AC memory-optimizer
基于遗忘曲线与间隔复习科学的高效学习与记忆优化计划生成器（Node.js 实现，含可视化图表，与 sleep-optimizer 同系列）。用户输入 memory-optimizer（或 /memory-optimizer）主动调用，或询问任何学习/记忆相关问题（复习计划、背书、遗忘、备考、学习计划、记忆力、考前冲刺等）时自动触发：先按问题框架收集备考阶段与任务目标，再生成含遗忘曲线图、复习调度甘特图和每日学习计划的可执行记忆优化方案
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 the model run — optional
- 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-no-whendescription does not say WHEN to use the skill (no "use when")
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. 49 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1156 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
- +2Single-language instructions
- +3Description length 219: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.
External checks
ClawHub: clean
This skill locally generates study plans and charts with disclosed command and file use, with no evidence of hidden network, credential, or destructive behavior.
LLM: benign (high) · VirusTotal: · 30 Aug 2026