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BD wevoicereply

【自动化语音合成与推送链路】 当用户要求语音回复、读一下或发声时，必须严格执行以下三步，严禁跳步： ### 第一步：文案生成 (Prompt A) 根据上下文生成自然、温暖的口语化文本。 请在长句子中加入中文逗号 `，` 以确保音频合成时有自然的停顿。 ### 第二步：音频合成 (执行 voice_reply_skill.py) **这是本工具的核心动作。** 将第一步生成的文本作为 `text` 参数传入，后台将自动运行 `voice_reply_skill.py` 脚本。 - **脚本逻辑**：Piper TTS 合成 WAV -> FFmpeg 转码 AMR -> 上传获取 URL。 - **产出物**：执行完成后，你会获得一个 JSON 对象，包含 `url` 字段（例如：`{"url": "https://.../xxx.amr"}`）。 ### 第三步：指令发送 (调用系统内置工具) 获取 URL 后，禁止结束对话，立即发起第二次调用。 - **目标工具**：`default_api.message` - **执行参数**： { "action": "send", "contentType": "voice", "media": "[此处填入从第二步脚本返回的url]", "target": "@all" } 【准则】发送完成后，仅需告知用户“语音已发送”，不得再次输出音频 URL。

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 59 tokens Open the sourcegithub.com↗ analyzed 3 d ago

【自动化语音合成与推送链路】 当用户要求语音回复、读一下或发声时，必须严格执行以下三步，严禁跳步： 第一步：文案生成 (Prompt A) 根据上下文生成自然、温暖的口语化文本。 请在长句子中加入中文逗号 ， 以确保音频合成时有自然的停顿。 第二步：音频合成 (执行 voicereplyskill.py)…

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype 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
B
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
52
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "parameters"

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 75Steps. 3 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 59 tokens
  • 100Running it twice. No mutating operations

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)
  • +4Structure: 2 headings, hard to scan
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -15SKILL.md body under 300 characters: nearly empty
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 631: enough signal without eating the budget
  • +3Step-by-step instructions: 3 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 52.