SKILLEMALL.ai

BD week-to-day-splitter

⚠️ **本 skill 包含以下能力,使用前请仔细阅读 SKILL.md 顶部「⚠️ 阅读前必读」章节**: - 文件读写 + 飞书 webhook 推送(daily plan 全文) - 3 个 cron 任务(每天自动执行) 周工作计划自动拆分到日计划系统 — 按子步骤时间标签,把周计划智能拆分成 5 个 daily plan。 适用场景: - 每周固定节奏的项目管理(周一-周五) - 多项目并行(每个项目含多个子步骤,时间标签驱动) - 想从一份周计划自动生成 5 份日计划 - 每天 15:00 推送次日计划到飞书私聊 本 skill 含完整工作流:v7 算法 + 周五复制 + 周日拆分 + 周日-周四推送。

ClawHub Agent Skills author: seairteng v1.1.0 MIT-0 7 files body ≈ 2 449 tokens Open the sourceclawhub.ai analyzed 3 d ago

⚠️ 本 skill 包含以下能力,使用前请仔细阅读 SKILL.md 顶部「⚠️ 阅读前必读」章节: - 文件读写 + 飞书 webhook 推送(daily plan 全文) - 3 个 cron 任务(每天自动执行) 周工作计划自动拆分到日计划系统 — 按子步骤时间标签,把周计划智能拆分成 5 个 daily…

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
D
48/100
Unfinished process
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. 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: 7. 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 48/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 100Steps. 55 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2449 tokens
  • low 13 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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -221 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 315: enough signal without eating the budget
  • +4Structure: 43 headings
  • +3Step-by-step instructions: 55 items
  • +4Has examples (14 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill mostly matches its planning purpose, but it asks for recurring automation that can rewrite weekly plans and send full daily plans externally with some important scope and privacy contradictions.
LLM: suspicious (high) · VirusTotal: · 18 Aug 2026