SKILLEMALL.ai

AC member-manager

管理組織成員(家人、同事、親戚等)的個人資料、農曆/新曆生日及紀念日,並用 OpenClaw cron 設置自動提醒。Use when: (1) 添加/編輯成員資料, (2) 設置生日或紀念日提醒, (3) 更新農曆對應的新曆日期, (4) 查看即將到來的重要日子, (5) 管理多個組織的成員。NOT for: 一般日程安排(用 calendar skill)。

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 5 157 tokens Open the sourcegithub.com analyzed 2 d ago

管理組織成員(家人、同事、親戚等)的個人資料、農曆/新曆生日及紀念日,並用 OpenClaw cron 設置自動提醒。Use when: (1) 添加/編輯成員資料, (2) 設置生日或紀念日提醒, (3) 更新農曆對應的新曆日期, (4) 查看即將到來的重要日子, (5) 管理多個組織的成員。NOT for…

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

ProcedureTelegramAI and agentsPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5157 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "env"
  • note frontmatter-key unknown frontmatter key "paths"
  • note frontmatter-key unknown frontmatter key "dependencies"
  • note frontmatter-key unknown frontmatter key "tools"

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
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (member-manager) differs from the folder (mybirthday)
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 70Execution cost. Instruction body is 5157 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 48 steps
  • low 11 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
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 182: enough signal without eating the budget
  • +4Structure: 40 headings
  • +3Step-by-step instructions: 48 items
  • +4Has examples (26 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.