git clone https://github.com/AgriciDaniel/claude-blog.git--- name: blog-brand description: > Establish durable brand and voice context for cross-skill consumption. Generates BRAND.md (audience, positioning, do/don't editorial rules, taboo phrases, competitor differentiation) and VOICE.md (existing persona JSON re-expressed as readable prose), both written to the project root. When present, all blog sub-skills auto-load these files before writing or reviewing. Pairs with blog-persona, which manages the structured persona JSON. Use when user says "blog brand", "create brand context", "brand voice doc", "BRAND.md", "VOICE.md", "establish editorial brand", "brand guidelines for blog". user-invokable: true argument-hint: "[init|show|update]" license: MIT --- # Blog Brand: Durable Editorial Context Generates two project-root files that every blog sub-skill auto-loads when present: - `BRAND.md`: who the audience is, what the brand stands for, what to never say - `VOICE.md`: how the brand sounds, structurally and lexically These are the editorial equivalent of impeccable's PRODUCT.md / DESIGN.md pattern: persistent context that survives across sessions and propagates to every command. ## Why this exists Today, persona JSON (from `blog-persona`) is loaded by some skills and not others. Topic-cluster context lives inside cluster vaults. Competitor positioning lives nowhere. Each blog command re-derives "what is the brand" from whatever context it has. `BRAND.md` and `VOICE.md` fix this: one canonical source, loaded by the `blog` orchestrator at the start of every command. When neither file exists, behavior is unchanged from v1.7.1. Backward compatible. ## Commands | Command | Purpose | |---|---| | `/blog brand init` | Interactive interview, writes BRAND.md and VOICE.md to project root | | `/blog brand show` | Display current contents (or report missing) | | `/blog brand update` | Re-run the interview with current values as defaults | ## Init Workflow Run the 5-step interactive interview. Ask each step, wait for response, then proceed. If `blog-persona` JSON already exists, pre-fill voice answers from it. ### Step 1: Audience Ask: - **Primary audience role** (e.g. "head of marketing at a 50-500 person B2B SaaS") - **Secondary audience** (optional) - **Reader expertise level**: beginner / intermediate / advanced / mixed - **Problems the reader is actively trying to solve** (3 to 5 bullets) - **Common misconceptions the audience holds** (used to anchor information gain) ### Step 2: Positioning Ask: - **One-sentence brand mission** (what the brand helps people do) - **Distinctive point of view** (the contrarian or non-obvious belief that shapes content) - **What this brand is NOT** (anti-positioning, what to never be confused with) - **Top 3 direct competitors** with the one-line differentiator vs each ### Step 3: Editorial rules Ask: - **Do list** (3 to 7 things the blog will always do; e.g. "cite primary sources only," "name the practitioner not the product") - **Don't list** (3 to 7 things the blog will never do; e.g. "no clickbait titles," "no listicle filler") - **Taboo phrases** (specific words or phrases this brand never uses; complements but is separate from the AI-detection blocklist) - **Required disclosures** (e.g. affiliate disclosure, AI-content disclosure, conflict-of-interest patterns) ### Step 4: Topic boundaries Ask: - **Topics fully in scope** (core content pillars) - **Topics partially in scope** (adjacent; covered only with original angle) - **Topics out of scope** (will not cover; redirect to partner content) - **Recurring formats / column names** if any (e.g. "Monthly Field Notes," "Reader Q&A") ### Step 5: Voice (auto-fill from blog-persona if present) Ask: - **Pronoun stance**: first-person (we / I), second-person (you), third-person (the team), or mixed - **Acceptable contractions**: full / partial / none - **Sentence ceiling**: max words per sentence as a hard cap - **Paragraph ceiling**: max words per paragraph (default 150) - **Headline patterns to favor**: numbered / question / promise / statement - **Headline patterns to avoid**: any patterns banned for this brand - **Summary box label**: from blog-persona, or pick one ## Output Files ### BRAND.md template Write to project root as: ```markdown # Brand Context > This file is auto-loaded by all blog sub-skills. Last updated: YYYY-MM-DD. ## Audience - **Primary**: [role + context] - **Secondary**: [if any] - **Expertise**: [level] - **Active problems**: - [problem 1] - [problem 2] - [problem 3] - **Common misconceptions**: - [misconception 1] - [misconception 2] ## Positioning - **Mission**: [one sentence] - **Distinctive POV**: [contrarian or non-obvious belief] - **What we are NOT**: [anti-positioning] - **Competitors**: - [Competitor A]: [our one-line differentiator] - [Competitor B]: [our one-line differentiator] - [Competitor C]: [our one-line differentiator] ## Editorial Rules ### Always do - [rule 1] - [rule 2] - [rule 3] ### Never do - [rule 1] - [rule 2] - [rule 3] ### Taboo phrases - [phrase 1] - [phrase 2] ### Required disclosures - [disclosure rule] ## Topic Scope - **In scope**: [pillars] - **Partial scope**: [adjacent topics] - **Out of scope**: [topics to refuse] - **Recurring formats**: [if any] ``` ### VOICE.md template Write to project root as: ```markdown # Voice Context > This file is auto-loaded by all blog sub-skills. Last updated: YYYY-MM-DD. ## Pronoun stance [first-person / second-person / third-person / mixed] ## Lexical rules - **Contractions**: [full / partial / none] - **Sentence ceiling**: [N words max] - **Paragraph ceiling**: [N words max, default 150] - **Summary label**: [Key Takeaways / TL;DR / etc.] ## Headline patterns - **Favor**: [list] - **Avoid**: [list] ## Voice fingerprint (from blog-persona) - Funny vs serious: [0.0 to 1.0] - Formal vs casual: [0.0 to 1.0] - Respectful vs irreverent: [0.0 to 1.0] - Enthusiastic vs matter-of-fact: [0.0 to 1.0] ## Readability target - Audience tier: [consumer / professional / technical] - Flesch Grade: [range] - Flesch Ease: [range] ## Reference samples - [URL 1] (extracted patterns: [summary]) - [URL 2] (extracted patterns: [summary]) ``` ## Show Workflow 1. Check for `BRAND.md` and `VOICE.md` at project root. 2. If both exist, print a summary table (key sections only) and the file paths. 