guideline-generation

작성자: anthropic

브랜드 문서, 영업 통화 기록, 발견 보고서 또는 직접 사용자 입력 등 다양한 출처를 바탕으로 포괄적이고 LLM에 최적화된 브랜드 보이스 가이드라인을 생성합니다. 원자료를 신뢰도 점수와 미해결 질문이 포함된 구조화되고 실행 가능한 가이드라인으로 변환합니다.

npx skills add https://github.com/anthropics/knowledge-work-plugins --skill guideline-generation

Guideline Generation

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.

Inputs

Accept any combination of:

  • Discovery report from the discover-brand skill (structured, pre-triaged)
  • Brand documents uploaded or from connected platforms (PDF, PPTX, DOCX, MD, TXT)
  • Conversation transcripts from Gong, Granola, manual uploads, or Notion meeting notes
  • Direct user input about their brand voice and values

When a discovery report is provided, use it as the primary input — sources are already triaged and ranked. Supplement with additional analysis as needed.

Generation Workflow

1. Identify and Classify Sources

Determine what the user has provided. If no sources are available:

  • Check if a discovery report exists from a previous /brand-voice:discover-brand run
  • Check .claude/brand-voice.local.md for known brand material locations
  • Suggest running discovery first: /brand-voice:discover-brand

2. Process Sources

For 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.

3. Synthesize Into Guidelines

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 AreWe 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:

  • Voice = personality, values, "We Are / We Are Not" — constant across all content
  • Tone = formality, energy, technical depth — flexes by context

Tone-by-Context Matrix:

ContextFormalityEnergyTechnical DepthExample
Cold outreachMediumHighLow"[example phrase]"
Enterprise proposalHighMediumHigh"[example phrase]"
Social mediaLowHighLow"[example phrase]"

4. Assign Confidence Scores

Score each section using the methodology in references/confidence-scoring.md:

  • High confidence: 3+ corroborating sources, explicit guidance found
  • Medium confidence: 1-2 sources, or inferred from patterns
  • Low confidence: Single source, inferred, or conflicting data

5. Surface Open Questions

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.

6. Quality Check

Before presenting, verify via the quality-assurance agent (defined in agents/quality-assurance.md):

  • All major sections populated (including Brand Personality and Content Examples if sources support them)
  • At least 3 voice attributes with evidence
  • "We Are / We Are Not" table has 4+ rows
  • Tone matrix covers at least 3 contexts
  • Confidence scores assigned per section
  • Source attribution for all extracted elements
  • No PII exposed
  • Open questions include recommendations

7. Present and Offer Next Steps

Summarize key findings:

  • Total sections generated with confidence breakdown
  • Strongest voice attribute and most effective message
  • Number of open questions (if any)

8. Save for Future Sessions

The default save location is .claude/brand-voice-guidelines.md inside the user's working folder.

Important: The agent's working directory may not be the user's project root (especially in Cowork, where plugins run from a plugin cache directory). Always resolve the path relative to the user's working folder, not the current working directory. If no working folder is set, skip the file save and tell the user guidelines will only be available in this conversation.

  1. Resolve the save path. The file MUST be saved to .claude/brand-voice-guidelines.md inside the user's working folder. Confirm the working folder path before writing.
  2. Check if guidelines already exist at that path
  3. If they exist, archive the previous version: Rename the existing file to brand-voice-guidelines-YYYY-MM-DD.md in the same directory (using today's date)
  4. Save new guidelines to .claude/brand-voice-guidelines.md inside the working folder
  5. Confirm to the user with the full absolute path: "Guidelines saved to <full-path>. /brand-voice:enforce-voice will find them automatically in future sessions."

The guidelines are also present in this conversation, so /brand-voice:enforce-voice can use them immediately without loading from file.

After saving, offer:

  1. Walk through the guidelines section by section
  2. Start creating content with /brand-voice:enforce-voice
  3. Resolve open questions

Privacy and Security

Enforce these privacy constraints throughout the entire generation workflow, not only at output time:

  • Redact customer names and contact information from all examples
  • Anonymize company names in transcript excerpts if requested
  • Flag any sensitive information detected during processing

Reference Files

  • references/guideline-template.md — Complete output template with all sections, field definitions, and formatting guidance
  • references/confidence-scoring.md — Confidence scoring methodology, thresholds, and examples

anthropic의 다른 스킬

analyzing-financial-statements
anthropic
이 스킬은 재무제표 데이터로부터 투자 분석을 위한 주요 재무 비율과 지표를 계산합니다.
applying-brand-guidelines
anthropic
이 스킬은 생성된 모든 문서에 일관된 기업 브랜딩과 스타일(색상, 글꼴, 레이아웃, 메시징 포함)을 적용합니다.
creating-financial-models
anthropic
이 스킬은 DCF 분석, 민감도 테스트, 몬테카를로 시뮬레이션, 시나리오 플래닝을 포함한 고급 재무 모델링 제품군을 투자…에 제공합니다.
board-minutes
anthropic
이사회 또는 위원회 회의록을 사내 형식으로 작성합니다. 캘린더에서 예정된 이사회 및 위원회 회의를 자동으로 감지하고, 안건을 요청한 후…
crm-cleanup
anthropic
HubSpot에서 오래된 거래, 중복 연락처, 누락된 필드를 스캔한 후 소유자가 승인한 항목을 수정합니다. 선택적 범위 인수를 받아 거래, 연락처 등을 지정할 수 있습니다.
redshift-api
anthropic
Amazon Redshift에 대해 SQL 실행 — 명령문 제출, 상태 폴링, 결과 페이지 탐색, 데이터베이스/스키마/테이블 탐색. 사용자가 원할 때마다 이 기능을 사용하세요…
ticket-deflector
anthropic
고객이 전달한 이메일이나 티켓을 읽고, PayPal에서 주문/환불 상태를 가져오며, HubSpot에서 계정 내역을 조회한 후, 소유자의 어조에 맞춰 답변을 작성합니다.
reg-feed-watcher
anthropic
규제 피드를 지금 확인하고, 마지막 확인 이후 새로 추가된 내용을 사용자의 중요도 기준에 따라 필터링하여 보고합니다. 사용자가 "피드 확인해 줘"라고 말할 때 사용하세요.