AD Get笔记炼金术
Get笔记炼金术 — 将Get笔记中的录音笔记（谈案、会议、课堂、电话） 经过隐私审查→内容蒸馏→结构化沉淀→多端归档，转化为可复用的知识数字资产。 适用人群：律师、咨询师、培训师、销售等大量使用录音记录工作的专业人士。 触发条件： (1) 用户说"处理录音""录音转知识库""录音沉淀""消化录音""录音炼金" (2) 用户说"Get笔记同步飞书""录音归档""录音整理" (3) 定时任务触发：自动扫描新录音并处理 (4) 用户指定某条录音笔记进行处理 核心能力：隐私保护、内容蒸馏、知识分类、多端沉淀（飞书多维表格+IMA知识库+飞书知识库） 依赖：Get笔记App + OpenClaw + 飞书（可选IMA知识库） 环境变量：GETNOTE_API_KEY、GETNOTE_CLIENT_ID
As a process D 46/100 · 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 46/100
- 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. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (Get笔记炼金术) differs from the folder (getnote-alchemy)
- 100Tools and files. No external tools needed
- 100Steps. 35 steps
- 100Execution cost. Instruction body is 1352 tokens
- low 12 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -227 emoji in the instructions: noise for the model
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 355: enough signal without eating the budget
- +4Structure: 36 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (10 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.