SKILLEMALL.ai

AC rental-helper

租房助手 - 帮助用户记录房源信息、计算租房预算、生成对比表格、提供租房避坑指南、智能推荐房源、批量导入、网页解析、图片识别、网站抓取。使用场景:(1) 记录和筛选房源信息 - 说"记录一个新房源"或"查看我的房源列表";(2) 计算租房预算 - 说"帮我算一下租房预算"或"这个房子每月要花多少钱";(3) 生成租房对比表格 - 说"对比一下这几个房源"或"生成房源对比表";(4) 租房避坑指南 - 说"租房要注意什么"或"有什么避坑建议";(5) 智能推荐房源 - 说"给我推荐几套房源"、"我公司在xxx,给我推荐走路10分钟能到的房源,价格在xx以内"、"我要租房,位置在xx,给我推荐附近3KM,离地铁或公交车站比较近的房源";(6) 看房记录 - 说"我在看房,想记录每个房子的优缺点";(7) 批量导入 - 说"批量导入房源";(8) 网页解析 - 说"帮我解析这个链接";(9) 图片识别 - 说"从这张图片提取房源信息";(10) 网站抓取 - 说"从贝壳/链家/58同城/安居客抓取房源"。

ClawHub Agent Skills author: sosshuai v1.3.0 MIT-0 18 files body ≈ 988 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
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

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: 18. 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 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. No external tools needed
  • 100Steps. 62 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 988 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -33 of 15 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 16 example trigger phrases
  • +3Description length 456: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 62 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
This rental helper is mostly purpose-aligned, but some data-fetching features can present sample listings as real and several workflows save sensitive rental details with limited disclosure or confirmation.
LLM: suspicious (high) · VirusTotal: · 29 May 2026