要求「配置 Get笔记」「连接 Get笔记」「帮我设置笔记」时（OAuth 授权流程） - 用户发送任意 URL 链接时（自动识别为保存链接笔记） - 用户发送图片时（自动识别为保存图片笔记） - 用户提到「笔记」「记一下」「保存」「收藏」「存起来」等关键词 - 用户询问「我之前存过什么」「帮我找一下」「搜一下笔记」等
As a process D 37/100 · 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: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 37/100
- 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
- 30Running it twice. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (Get笔记) differs from the folder (lovefromio-getnote)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Execution cost. Instruction body is 4248 tokens
- 100Steps. 73 steps
- 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
- -231 emoji in the instructions: noise for the model
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 654: enough signal without eating the budget
- +4Structure: 52 headings
- +3Step-by-step instructions: 73 items
- +4Has examples (51 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.