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AC google-autoreply

Onboard a merchant into the Auto Google Review Reply product: register/login, connect a Google Business Profile, benefit from a 5-day free trial (membership check, no forced payment), and configure AI auto-reply. Invoke when the user wants to set up an account, connect Google Business, check membership/ trial status, or enable AI review auto-reply.

ClawHub Agent Skills author: cheaplightstore v1.0.0 MIT-0 2 files body ≈ 4 251 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Onboard a merchant into the Auto Google Review Reply product: register/login, connect a Google Business Profile, benefit from a 5-day free trial (membership…

As a process C 60/100 · Has gaps — weak spots: result and completion, running it twice

AnalyzerStripeSoftware developmentWriting and documentstype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Running it twice w 4
30
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: google-autoreply (ClawHub)

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 · 0

    ✓ No critical or high findings

    Files scanned: 2. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 22 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4251 tokens
    • 85Steps. 66 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (11 tags): a typed call is more reliable

    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 350: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 66 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    The skill matches its onboarding purpose, but it should be reviewed because it exposes long-lived account tokens through URLs and persistent storage while controlling business-account setup.
    LLM: suspicious (high) · 8 Sept 2026