SKILLEMALL.ai
RU Sign in

BC cleaning-assistant

智能保洁全流程管理助手。覆盖保洁计划生成、房间清洁检查清单、保洁用品库存管理、 清洁记录追踪、保洁知识库（去油污/除水垢/防霉/地板护理等）、可视化月度报告。 自然语言录入保洁任务，本地SQLite存储，交互式HTML报告。 触发词：保洁, 打扫卫生, 大扫除, 清洁计划, 清洁清单, 保洁用品, 清洁技巧, 去油污, 除霉, 擦窗, 拖地, 清洁报告, 今天打扫, 家务, 做卫生。

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 5 files body ≈ 1 034 tokens Open the sourceclawhub.ai↗ analyzed 20 h ago

智能保洁全流程管理助手。覆盖保洁计划生成、房间清洁检查清单、保洁用品库存管理、 清洁记录追踪、保洁知识库（去油污/除水垢/防霉/地板护理等）、可视化月度报告。 自然语言录入保洁任务，本地SQLite存储，交互式HTML报告。 触发词：保洁, 打扫卫生, 大扫除, 清洁计划, 清洁清单, 保洁用品, 清洁技巧…

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

ProcedureSoftware developmenttype 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
B
86/100
safety, quality, tests
Safety 60%
95
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Bash WebFetch

Files scanned: 5. 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 "display_name"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "location"
  • note frontmatter-key unknown frontmatter key "agent_created"

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
  • 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
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1034 tokens
  • 100Running it twice. No mutating operations
  • 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 193: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (18 code blocks)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This is a coherent local cleaning-management skill, but users should know it may save cleaning activity from broad natural-language triggers and its HTML reports load Chart.js from a CDN.
LLM: benign (medium) · VirusTotal: · 20 Jun 2026