BC second-brain-digest
碎片内容提炼工作流。把扔进来的任何碎片内容（文章、笔记、截图文字、对话记录、 想法片段）提炼成标准化知识卡片，并自动发现与已有卡片的关联， 构建可检索、可复用的个人知识库。 触发场景（必须使用本 skill）： - 用户说"帮我消化 / 整理 / 提炼这篇文章 / 这段内容" - 用户粘贴了一段文字、截图文字、链接内容并想要"存起来" - 用户说"把这个加到我的知识库 / second brain / 笔记" - 用户说"帮我做成卡片 / 做成笔记" - 用户分享了读后感、想法片段、会议记录想要结构化 - 任何"我看到了一些东西，想把它变成有用的知识"的场景 与普通总结的区别：不只是缩短内容，而是提炼成标准卡片格式， 并发现与已有知识的连接点，让知识可以被检索和复用。
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.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 341 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "difficulty"
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. No external tools needed
- 100Steps. 14 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 508 tokens
- 100Running it twice. No mutating operations
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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 340: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (4 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.