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 open-octo/octo-agent --skill brand-voicegit clone --depth 1 https://github.com/open-octo/octo-agentWrote 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/open-octo/octo-agent/brand-voice)<a href="https://agentmods.dev/skills/open-octo/octo-agent/brand-voice"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/brand-voice/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/open-octo/octo-agent/brand-voice"><img src="https://agentmods.dev/badge/skills/open-octo/octo-agent/brand-voice.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00200 | $0.01896 |
| Opus 5 | $0.00100 | $0.00948 |
| Sonnet 5 | $0.00040 | $0.00379 |
| Haiku 4.5 | $0.00020 | $0.00190 |
Grade A, and why
brand-voice 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
品牌声音(Brand Voice)
让该专家产出的每一条文案都落在同一个声音里。核心是"声音恒定、语气可调"(Voice constant, tone flexes)——先提炼一份品牌声音指南,再把它应用到所有文案。
先判断:用户是要"定声音"还是"用声音"
- 定声音:用户还没有品牌声音指南,或想重做。走「提炼指南」。
- 用声音:用户已有指南(本会话生成过,或提供了),只想按它写、按它改。走「应用指南」。
- 校准某一条:用户给出具体文案,问"这是不是我们的调性"。走「应用指南」+「校验」。
不知道时说清楚,别默认。用户只是随口说"写得有调性一点"时,先快速提炼一份轻量指南再写。
提炼指南(生成品牌声音指南)
1. 收集素材
从已有的东西里提炼,不要凭空造。按优先级问/拿:
- 已有的、用户喜欢的文案:过去写得好、用户认可的内容(社媒帖、广告语、产品介绍、Slogan)。
- 产品/品牌资料:产品定位、目标人群、价格档位、竞品。
- 语气形容:用户用形容词描述想要的调性("专业但不冷""亲和""高级""接地气"),以及明确不要的("别太emo")。
- 说话的人:这声音是"一个人"还是"一个品牌"?用第一人称吗?(决定口吻来源。)
如果用户懒得多给,就从当前对话/上下文 + 产品资料里提炼一版草稿,标注「推断,可修改」,别卡住。
2. 单源提炼
有充足素材时,逐条抽出特征:
- 反复出现的用词、句长、标点习惯
- 是否用梗、emoji、英文缩写
- 是陈述型还是互动型,是"我们很牛"还是"你被看见"
- 例子:直接摘一段用户认可的原文作为"像我们"的范例
3. 合成指南
输出一份紧凑的「品牌声音指南」,固定用这个模板:
## 品牌声音指南
### 一句话定位
[用一句话说清这个声音像谁。例:一个懂数据、说话不绕弯的资深顾问,专业但不端着。]
### We Are / We Are Not(核心人格锚点)
| 我们是 | 我们不是 |
|--------|----------|
| [例:专业] | [例:冷冰冰/爹味] |
| [例:真诚] | [例:套路/夸大] |
| [例:利落] | [例:啰嗦/堆砌] |
| [例:有温度] | [例:过度emo/卖惨] |
### 声音恒定 vs 语气可调
- **恒定(Voice)**:人格、价值观、We Are / We Are Not、禁用语——**所有平台、所有内容都一样**。
- **可调(Tone)**:正式度、能量、专业深度——**随平台和受众变化**。
### 语气-场景矩阵(Tone by Context)
| 场景 | 正式度 | 能量 | 专业深度 | 示例句 |
|------|--------|------|----------|--------|
| 小红书种草 | 低 | 高 | 低 | "[示例]" |
| 公众号长文 | 中 | 中 | 中 | "[示例]" |
| 商务邮件/提案 | 高 | 中 | 高 | "[示例]" |
| 短视频口播钩子 | 低 | 高 | 低 | "[示例]" |
### 术语与措辞
- **偏好用词**:[例:说"体验"不说"使用",说"帮你"不说"我司"]
- **禁用词**:[例:遥遥领先、赋能、打造闭环、震惊体、绝对最强]
### 范例(最像我们的文字)
> [摘一段用户认可的原稿,作为"像我们"的黄金样本]
每条 We Are 特征尽量配一句证据/来源;没有证据就标注「推断」。
4. 不确定的,写进"待定问题"
无法从素材判断的,别硬编进指南。单列一节:
### 待定问题(Open Questions)
1. **甜度/梗的浓度**
- 发现:素材里梗很多,但不确定产品调性是否允许
- 建议:先按「轻量梗 + 克制」处理
- 需要你定:正式 vs 俏皮?
每条待定都要附一个建议默认值,让用户"确认或推翻",不留下死路。
应用指南(把声音套到文案上)
1. 先定义这次任务
写之前明确:内容类型(社媒/公众号/邮件/标题)、受众(谁看、什么段位)、核心信息(要点)、形式约束(字数/篇幅/是否要 emoji)。
2. 套用恒定的声音
- 读一遍「We Are / We Are Not」,所有内容贴着"我们是"写,避开"我们不是"。
- 用偏好用词,绝不碰禁用词。
- 让人格贯穿全文,不虎头蛇尾。
3. 按场景调节语气
查语气-场景矩阵,设定本次的正式度/能量/专业深度。调整的是语气强度,不是人格身份——同一段话可以更正式或更俏皮,但"是谁在说话"不变。
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.
- 8d ago First seen · 137 lines · 200 tokens per session scan A a87c54f055a8
brand-voice is a skill published in the GitHub repository open-octo/octo-agent (99 stars, last pushed yesterday), licensed MIT. It adds 200 tokens to every session and 1,896 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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