SKILLEMALL.ai

AD work-analysis-report

工作内容分析与时间分配规划全流程。用户提供有道云笔记个人Key和日常工作的取数规则,自动完成提取工作笔记、分类统计、生成工作分布分析报告、生成时间分配规划报告。适用于技术经理、项目经理等需要从工作日志中提炼工作结构并规划时间分配的岗位。触发词包括工作分析、工作分布、时间分配规划、工作总结分析、有道云工作分析、工作复盘、时间管理规划。

ClawHub Agent Skills author: ydyzzjsl v1.0.0 MIT-0 6 files body ≈ 578 tokens Open the sourceclawhub.ai analyzed 2 d ago

工作内容分析与时间分配规划全流程。用户提供有道云笔记个人Key和日常工作的取数规则,自动完成提取工作笔记、分类统计、生成工作分布分析报告、生成时间分配规划报告。适用于技术经理、项目经理等需要从工作日志中提炼工作结构并规划时间分配的岗位。触发词包括工作分析、工作分布、时间分配规划、工作总结分析、有道云工作分析、工作复盘…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
49/100
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: 6. 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 "agent_created"

Process rating: all ten parameters 49/100

  • 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
  • 40Consistency. Frontmatter name (work-analysis-report) differs from the folder (work-summary-plan)
  • 100Tools and files. No external tools needed
  • 100Steps. 41 steps
  • 100Execution cost. Instruction body is 578 tokens
  • 100Running it twice. No mutating operations

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 167: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
The skill’s core purpose is coherent, but it asks for a personal cloud-note key, reads private work notes, and saves derived findings locally without enough consent and retention controls.
LLM: suspicious (high) · 15 Jul 2026