SKILLEMALL.ai

AD efficiency-gold-miner-sop-alchemy-universal

像火眼金睛一样看穿日常工作痕迹里的隐形重复摩擦,把日程、待办、文档、表格、审批、 邮件和消息等泥沙放进 SOP 炼丹炉,提炼出值得沉淀的自动化 SOP 金丹、Skill 需求卡片、 日报草稿和明日待办。支持钉钉/悟空 dws,也可适配 Google Workspace 等工作数据源。 Use when user mentions "复盘今天", "哪些工作可以自动化", "帮我生成日报", "找出重复工作", "沉淀成 Skill", or asks to "分析今天的工作摩擦", "把重复工作整理成 SOP". Distinct from 日报写作技能(只生成汇报文本) and 待办管理技能(只创建或查询待办). Do NOT use for 屏幕监听、员工效率监控、绩效评价、无用户授权的数据分析.

ClawHub Agent Skills author: nopedijah v1.0.0 MIT-0 6 files body ≈ 1 834 tokens Open the sourceclawhub.ai analyzed 3 d ago

像火眼金睛一样看穿日常工作痕迹里的隐形重复摩擦,把日程、待办、文档、表格、审批、 邮件和消息等泥沙放进 SOP 炼丹炉,提炼出值得沉淀的自动化 SOP 金丹、Skill 需求卡片、 日报草稿和明日待办。支持钉钉/悟空 dws,也可适配 Google Workspace 等工作数据源。 Use when user…

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureNotionPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
D
47/100
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

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 47/100

    • 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
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 57 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1834 tokens
    • 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

    • +3Output format is not stated: the model decides each time
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 355: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 57 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This skill is a disclosed productivity-analysis assistant that uses authorized workplace connectors and requires confirmation before writing or sending anything.
    LLM: benign (high) · VirusTotal: · 2 Jun 2026