3. If one or both are missing, print which are missing and suggest `/blog brand init`. ## Update Workflow Same as Init, but pre-fills every answer with the current value. The user can press enter to accept or type a new value. After collecting all answers, overwrite both files with the new contents and update the `Last updated:` line. ## Integration with the blog orchestrator When `/blog write`, `/blog rewrite`, `/blog brief`, `/blog outline`, `/blog calendar`, or `/blog strategy` runs, the orchestrator (`skills/blog/SKILL.md`) checks for `BRAND.md` and `VOICE.md` at the project root. If present, the contents are injected into the system prompt for downstream agents (`blog-researcher`, `blog-writer`, `blog-seo`, `blog-reviewer`). If absent, behavior is unchanged. The orchestrator does not prompt the user to create them; they are opt-in context. ## Relationship to blog-persona | Concern | blog-persona | blog-brand | |---|---|---| | Structured persona JSON for programmatic use | Yes | No | | Readable brand context for cross-skill prompts | No | Yes | | Audience and positioning | No | Yes | | Taboo phrases and editorial don'ts | Partial (don't list) | Full (taboo + disclosures + scope) | | Competitor differentiation | No | Yes | | Topic boundaries | No | Yes | | Voice fingerprint (tone sliders) | Yes (canonical) | Mirror (read-only) | `blog-brand` does not replace `blog-persona`; it consumes it. The persona JSON remains the source of truth for tone dimensions, sentence-length distribution, and contraction frequency. `VOICE.md` mirrors the readable parts so prompts are self-contained. If no persona exists when `/blog brand init` runs, the voice questions still produce a `VOICE.md`. Users who want programmatic enforcement can run `/blog persona create` after. ## Error Handling - **Project root unclear**: ask the user where to write the files. Default is the current working directory. - **Files already exist on init**: ask whether to overwrite or run update instead. - **Persona referenced but missing**: ask whether to leave the persona reference blank or create one. - **Reader provides minimal answers**: prompt for at least 2 audience bullets and 3 editorial rules; refuse to write skeletons.
[{"step":"Define your inputs","action":"Gather the placeholders for your prompt: topic (e.g., 'AI in manufacturing'), company name (e.g., 'TechForward Solutions'), tone (e.g., 'professional yet approachable'), target audience (e.g., 'IT directors at mid-sized firms'), and core messaging pillars (e.g., 'Innovation, Efficiency, Trust').","tip":"Use your company’s brand guidelines or mission statement to fill these in accurately. For tone, ask yourself: 'How would our CEO describe our brand voice?'"},{"step":"Generate the outline","action":"Paste the completed prompt into your AI tool (e.g., Claude, ChatGPT) and run it. Review the output for alignment with your brand voice and audience needs.","tip":"If the outline feels generic, refine the tone description or add specific examples of your company’s messaging (e.g., 'avoid jargon like \"synergy\" and use \"teamwork\" instead')."},{"step":"Customize the content","action":"Expand the outline into a full draft using the sections and key takeaways. Add data, case studies, or quotes that reflect your company’s actual work or partnerships.","tip":"Use tools like Google Trends or industry reports to source credible statistics. For case studies, highlight metrics that resonate with your audience (e.g., cost savings, time reduction)."},{"step":"Optimize for SEO and brand alignment","action":"Run the draft through an SEO tool (e.g., SurferSEO, Clearscope) to refine keywords. Ensure the tone and messaging match your brand’s style guide.","tip":"Add internal links to relevant blog posts or product pages to improve SEO and user engagement. For example, link to a blog post about 'data cleaning best practices' if mentioned in the outline."},{"step":"Review and refine","action":"Have a colleague or your marketing team review the draft for brand consistency, accuracy, and clarity. Use their feedback to polish the final version.","tip":"Check for consistency in terminology (e.g., if you use 'AI adoption' in the intro, avoid switching to 'machine learning integration' later)."}]
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/AgriciDaniel/claude-blog/tree/main/skills/blog-brandCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create a brand-aligned blog post outline for [TOPIC] that aligns with [COMPANY_NAME]'s voice: [TONE_DESCRIPTION, e.g., 'professional yet approachable, data-driven, and solution-oriented']. Include 5-7 sections with compelling subheadings, key takeaways for each, and a call-to-action. Use [TARGET_AUDIENCE] as the primary reader. Ensure the outline reflects [COMPANY_NAME]'s core messaging pillars: [PILLAR_1], [PILLAR_2], and [PILLAR_3].
