SKILLEMALL.ai

AC scheduled-voice-briefing

General-purpose skill for turning natural language requests into scheduled voice notifications and structured briefings. Use when the user wants to create, update, pause, resume, or remove recurring or one-time voice briefing tasks; configure timing, modules, language, voice, or tone in natural language; or manage scheduled spoken summaries such as reminders and daily briefings. This public package focuses on configuration, structure, runtime integration guidance, and production-like examples. It does not bundle provider-specific TTS implementations, does not include built-in API credentials, and does not access local files, system resources, or user data without explicit input. / 通用型 Skill,用于将自然语言请求转换为定时语音通知与结构化播报。适用于创建、修改、暂停、恢复或删除一次性或周期性语音播报任务,并支持通过自然语言配置时间、模块、语言、声音与语气。本公开包聚焦于配置结构、运行时集成说明和更接近真实使用场景的示例,不内置厂商特定的 TTS 实现,不包含内置 API 凭证,也不会在未明确输入的情况下访问本地文件、系统资源或用户数据。

ClawHub Agent Skills author: sunxq1017 v1.0.4 MIT-0 7 files body ≈ 1 281 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
64/100
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 · 0

    ✓ No critical or high findings

    Files scanned: 7. 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 64/100

    • 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
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 84 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1281 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 874: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 84 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This skill appears to be a straightforward local template and translation helper with no hidden network, credential, persistence, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026