BC llm-provider-whisper-tool-pro
面向团队与企业用户的 Whisper 语音转文字工具(专业版)。核心能力: - 涵盖免费版全部能力(本地转录、翻译、多格式输出) - 批量处理:目录级递归转录,支持任务队列 - GPU 加速:CUDA / Metal / MPS 全面支持 - 说话人分离(diarization):多人对话识别 - 自定义词典:专业术语与品牌名词优化 - API 服务化:FastAPI 封装,支持远程调用 - 模型管理:多版本切换与预加载 - 质量评估:置信度分析与人工校对流程 适用场景: - 企业会议纪要自动化流水线 ...
面向团队与企业用户的 Whisper 语音转文字工具(专业版)。核心能力: - 涵盖免费版全部能力(本地转录、翻译、多格式输出) - 批量处理:目录级递归转录,支持任务队列 - GPU 加速:CUDA / Metal / MPS 全面支持 - 说话人分离(diarization):多人对话识别 -…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 257 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "edition" - note
frontmatter-keyunknown frontmatter key "tools" - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 49 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3002 tokens
- 100Running it twice. No mutating operations
- low 16 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)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +3Description length 257: enough signal without eating the budget
- +4Structure: 43 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (13 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.