AD memory-keeper
解决 AI /new 后失忆问题的记忆管理 skill。三层加载机制（热/温/冷），session 启动时只取当前需要的记忆，省 token。 包含：任务状态恢复、每日日记、项目索引、Dream 定期整理。纯文件系统，无需外部服务。 用户说"先这样"、"暂停"、"记住这个"、里程碑完成时触发。 Memory management skill that prevents AI amnesia after /new resets. 3-tier loading (hot/warm/cold) loads only what's needed per session — saving tokens. Includes: task state recovery, daily journal, project index, Dream consolidation. Pure filesystem, no external services. Triggers on "pause", "remember this", milestone completion.
As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
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: 6. 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 49/100
- 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
- 40Consistency. Frontmatter name (memory-keeper) differs from the folder (smart-memory-keeper)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 25 steps
- 100Execution cost. Instruction body is 938 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
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
- +1No license
- +2Single-language instructions
- +3Description length 481: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.