blog-brief
Generate detailed content briefs for blog posts with target keywords, content outlines, competitive analysis, recommended statistics, image and chart suggestions, word count targets, internal linking architecture, template recommendations (12 types), TL;DR drafts, citation capsule planning, information gain prompts, and multi-channel distribution plans. Briefs are optimized for Google rankings and AI citations (GEO/AEO). Use when user says "content brief", "blog brief", "write brief", "outline blog", "plan blog post", "blog outline", "content outline".
What this skill does
# Blog Brief Generator: Content Planning Generates comprehensive content briefs that guide blog writing for maximum impact on both Google rankings and AI citation platforms. Reference documents: - `references/content-templates.md`: template selection criteria - `references/distribution-playbook.md`: channel-specific distribution tactics - `references/internal-linking.md`: link architecture patterns - `skills/blog/references/research-quality.md` - 5-dim quality rubric, pre-flight trap classes, freshness floors (v1.8.0; cross-skill ref lives in the orchestrator's references dir) - `skills/blog/references/synthesis-contract.md` - 6 LAWs for synthesis output (v1.8.0) ## Auto-loaded inputs (v1.8.0) When `DISCOURSE.md` is present at the project root (produced by `/blog discourse`), load it before starting brief generation. Use the discourse brief's "What's NEW" themes, "Consensus" themes, and "Contrarian takes" sections to enrich the competitive landscape and information-gain sections of this brief. Cite from DISCOURSE.md using the same inline `[name](url)` pattern. If DISCOURSE.md is absent, behavior is unchanged. ## Cross-reference For evidence-led keyword discovery, audience-avatar prompts, and content prioritization (directly upstream of brief generation), see `/blog flow find`. ## Workflow ### Step 1: Topic Intake Gather from the user: 1. **Topic or keyword** (required) 2. **Target audience** (who reads this?) 3. **Search intent**: Informational, commercial, transactional, navigational 4. **Business context**: What does the company do? What's the CTA? If only a topic is given, infer the rest from context. ### Step 2: Keyword Research Using WebSearch: 1. Search for the target keyword; analyze what currently ranks 2. Identify **primary keyword** (exact match target) 3. Identify **3-5 secondary keywords** (related terms, long-tail) 4. Identify **3-5 question queries** (People Also Ask style) 5. Note the **search intent**: what do searchers actually want? ### Step 2.5: Template Recommendation Analyze the topic, search intent, and competitive landscape to recommend one of 12 content templates. Load `references/content-templates.md` for selection criteria. **Available templates:** | Template | Best For | |----------|----------| | `how-to-guide` | Step-by-step instructional content | | `listicle` | Curated lists, ranked items, resource roundups | | `case-study` | In-depth analysis of a specific example or result | | `comparison` | Side-by-side evaluation of 2+ options | | `pillar-page` | Comprehensive topic hub linking to cluster content | | `product-review` | Detailed evaluation with pros/cons/verdict | | `thought-leadership` | Expert opinion, industry trends, predictions | | `roundup` | Expert quotes, tool collections, best-of lists | | `tutorial` | Technical walkthrough with code/config examples | | `news-analysis` | Timely coverage with expert commentary | | `data-research` | Original data, survey results, benchmark findings | | `faq-knowledge` | Question-driven reference content | **Selection process:** 1. Match search intent to template strength 2. Check what format top-ranking competitors use 3. Consider the user's available assets (data, expertise, tools) 4. Load the matching template file from `templates/[type].md` 5. Include the template name in the brief output ### Step 3: Competitive Analysis Analyze the top 3-5 ranking pages for the target keyword: 1. **Content length**: What's the average word count? 2. **Heading structure**: How many H2s? What topics do they cover? 3. **Visual elements**: Do competitors use charts, images, videos? 4. **Content gaps**: What do all competitors miss? 5. **Freshness**: How recently were they updated? 6. **Schema**: Do they use FAQ or other rich results? (Note: HowTo deprecated Sept 2023) 7. **Template pattern**: What content format do top results use? ### Step 4: Statistics Research Find 8-12 statistics the article should include: 1. Search: `[topic] study 2025 2026 data statistics research` 2. Prioritize tier 1-3 sources 3. For each stat, record: value, source, URL, date, methodology 4. Identify 2-4 stats suitable for chart visualization 5. Identify 1-2 stats suitable for TL;DR and social sharing ### Step 5: Generate the Brief Output format: ``` # Content Brief: [Title Suggestion] ## Template **Recommended**: [template-name]: [1-sentence rationale] **Template file**: `templates/[type].md` ## Target Keywords - **Primary**: [keyword]: [estimated monthly search volume if available] - **Secondary**: [keyword 1], [keyword 2], [keyword 3] - **Questions**: [question 1], [question 2], [question 3] ## Search Intent [Informational/Commercial/Transactional]: [1-2 sentence explanation of what the searcher wants] ## Content Parameters - **Word count**: [2,000-2,500] words - **Reading level**: Flesch 60-70 (expert-accessible) - **Format**: [Markdown/MDX/HTML] - **H2 sections**: [6-8] - **Images**: 3-5 from Pixabay/Unsplash - **Charts**: 2-4 via built-in blog-chart (diverse types) - **FAQ items**: 3-5 ## Recommended Title [Question-format title including primary keyword, under 60 chars] Alternative titles: 1. [Option 2] 2. [Option 3] ## Meta Description [150-160 chars, fact-dense, includes 1 statistic, ends with value proposition] ## TL;DR Draft > **TL;DR:** [40-60 word summary with key finding + 1 statistic + source. > Should be self-contained; a reader who only reads this box gets the > core value of the article.] ## Information Gain Opportunities - **[ORIGINAL DATA]**: [Suggestion for proprietary data, survey, experiment, or benchmark the author can produce to differentiate this post] - **[PERSONAL EXPERIENCE]**: [Suggestion for first-hand observation, test result, or case study to include: "When we tested X, we found Y"] - **[UNIQUE INSIGHT]**: [Suggestion for contrarian take, novel analysis, or non-obvious connection that competitors have not covered] ## Content Outline ### Introduction (100-150 words) - Hook: [Surprising statistic to open with] - Problem: [What challenge does the reader face?] - Promise: [What will they learn?] - TL;DR box placement (after hook, before first H2) ### H2: [Question Format] (300-400 words) - **Answer-first**: Open with [specific stat + source] - Cover: [subtopic 1], [subtopic 2] - **Image**: [Description of recommended image] - **Key stat**: [Specific statistic to include] ### H2: [Question Format] (300-400 words) - **Answer-first**: Open with [specific stat + source] - Cover: [subtopic 1], [subtopic 2] - **Chart**: [Type] showing [data description] - **Key stat**: [Specific statistic to include] [... repeat for 6-8 sections ...] ### FAQ Section (3-5 items) 1. [Question]: Answer with [stat + source] 2. [Question]: Answer with [stat + source] 3. [Question]: Answer with [stat + source] ### Conclusion (100-150 words) - Key takeaways (bulleted) - Call to action: [What should the reader do next?] ## Statistics to Include | # | Statistic | Source | Year | Section | |---|-----------|--------|------|---------| | 1 | [stat] | [source + URL] | 2025 | H2: Section 1 | | 2 | [stat] | [source + URL] | 2026 | H2: Section 2 | | ... | ... | ... | ... | ... | ## Citation Capsule Plan For each H2, plan a 40-60 word self-contained passage optimized for AI extraction. Each capsule should include a stat, its source, and a clear claim that can stand alone when quoted. | Section | Capsule Focus | Key Stat | Source | |---------|--------------|----------|--------| | H2: [Section 1] | [Core claim this section makes] | [stat] | [source] | | H2: [Section 2] | [Core claim this section makes] | [stat] | [source] | | H2: [Section 3] | [Core claim this section makes] | [stat] | [source] | | ... | ... | ... | ... | ## Cover Image | Option | Details | |--------|---------| | Photo cover | [Pixabay/Unsplash/Pexels search terms for wide hero image] | | Generated SVG | [Text-on-gradient concept with key stat, if data-heavy topic] | | Dimensions | 1200x630 (OG-compatible) | ##
Related in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
IncludedBuild conversational AI voice agents with ElevenLabs Platform using React, JavaScript, React Native, or Swift SDKs. Configure agents, tools (client/server/MCP), RAG knowledge bases, multi-voice, and Scribe real-time STT. Use when: building voice chat interfaces, implementing AI phone agents with Twilio, configuring agent workflows or tools, adding RAG knowledge bases, testing with CLI "agents as code", or troubleshooting deprecated @11labs packages, Android audio cutoff, CSP violations, dynamic variables, or WebRTC config. Keywords: ElevenLabs Agents, ElevenLabs voice agents, AI voice agents, conversational AI, @elevenlabs/react, @elevenlabs/client, @elevenlabs/react-native, @elevenlabs/elevenlabs-js, @elevenlabs/agents-cli, elevenlabs SDK, voice AI, TTS, text-to-speech, ASR, speech recognition, turn-taking model, WebRTC voice, WebSocket voice, ElevenLabs conversation, agent system prompt, agent tools, agent knowledge base, RAG voice agents, multi-voice agents, pronunciation dictionary, voice speed control, elevenlabs scribe, @11labs deprecated, Android audio cutoff, CSP violation elevenlabs, dynamic variables elevenlabs, case-sensitive tool names, webhook authentication
humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
IncludedSalesforce architecture diagrams using Mermaid with ASCII fallback. Use this skill when generating text-based diagrams for Salesforce architecture, OAuth flows, ERDs, integration sequences, or Agentforce structure. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use generating-visual-diagrams), or asks about non-Salesforce systems.