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基于品牌资料、通话或报告，自动提炼并生成可执行的品牌语调指南
复制安装指令，让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "guideline-generation" 技能： 1. 下载 https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/partner-built/brand-voice/skills/guideline-generation/SKILL.md 2. 保存为 ~/.claude/skills/guideline-generation/SKILL.md 3. 装好后重载技能，告诉我可以用了
请根据我上传的品牌手册、官网文案和宣传资料，提炼品牌语调指南。输出包括：品牌个性、语气原则、推荐用词、禁用表达、示例文案，以及适用于官网、邮件和社媒的写作规范。
一份结构清晰的品牌语调指南，覆盖语气原则、词汇建议、禁忌表达与多渠道写作规范。
请分析这些销售通话记录和会议转录，识别我们最自然、最有说服力的表达方式，并整理成品牌声音指南。请总结核心语气、常见表达模式、客户易接受的话术，以及应避免的说法。
一份基于真实对话的品牌声音指南，突出高转化表达模式和风险用语提醒。
我有一份品牌调研/Discovery 报告，请把它转化为可执行的品牌指南。请输出目标受众沟通原则、品牌定位表达、语调层级、内容风格建议，以及团队可直接使用的写作模板。
一套可落地的品牌指南，将调研结论转化为团队可直接执行的表达标准与模板。
Generate comprehensive, LLM-ready brand voice guidelines from any combination of sources — brand documents, sales call transcripts, discovery reports, or direct user input. Transform raw materials into structured, enforceable guidelines with confidence scoring and open questions.
Accept any combination of:
When a discovery report is provided, use it as the primary input — sources are already triaged and ranked. Supplement with additional analysis as needed.
Determine what the user has provided. If no sources are available:
/brand-voice:discover-brand run.claude/brand-voice.local.md for known brand material locations/brand-voice:discover-brandFor documents: Delegate to the document-analysis agent for heavy parsing. Extract voice attributes, messaging themes, terminology, tone guidance, and examples.
For transcripts: Delegate to the conversation-analysis agent for pattern recognition. Extract implicit voice attributes, successful language patterns, tone by context, and anti-patterns.
For discovery reports: Extract pre-triaged sources, conflicts, and gaps. Use the ranked sources directly.
Merge all findings into a unified guideline document following the template in references/guideline-template.md. Key sections:
"We Are / We Are Not" Table — The core brand identity anchor:
| We Are | We Are Not |
|---|---|
| [Attribute — e.g., "Confident"] | [Counter — e.g., "Arrogant"] |
| [Attribute — e.g., "Approachable"] | [Counter — e.g., "Casual or sloppy"] |
Derive attributes from the most consistent patterns across sources. Each row should have supporting evidence.
Voice Constants vs. Tone Flexes — Clarify what stays fixed and what adapts:
Tone-by-Context Matrix:
| Context | Formality | Energy | Technical Depth | Example |
|---|---|---|---|---|
| Cold outreach | Medium | High | Low | "[example phrase]" |
| Enterprise proposal | High | Medium | High | "[example phrase]" |
| Social media | Low | High | Low | "[example phrase]" |
Score each section using the methodology in references/confidence-scoring.md:
Generate open questions for any ambiguity that cannot be resolved:
## Open Questions for Team Discussion
### High Priority (blocks guideline completion)
1. **[Question Title]**
- What was found: [conflicting or incomplete info]
- Agent recommendation: [suggested resolution with reasoning]
- Need from you: [specific decision or confirmation needed]
Every open question MUST include an agent recommendation. Turn ambiguity into "confirm or override" — never a dead end.
Before presenting, verify via the quality-assurance agent (defined in agents/quality-assurance.md):
…
围绕客户问题进行多来源调研与溯源，快速整理背景并支持准确回复。
帮助你生成营销绩效报告，提炼关键指标、趋势洞察与优化建议