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AF rollinggo-hotel-booking

RollingGo Hotel Search & Booking Assistant. Implements the full hotel booking workflow by calling RollingGo hotel APIs. Supported scenarios: ① Search hotels by city, attraction, metro, airport, etc. ② Filter by star rating, budget, tags (pool, breakfast, family, pet-friendly) ③ Query real-time room types and prices for specific hotels ④ Compare multiple hotels ⑤ Guide users to complete booking. Triggers: Find a hotel, book a hotel, search hotels, hotel recommendation, hotel queries, nearby hotels, five-star hotels, homestays, resorts, check prices, check room types, check-in, where to stay, accommodation, rollinggo, travel accommodation, business trip accommodation, family hotels, hotels with a pool, hotels with breakfast.

ClawHub Agent Skills author: RollingGo v0.1.0 MIT-0 4 files body ≈ 3 450 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

RollingGo Hotel Search & Booking Assistant.

As a process F 40/100 · Will not run — References files that are not bundled: url, <url>, {bookingUrl}

ProcedureMarketingCommercetype 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
40/100
Will not run
References files that are not bundled: url, <url>, {bookingUrl}
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: 4. 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: {bookingUrl}
  • warning missing-ref reference to a missing file: {imageUrl}
  • note frontmatter-key unknown frontmatter key "repository"

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: url, <url>, {bookingUrl}
  • 0Tools and files. 4 referenced file(s) missing: url, <url>, {bookingUrl}
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (rollinggo-hotel-booking) differs from the folder (rollinggo-hotel-booking-global)
  • 50Failures and branches. 0 branches, has a failure section
  • 85Steps. 37 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Execution cost. Instruction body is 3450 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • high The skill tells the model to perform an irreversible action with no human approval

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 732: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 37 items
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 1 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 uses unpinned install/update paths and can create real booking orders with sensitive account data, so users should review it carefully before installing.
LLM: suspicious (high) · 22 Jul 2026