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sales-verbit销售动词

Agent Skill

sales-verbit 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

289

周安装

8

GitHub Stars

13

下载量

65
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sales-verbit(销售动词)
来源仓库:https://github.com/sales-skills/sales
仓库路径:skills/sales-verbit
安装命令:
npx skills add https://github.com/sales-skills/sales --skill sales-verbit
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/sales-skills/sales --skill sales-verbit

简介

sales-verbit 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 适用于销售动词相关的代码协作、仓库管理和 Issue 处理等 GitHub 工作流任务。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 核验具体用法。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • 可结合来源仓库路径 skills/sales-verbit 继续核验功能细节和使用边界。

SKILL.md

name
sales-verbit
description
Verbit platform help — enterprise AI+human transcription, live captioning, and accessibility (ADA/WCAG/CVAA) for education, legal, media, and government. Use when setting up Verbit live captioning for Zoom or Teams events, uploading recorded audio for human-verified transcription, integrating the Verbit API (Insights, Live Booking, Post-Production, Search) into a transcript pipeline, comparing Verbit vs Rev vs 3Play Media vs Sonix for enterprise transcription, troubleshooting ASR punctuation or speaker diarization errors, choosing between self-service and enterprise plans, or configuring SmartPlayer for accessible video playback. Do NOT use for picking a sales meeting note-taker (use /sales-note-taker) or reviewing a call for coaching (use /sales-call-review).
argument-hint
[describe what you need help with in Verbit]
license
MIT
version
1.0.0
tags
[sales, transcription, captioning, accessibility, enterprise, platform]
github
https://github.com/verbit-ai

Verbit Platform Help

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated platform knowledge.

  1. What's your primary goal?

- A) Set up live captioning for events, lectures, or meetings - B) Transcribe recorded audio/video files (post-production) - C) Integrate the Verbit API into a transcript pipeline - D) Compare Verbit to alternatives for my use case - E) Configure Gen.V AI insights (summaries, keywords, quizzes) - F) Troubleshoot transcription accuracy or speaker labeling issues

  1. What vertical are you in?

- A) Higher education (lectures, courses, LMS) - B) Legal (depositions, court proceedings, compliance) - C) Media & entertainment (subtitles, dubbing, localization) - D) Corporate / government (meetings, town halls, events) - E) Other

  1. Which plan are you on (or considering)?

- A) Self-Service ($29/mo, 20 hrs AI transcription) - B) Enterprise (custom, human-verified) - C) Not sure yet

Skip-ahead rule: if the user's prompt already contains enough context, skip to Step 2.

Step 2 — Route or answer directly

Problem domainRoute to
Choosing a live sales meeting note-taker/sales-note-taker {user's question}
Reviewing a specific call for coaching/sales-call-review {user's question}
Building a coaching program from call data/sales-coaching {user's question}
General CRM-to-tool integration (Zapier, webhooks, iPaaS)/sales-integration {user's question}
Comparing batch transcription tools (Sonix, TranscribeMe)/sales-note-taker {user's question}

When routing, provide the exact command.

Step 3 — Verbit platform reference

Read references/platform-guide.md for the full platform reference — modules, pricing, integrations, data model, workflows.

If the question involves the API, also read references/verbit-api-reference.md.

Answer the user's question using only the relevant section. Don't dump the full reference.

Step 4 — Actionable guidance

You no longer need the platform guide — focus on the user's specific situation.

Plan selection: Self-Service ($29/mo) for occasional AI-only transcription up to 20 hrs/mo. Enterprise for human-verified accuracy, compliance (HIPAA BAA, SOC 2), SLA guarantees, and volume pricing. No mid-market tier exists — if you need more than 20 hrs/mo but don't need enterprise contracts, compare Sonix ($10/hr PAYG) or Rev ($0.25/min AI).

API selection: Use Post-Production API for batch upload workflows. Use Live Booking API for scheduled live captioning events. Use Insights API (Gen.V) for AI-generated summaries, keywords, and quizzes from completed transcripts.

Accuracy optimization: Upload highest-quality audio. Request custom ASR model tuning on Enterprise. Use human review tier for legal/compliance content where 99%+ accuracy is required.

If you discover a gotcha, workaround, or tip not covered in references/learnings.md, append it there.

