AC deep-learning
全能深度阅读工具（All-in-One Deep Reading）。适用于书/长文/研报/论文的深度消化。Use when 用户要深度消化一本书/长文/研报/论文并构建知识网络、产出结构笔记与原子笔记。融合 Mortimer Adler（结构）、Feynman（解释）、Luhmann（网络）、Pragmatist（工具化）、Critics（辩论）；强制 High Fidelity 案例保留与 Actionable 工具提取。关键词：深度阅读、结构笔记、deep learning、卢曼。
As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 59/100
- 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
- 40Result and completion. Does not say what the result is
- 100Tools and files. No external tools needed
- 100Steps. 48 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1485 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
- +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
- +1No license
- +2Single-language instructions
- +3Description length 245: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 48 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.