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AF cron-task

创建定时任务：扫描上下文→9项就绪检查→生成执行器脚本→撰写Schedule提示词→配置飞书IM提醒+三端归档(飞书云盘/Obsidian/IMA)。当用户说「创建定时任务」「定时任务创建」时触发，适用于任务/技能调试接近完成、需要转为稳定每日自动运行的场景。Do NOT use for 一次性任务、手动触发的脚本执行、或非周期性工作流。

ClawHub Agent Skills author: AI花生 v1.0.1 MIT-0 10 files body ≈ 1 078 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

创建定时任务：扫描上下文→9项就绪检查→生成执行器脚本→撰写Schedule提示词→配置飞书IM提醒+三端归档(飞书云盘/Obsidian/IMA)。当用户说「创建定时任务」「定时任务创建」时触发，适用于任务/技能调试接近完成、需要转为稳定每日自动运行的场景。Do NOT use for…

As a process F 36/100 · Will not run — References files that are not bundled: scripts/<task-name>_executor.py

ProcedureObsidianSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
F
36/100
Will not run
References files that are not bundled: scripts/<task-name>_executor.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/<task-name>_executor.py
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "effort"

Process rating: all ten parameters 36/100

Will not run. References files that are not bundled: scripts/<task-name>_executor.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/<task-name>_executor.py
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1078 tokens
  • low The response is described with custom markup (3 tags): a typed call is more reliable

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 170: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: clean
This skill openly creates recurring task automation with generated scripts, Feishu notifications, and multi-destination archiving, but users should review credentials and destinations before use.
LLM: benign (medium) · VirusTotal: · 11 Jul 2026