SKILLEMALL.ai
GitHub ↗ RU Sign in

AC google-hotels

Search Google Hotels for hotel prices, ratings, and availability using browser automation. Use when user asks to search hotels, find accommodation, compare hotel prices, check availability, or look up places to stay. Triggers include "search hotels", "find hotels", "hotels in", "where to stay", "accommodation", "hotel prices", "cheapest hotel", "best hotel", "places to stay", "hotel near", "book a hotel", "hotel ratings".

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 2 978 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Search Google Hotels for hotel prices, ratings, and availability using browser automation.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureCommerceSoftware developmentAI and agentstype 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
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
93
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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
    • low Secrets in code secret-high-entropy-token SKILL.md:58
      High-entropy token-like string (may be an id, hash or a credential)
      # Output: CAEa…ggB

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 56/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (google-hotels) differs from the folder (hotel-search)
    • 85Steps. 34 steps, 1 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 2978 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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

    • +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
    • +5Description quotes 12 example trigger phrases
    • +3Description length 425: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.