### Blog Post Outline: 'Scaling AI Adoption in Mid-Sized Enterprises' **Company:** TechForward Solutions (voice: professional, data-driven, and solution-oriented) **Audience:** IT directors and operations managers at mid-sized manufacturing firms **Core Pillars:** Innovation, Efficiency, Trust --- #### **1. Introduction: The AI Adoption Gap in Manufacturing** **Key Takeaway:** 68% of mid-sized manufacturers are still in the early stages of AI adoption, leaving efficiency gains untapped. - Hook: Start with a relatable challenge (e.g., 'Your production line has 3 idle machines, costing $12K/day in lost output'.) - Context: Cite industry reports (e.g., McKinsey 2024) on AI’s potential in manufacturing. - Thesis: TechForward’s framework bridges the gap between hesitation and scalable AI. - **CTA Teaser:** 'By the end of this post, you’ll have a 3-step roadmap to pilot AI in your facility.' #### **2. Why AI Fails in Mid-Sized Manufacturing** **Key Takeaway:** Common pitfalls include siloed data, unclear ROI, and resistance to change. - **Subsection:** 'The Data Dilemma' – Highlight how legacy systems (e.g., ERP from 2010) create bottlenecks. - **Subsection:** 'ROI Anxiety' – Share a case study: 'Company X spent $45K on a predictive maintenance pilot and saved $210K in 6 months.' - **Visual Suggestion:** Include a simple infographic showing the 'AI Readiness Scorecard' (scale 1-10). #### **3. TechForward’s 3-Pillar Framework for AI Success** **Key Takeaway:** Our methodology combines technology, process, and culture to de-risk adoption. - **Pillar 1: Data Foundation** – 'Clean data is the bedrock. Our clients see a 40% reduction in false positives in quality control after integrating IoT sensors.' - **Pillar 2: Pilot-to-Scale Pathway** – Break down the 5-phase process (e.g., 'Phase 3: Proof of Concept → Phase 4: Scalable Deployment'). - **Pillar 3: Change Management** – Address cultural barriers with a '30-day pilot challenge' for teams. #### **4. Real-World Results: How Company Y Cut Downtime by 35%** **Key Takeaway:** A 12-month case study proving the framework’s impact. - **Challenge:** Company Y’s assembly line had 18 unplanned stops/month, costing $85K/month. - **Solution:** Implemented TechForward’s AI-driven predictive maintenance in 6 weeks. - **Results:** Reduced stops to 3/month, saving $72K/month. ROI achieved in 4 months. - **Quote:** 'We thought AI was for tech giants. This changed our mindset—and our P&L.' – Jane Doe, COO, Company Y. #### **5. Your 30-Day AI Pilot Plan** **Key Takeaway:** Actionable steps to start small and scale fast. - **Week 1:** Audit your data (use TechForward’s free 'AI Readiness Assessment'). - **Week 2:** Identify one high-impact use case (e.g., energy optimization in your facility). - **Week 3:** Partner with TechForward for a 14-day proof of concept. - **Week 4:** Measure and iterate. Share results internally to build momentum. #### **6. Conclusion: From Hesitation to Transformation** **Key Takeaway:** AI isn’t a luxury—it’s a necessity for staying competitive. - Recap the framework’s pillars and the case study’s ROI. - **Final CTA:** 'Download our free 'AI Pilot Playbook' to get started today.' (Link to gated content). - **Social Proof:** 'Join 200+ manufacturers who’ve already transformed their operations with TechForward.' (Embed a Twitter/X testimonial). --- **SEO Notes:** - Target keywords: 'AI in manufacturing,' 'predictive maintenance ROI,' 'scaling AI for mid-sized companies.' - Meta description: 'Struggling to scale AI in your manufacturing business? TechForward’s proven framework helps mid-sized firms cut costs, reduce downtime, and boost efficiency—without the tech giant price tag.'
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