Content brief
Automate SEO audits, briefs, and content strategy using eleven production-tested Claude skills that perform independent research without external data input.
npx -y skills add flakey-caster542/superseo-skills --skill content-briefAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Use when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.
SKILL.md
5.8 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it
Content Brief
A writer-ready content brief based on real SERP analysis. The agent Googles the target keyword, reads the top 10 results, classifies intent, identifies competitor gaps, and produces the brief. No keyword tool exports, no manual SERP pasting.
Input
Target keyword (required). Optionally: business context if you want the brief tailored to a specific audience/tone.
If the user didn't provide a keyword, ask for it before proceeding.
Role
You are a senior content strategist and SEO brief specialist with 10+ years of experience. Your job is to produce a complete, writer-ready brief based on what actually ranks right now — not a generic template.
Step 1: Research the SERP
Google the target keyword. Read the top 10 results. For each top-ranking page, note:
- Content format (listicle / long-form guide / comparison / how-to / tool / video)
- Approximate word count
- Heading structure (H1, main H2s)
- Content angle and unique hook
- What they cover that others don't
- Whether they appear to hold a featured snippet, People Also Ask positions, or other SERP features
Step 2: Identify Search Intent
Classify dominant intent: Informational / Commercial Investigation / Transactional / Navigational.
Apply intent-specific length guidance:
- Informational: 1,500–3,000+ words — completeness, PAA coverage
- Commercial: 2,000–4,000 words — features, comparison, objectivity
- Transactional: 800–1,500 words — trust signals, CTAs, specs
- Navigational: 500–1,000 words — speed, direct info
Target word count = average of top 5 results + 10%. Never pad to hit a number.
Step 3: Map the People Also Ask
If PAA questions appear for this keyword, write them down verbatim. They'll become H2/H3 headings in the outline.
Step 4: Identify Content Type
Pick the content type from the SERP pattern. Content types: how-to, definition/explainer, comparison, listicle, product-review, case-study, pillar-page, faq-page, landing-page, service-page, category-page, buying-guide, alternatives-page, pricing-page, location-page.
Load references/content-types-overview.md for the one-screen decision table covering all 23 content types (H1/H2 structure, schema, snippet format, word counts). Use it to pick the right type in 30 seconds, then hand the choice over to write-content.
Step 5: Produce the Brief
Target Keyword Analysis
- Primary keyword | Apparent difficulty based on SERP competition | Dominant intent
- Difficulty strategy: Easy SERP (lots of low-DR competitors, mixed intent) = 3-6 months realistic / Moderate (all top results are DR 40+, uniform intent) = 6-12 months / Hard (top results are all DR 60+, highly optimized, long-form) = 12+ month authority play
- Related terms to target on the same page (from what the top pages cover as H2s)
SERP Competitive Intelligence
For each of the top 3 competitors:
- URL | Estimated words | Format type | Key sections covered | What they miss
Content Gap Analysis
Specific subtopics covered by 2+ top competitors but missing from where most results are thin. Name exact missing sections — not generic "add more depth."
Recommended Outline
H1 and H2/H3 structure aligned to search intent and the gap analysis. Include:
- Featured snippet target: which H2 hosts the 40-60 word snippet answer — mark the spot
- PAA integration: questions to address as H2/H3 headings
- FAQ section if 3+ PAA questions exist
Hub & Spoke Architecture
- This piece as: hub / spoke / standalone (based on keyword breadth)
- Internal linking pattern recommended
Technical Optimization
- Title tag: 50-60 chars, primary keyword near front
- Meta description: 150-160 chars, intent signal + CTA
- Schema: Article / FAQ / HowTo / Product / Review (choose based on content type)
- Featured snippet format: paragraph (what is) / ordered list (how to) / table (comparison)
E-E-A-T Signals Required
- Author expertise markers needed
- Original data or research to include
- External authoritative sources to cite
Resource Assessment
- Effort: Low (500-1,000w, 2-4h) / Medium (1,000-2,500w, 6-12h) / High (2,500w+, 16h+)
- Realistic 3-month target position given SERP difficulty
What to Ignore
- Keyword density targets — write naturally. Primary keyword in H1, first 100 words, 2-3 H2s (~2% body density is a ceiling, not a target)
- NLP term lists of 50+ words — focus on 5-8 core entities that must appear
- Word count without checking SERP — "write 3,000 words" without intent matching creates padded content
Next Step
Brief ready? Use the write-content skill with this brief as context to write the article.
Bundled references
Load these from references/ only when the step calls for them — don't preload.
content-types-overview.md— decision table for picking the right content type (Step 4)intent-matching.md— deep read on Informational / Commercial / Transactional / Navigational signal matching (Step 2, when the SERP intent is mixed or unclear)serp-driven-writing.md— how to turn the top 10 read into outline decisions (Step 5, if the gap analysis is thin)information-gain-writing.md— what qualifies as "new information" vs. index (Step 5, when briefing the unique angle)structured-data-snippets.md— snippet format per content type (Step 5, "Technical Optimization" block)human-input-framework.md— the 2-3 questions to ask the writer before they start (optional, when briefing for an outside writer rather than the agent)
Gives 0 of the 12 instructions most plan spec skills give in ~1.4k tokens
Counted across 1,099 of the 1,860 authors here whose files we hold, read 2026-08-07
- ask one question at a timein 51 of 1099
- Break plans into vertical slicesin 29 of 1099, across 11 files
- Publish issues in dependency orderin 27 of 1099, across 9 files
- Iterate until user approves the breakdownin 25 of 1099, across 7 files
- Explore the repository to understand the codebase statein 24 of 1099, across 7 files
- Use domain glossary vocabularyin 23 of 1099, across 5 files
- Apply correct triage labels to published issuesin 23 of 1099, across 5 files
- prefer AFK slices over HITLin 22 of 1099, across 7 files
- write a specification before writing any codein 22 of 1099, across 14 files
- Write failing tests before implementation codein 22 of 1099, across 20 files
- ask clarifying questions until requirements are concretein 21 of 1099, across 13 files
- Respect existing architecture decision recordsin 20 of 1099, across 5 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.