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AC ai-receptionist

Your 24/7 AI front desk. Polly answers incoming calls, filters spam, takes messages, and routes important calls — so you never miss what matters.

ClawHub Agent Skills author: hi-yox v1.0.0 MIT-0 8 files · 6 scripts body ≈ 4 878 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "keywords"
    • note frontmatter-key unknown frontmatter key "permissions"
    • note frontmatter-key unknown frontmatter key "dependencies"
    • note frontmatter-key unknown frontmatter key "scripts"

    Process rating: all ten parameters 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (ai-receptionist) differs from the folder (ai-receptionist-skills)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4878 tokens
    • 100Steps. 36 steps
    • 100Failures and branches. 6 branches, has a failure section
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress

    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
    • -234 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 145: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 36 items
    • +4Has examples (13 code blocks)
    • +3All 6 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    This looks like a real PollyReach receptionist integration, but it needs Review because it handles sensitive call/message data, persistent credentials, outbound calls, and account-changing settings with under-scoped disclosure.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026