Gotchas

*Best-effort from research — review these, especially items about plan-gated features and integration gotchas that may be outdated.*
  • No mid-market pricing tier. Self-Service is $29/mo (20 hrs AI-only). Enterprise is custom (~$33K-$75K/yr). Nothing in between — mid-size teams are stranded.
  • Self-Service is AI-only transcription. No human review, no custom ASR models, no compliance certifications. Those require Enterprise.
  • ASR punctuation and grammar errors are common. Unnecessary spaces before punctuation, run-on sentences, missed capitalization — review and edit output before publishing.
  • Speaker diarization splits speakers incorrectly. Multiple speakers grouped under one label or one speaker split into several — worse with similar voices or cross-talk.
  • Bearer tokens expire after 24 hours. API integrations must handle token refresh. Post-Production API alternatively supports API key auth (simpler for server-to-server).
  • Gen.V Insights require completed transcripts. You can't generate summaries or keywords until transcription processing finishes.
  • Rate limits are not published. Design with conservative throttling and exponential backoff. Monitor for 429 responses.
  • VITAC (Verbit subsidiary) has separate quality issues. If you're evaluating broadcast captioning, VITAC reviews cite inconsistent quality and missed deliverables.

Related skills

  • /sales-note-taker — Comparing AI meeting note-takers and conversation intelligence platforms, or choosing batch transcription tools (Sonix, TranscribeMe, Rev)
  • /sales-sonix — Sonix platform help (batch AI transcription, translation, subtitles)
  • /sales-transcribeme — TranscribeMe platform help (human+AI hybrid transcription)
  • /sales-integration — General tool integration patterns (Zapier, webhooks, iPaaS)
  • /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do

Examples

Example 1: Comparing Verbit vs Sonix for education transcription

User says: "I need to transcribe 500 hours of lecture recordings per semester for accessibility compliance. Should I use Verbit or Sonix?" Skill does:

  1. Notes the volume (500 hrs/semester) and compliance requirement (ADA/WCAG)
  2. Compares: Sonix is $10/hr PAYG ($5K total) with AI-only output. Verbit Enterprise offers human-verified transcription with ADA/WCAG compliance certifications and LMS integrations (Blackboard, Canvas).
  3. Recommends Verbit Enterprise if accuracy and compliance documentation matter. Sonix if budget is primary and human review can be done in-house.
  4. Notes Verbit's Captivate ASR can be custom-tuned for academic terminology on Enterprise.

Result: Clear comparison based on volume, compliance needs, and budget.

Example 2: Setting up live captioning for Zoom events

User says: "I need live captions for our weekly town hall on Zoom — about 200 attendees." Skill does:

  1. Reads the platform guide for Live Booking API and Zoom integration details
  2. Explains Verbit integrates natively with Zoom for live captioning — no separate bot needed
  3. Walks through booking flow: create order via Live Booking API or dashboard, specify Zoom meeting ID, select captioning profile
  4. Notes human captioners are available for Enterprise plans (higher accuracy) vs AI-only for Self-Service

Result: Step-by-step setup for Zoom live captioning with plan-appropriate options.

Example 3: Building a post-production transcript pipeline

User says: "I want to automatically transcribe every recording uploaded to our S3 bucket using the Verbit API." Skill does:

  1. Reads the API reference for Post-Production endpoints
  2. Sketches the pipeline: S3 event → Lambda → POST /job/new + POST /job/add_media (S3 URL) + POST /job/perform_transcription → poll GET /job/info for status → GET /job/get_caption for completed transcript
  3. Notes API key auth is simpler for server-to-server (vs OAuth Bearer tokens with 24hr expiry)
  4. Flags: no webhook for job completion documented — must poll status endpoint

Result: Working pipeline architecture with Verbit-specific API flow.

Troubleshooting

ASR output has punctuation and grammar errors

Symptom: Transcripts have extra spaces before periods, missing capitalization, run-on sentences Cause: Verbit's Captivate ASR engine produces raw output that needs post-processing — common across all ASR engines but more noticeable in Verbit's output per user reports Solution: Use the Verbit editor to clean up output. On Enterprise, request custom ASR model tuning for your domain vocabulary. For critical content (legal, compliance), use human review tier instead of AI-only.

Speaker labels are wrong or missing

Symptom: Two speakers labeled as one, or one speaker split into 5+ labels Cause: Speaker diarization struggles with similar voices, cross-talk, poor audio quality, and single-channel (mono) recordings Solution: Upload stereo/multi-channel audio when possible. Review and merge/rename speakers in the editor. On Enterprise, request custom speaker model training. For critical content, use human review.

Post-Production API job stuck in processing

Symptom: Job status stays "processing" for hours after upload Cause: Large files, queue backlog, or unsupported format. Human review tier has longer turnaround (hours to days, not minutes). Solution: Check file format is supported (MP3, WAV, MP4, M4A, FLAC, OGG). For large files, use URL-based upload instead of direct upload. Check Verbit status page for service issues. On Enterprise, contact support for priority processing. AI-only jobs should complete in minutes — if not, the file may have processing errors.

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02

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03

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.88%
按下载量换算24

Claude

29.2%
按下载量换算19

Cursor

15.7%
按下载量换算10

Gemini CLI

9.69%
按下载量换算6

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