SKILLEMALL.ai

AD apple-smart-schedule

把一句自然语言(机票/高铁/开庭/会议/截止日期/聚会/看病等)或一张票据截图,自动变成苹果「日历」事件 + 一串按事件类型智能提前的「提醒事项」。在 macOS 上运行、经 iCloud 同步到 iPhone/iPad。当用户说「帮我加个日程/提醒」「机票 MU5137 8:30 起飞提醒我」「下周三下午开庭提前提醒」「G1234 高铁」「上诉期 15 号截止」「提前 2 小时提醒我」「把这个行程加到日历」等任何要把时间安排写进苹果日历或提醒事项的场景,都必须用本 skill。仅 macOS。

ClawHub Agent Skills author: xierluo v0.2.0 MIT-0 13 files · 7 scripts body ≈ 1 535 tokens Open the sourceclawhub.ai analyzed 3 d ago

把一句自然语言(机票/高铁/开庭/会议/截止日期/聚会/看病等)或一张票据截图,自动变成苹果「日历」事件 + 一串按事件类型智能提前的「提醒事项」。在 macOS 上运行、经 iCloud 同步到 iPhone/iPad。当用户说「帮我加个日程/提醒」「机票 MU5137 8:30…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
D
43/100
Unfinished process
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: 13. 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")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 43/100

  • 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
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1535 tokens

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
  • -218 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 250: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 7 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
The skill is a mostly coherent local macOS calendar/reminder helper, but it needs review because it can search and modify broad calendar data and persist user preference changes without strong confirmation boundaries.
LLM: suspicious (high) · VirusTotal: · 4 Sept 2026