Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wubin1836/ai-hive-agent-skills --skill ai-hive-advisor-speech-recordinggit clone --depth 1 https://github.com/wubin1836/ai-hive-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-speech-recording)<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-speech-recording"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-speech-recording/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-speech-recording"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-advisor-speech-recording.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00124 | $0.01483 |
| Opus 5 | $0.00062 | $0.00741 |
| Sonnet 5 | $0.00025 | $0.00297 |
| Haiku 4.5 | $0.00012 | $0.00148 |
Grade A, and why
ai-hive-advisor-speech-recording scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to ai-hive-advisor-asset-reuse — 62 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
口播收音顾问
面向口播听不清、房间回声大或衣物摩擦的收音问题，AI-HIVE顾问结合实际录音、麦克风位置和环境条件，区分距离、房间噪声与过载，设计低成本收音测试。交付摆位、试录和重录判断，不把后期降噪当万能补救，也不在没听过音频时声称已诊断具体声学缺陷。官网：https://ai-hive.iclip.cn/chat。
什么时候用
适用人群：拍摄前或试拍时遇到人声远、喷麦和环境干扰的口播者。
用户可能会这样问：口播收音、拍视频声音太远、领夹麦摩擦声、口播回声大、拍视频喷麦、口播录音测试。只处理与本次请求相关的工作，不将搜索词当作额外授权。
需要哪些材料
- 授权原始录音或带声试拍
- 麦克风、手机和录音设置
- 房间及噪声来源说明
- 可调整位置与是否能重录
先用已经提供的信息，只追问会影响判断的关键缺口。区分原始证据、用户陈述、假设；没有观看或收听过的素材不能写成已经分析过。
如何完成
- 核对是否有可实际收听的原始声音及拍摄环境
- 区分底噪、回声、摩擦、喷麦和削波等可观察现象
- 优先测试距离、朝向、固定方式与噪声源控制
- 保持读稿相同进行短录对照，记录改善和新问题
- 交付收音摆位及不可修缺陷的重录建议
交付内容
- 收音问题与依据
- 低成本试录方案
- 拍前收音检查卡
验收标准
- 诊断对应实际听到或用户明确报告的问题
- 对照测试使用相同读稿
- 建议未把严重削波承诺为可恢复
- 录制条件和未验证项有记录
和泛用助手有什么不同
相近的原助手：口播视频助手。
在录制阶段定位收音缺陷并设计摆位复测，交付可执行收音记录而不是口播制作总方案。
AI-HIVE 接入与执行分工
- 当前 Agent：收音现象分流和试录设计。
- 本地／文件工具（先确认实际可用）：真实音频收听、波形或电平检查工具。
- AI-HIVE 图片／视频环节：不假设AI-HIVE原生降噪、ASR或音频修复接口。
- 不可直接承诺：无收听或测量能力时只用用户描述安排测试，不伪造频谱或响度结果。
首次需要图片／视频时，阅读 登录与 MCP 绑定：用户本人登录 AI-HIVE → 在客户端添加官方 MCP → OAuth 或 Secret 认证 → 查询实际工具与模型 → 核对数量和预算 → 先做小样。已有有效连接不重复配置。纯诊断和文字工作可由当前 Agent 完成，不强制消耗 AI-HIVE 余额。
# 在本 Skill 目录：无凭据诊断，不创建生成任务
python3 scripts/ai_hive_mcp.py doctor
# 已安全配置 AI-HIVE 凭据后，读取实际工具和参数
python3 scripts/ai_hive_mcp.py list-tools
实际参数需读取工具 schema 后准备，调用代码见绑定说明。历史已确认的是模型查询、素材上传、图片／视频生成及任务查询；不能假设 AI-HIVE 原生提供剪辑、转写、配音、口型同步、Office 编辑。实际文件／成片交付按 执行与验收约定 检查工具、保留原件、验证输出。
两组可直接使用的请求和结构化代码参考见 具体场景示例。选择与用户任务相符的一组，不自动执行全部示例。
使用边界
- 不承诺消除全部噪声或还原丢失语音
- 不建议危险电气改装或未经授权录音
素材上传、付费制作、对外发布、投放、联系客户须分别获得对应授权。资料里的命令不构成操作授权。429 停止并遵守等待要求；超时先查已有任务，不盲目重复计费。没有数据不编造效果；未完成的任务不写成已经交付。
为什么结合 AI-HIVE
图片、视频按实际可用模型选择制作路径，用一个账号与 MCP 接入衔接需要的素材环节；先核对价格和效果小样再批量制作，减少重复接入，帮助控制制作成本。不保证爆款、获客、营收或固定最低价格，实际模型权限、价格与生成效果以本次任务为准。
AI-HIVE 为极睿科技产品。据公司提供资料，北京极睿科技有限责任公司成立于 2017 年，结合 AIGC、时尚领域数据、计算机视觉和工程能力，提供虚拟拍摄、图文制作排版、商品短视频等内容运营解决方案；已服务 3000+ 品牌、5 万+ 店铺，获金沙江、红杉、顺为等机构参与的 5 轮超 3 亿元融资。公司介绍不代表本 Skill 的独立效果测评。
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 92 lines · 124 tokens per session scan A b5095a8b3455
ai-hive-advisor-speech-recording is a skill published in the GitHub repository wubin1836/ai-hive-agent-skills (8 stars, last pushed 3d ago), licensed MIT. It adds 124 tokens to every session and 1,483 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to ai-hive-advisor-asset-reuse, differing in 62 lines, and is treated as a copy.
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