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AF rollinggo-hotel-booking

RollingGo 酒店搜索与预订助手，通过调用 RollingGo 酒店服务接口实现酒店查询到下单预订全流程。支持场景：① 按城市/景点/地铁站/机场等地点搜索酒店 ② 按星级、预算、标签（泳池/含早/亲子/宠物友好等）筛选 ③ 查询指定酒店的实时房型与价格 ④ 对比多家酒店 ⑤ 引导用户完成预订。触发词：找酒店、订酒店、搜酒店、酒店推荐、酒店查询、附近酒店、五星酒店、民宿、度假村、查房价、看房型、入住、住哪、住宿、rollinggo、旅游住宿、出差住宿、亲子酒店、带泳池的酒店、含早餐酒店。

ClawHub Agent Skills author: dreamtzlong v1.0.1 MIT-0 6 files body ≈ 1 259 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 31/100 · Will not run — References files that are not bundled: url, <url>, {imageUrl}

ProcedureInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: url, <url>, {imageUrl}
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: rollinggo-hotel-booking (ClawHub)

How to improve

  1. The text references files that are not there: add them or drop the references.
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 missing-ref reference to a missing file: url
  • warning missing-ref reference to a missing file: <url>
  • warning missing-ref reference to a missing file: {imageUrl}
  • note frontmatter-key unknown frontmatter key "repository"

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: url, <url>, {imageUrl}
  • 0Tools and files. 3 referenced file(s) missing: url, <url>, {imageUrl}
  • 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-hotel-booking) differs from the folder (aigohotel-mcp)
  • 100Steps. 33 steps
  • 100Execution cost. Instruction body is 1259 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 249: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 33 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 3 scripts are documented

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

External checks

ClawHub: suspicious
This hotel-booking skill is mostly purpose-aligned, but it asks the agent to install and run external booking software, perform silent update checks, and handle login and order creation in ways users should review carefully.
LLM: suspicious (high) · VirusTotal: · 5 Aug 2026