SKILLEMALL.ai

AB booking-manager

AI-powered booking manager that connects to any existing booking system and lets business owners manage appointments through their phone (Telegram, WhatsApp, etc.). Use when: setting up an AI assistant to monitor bookings, notify owners of new enquiries, confirm/reschedule/cancel appointments conversationally, send professional customer emails with calendar invites, and provide schedule summaries. Works with any data source: SQL databases (PostgreSQL, SQLite/Turso, MySQL), REST APIs (Calendly, Square, Fresha), Google Sheets, or webhooks. Triggers on: "booking assistant", "appointment manager", "manage bookings via phone", "AI receptionist", "booking notifications", "salon booking system".

ClawHub Agent Skills author: Cskar10 v1.3.2 MIT-0 6 files body ≈ 2 892 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 73/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationTelegramWhatsAppGoogle SheetsPostgreSQLPersonal productivityInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
73/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Inputs and preconditions w 11
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "repository"

    Process rating: all ten parameters 73/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 51 steps
    • 100Failures and branches. 5 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2892 tokens
    • 100Running it twice. Mutating operations check current state
    • 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

    • +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 6 example trigger phrases
    • +3Description length 697: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (11 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This skill is a disclosed booking assistant that needs booking data and email access to do its job, with privacy and setup-boundary cautions but no evidence of hidden or malicious behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026