BD memory-auto-manager
记忆自动主动管理：每15分钟自动扫描对话，发现重要信息自动写入每日memory。解决"主动感知靠自觉经常失效"的问题。 使用场景： - 每次对话结束后自动检查是否有遗漏的重要信息 - 自动记录完成的任务、决策、教训、配置变更 - 增量扫描对话历史，不遗漏不重复 - user + assistant 消息双读，确保不漏AI回复中的结论 核心组件： - memory-scan.py：增量扫描脚本 - 两个 cron：Memory自动检查（每15分钟）+ 每周六记忆合并 适用版本：OpenClaw
As a process D 49/100 · 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.
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 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
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (memory-auto-manager) differs from the folder (xiazun-memory-auto)
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 14 steps
- 100Execution cost. Instruction body is 754 tokens
- 100Running it twice. No mutating operations
- low 10 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
- +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
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 251: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.
External checks
ClawHub: suspicious
This skill appears to do what it says, but it creates an ongoing background process that reads private conversations and stores selected details without clear limits or cleanup controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026