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AD rollinggo-searchhotel

使用 RollingGo CLI 查询酒店信息、筛选结果、读取酒店标签和获取房型价格。当用户需要按目的地 / 日期 / 星级 / 预算 / 标签 / 距离搜索酒店、查看酒店详情与房型报价，或读取酒店标签库时触发本技能。触发短语——"搜索酒店"、"查酒店"、"酒店详情"、"房型价格"、"酒店标签"、"附近酒店"、"rollinggo"。

ClawHub Agent Skills author: zlon v1.0.1 MIT-0 5 files body ≈ 720 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructuretype 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
41/100
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

The same skill appears in 3 more places: ClawHub, ClawHub, ClawHub

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: 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 "homepage"

Process rating: all ten parameters 41/100

  • 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 (rollinggo-searchhotel) differs from the folder (hotel-recommendation)
  • 60Tools and files. Uses tools (python, node) that frontmatter does not declare
  • 100Steps. 28 steps
  • 100Execution cost. Instruction body is 720 tokens
  • 100Running it twice. No mutating operations
  • low 10 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 168: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

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

External checks

ClawHub: clean
This skill is a disclosed hotel-search integration that uses a RollingGo API key and CLI, with some documentation issues users should handle carefully.
LLM: benign (high) · VirusTotal: · 5 Aug 2026