SKILLEMALL.ai

AB ivr-voice-pack

Build a labeled IVR voice pack for a phone tree: welcome, menu, hold, transfer, after-hours, and error prompts in one consistent brand voice. This phone tree voice studio and hotline voiceover shop records about twelve default prompts as a voice menu you can drop into the switch. Use it for IVR prompts, call center voice, auto attendant audio, and customer-service phone menus.

ClawHub Agent Skills author: beatra-ai v0.1.2 MIT-0 14 files body ≈ 1 801 tokens Open the sourceclawhub.ai analyzed 3 d ago

Build a labeled IVR voice pack for a phone tree: welcome, menu, hold, transfer, after-hours, and error prompts in one consistent brand voice.

As a process B 66/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

GeneratorContact centreAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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: 14. 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 66/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 3 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 22 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1801 tokens
    • low The skill ranks results itself: that belongs to the system behind the tool, not the model

    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
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 379: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (8 of 8)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    The voice-pack workflow is legitimate, but the skill requests broad account and spending permissions and silently self-updates local code by default, so it needs Review before installation.
    LLM: suspicious (high) · 6 Sept 2026