SKILLEMALL.ai

AC memory-auto-update

记忆自动更新 - 智能识别重要内容,自动更新记忆,再也不用担心忘记了。 触发场景(中文): - 更新记忆、保存一下、记录今天的对话 - 这个很重要、你记住、记下来、别忘了 - 你忘了吗、你怎么不记得、你记性太差了 - 今天就这样、先到这里、结束了、总结一下今天 - 设置更新模式、设置更新频率、当前设置是什么 - 保存到记忆、写记忆、记录一下 触发场景(English): - update memory, save this, remember this, don't forget - did you forget, how can you not remember - that's all for today, summarize today - update mode, update frequency, current settings 支持3种模式:主动模式、被动模式、智能混合模式 支持5种频率:实时、30分钟、1小时、手动、对话结束

ClawHub Agent Skills author: zhuqingsonga v1.0.0 MIT-0 11 files body ≈ 1 027 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. 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: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description 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. No external tools needed
  • 100Steps. 44 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1027 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 429: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (17 code blocks)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This memory skill is not clearly malicious, but it can persist conversation content through broad everyday phrases and does not clearly bound consent, retention, or file-write scope.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026