| name | content-writing |
| description | Use when creating written content -- blog posts, documentation, marketing copy, technical writing -- covers structure, audience targeting, SEO basics, and editorial quality |
| allowed-tools | Read Write Edit |
Content Writing
When helping with written content -- blog posts, articles, newsletters, thought leadership, documentation -- follow these principles for well-structured, engaging, and audience-appropriate writing.
Core Instructions
- Hook in the first sentence -- start with a surprising stat, bold claim, or relatable problem
- Use the inverted pyramid -- most important info first, details later
- Structure for scanning -- short paragraphs (2-3 sentences), headers every 200-300 words, bullet lists for key points
- Write at 8th-grade reading level -- clear, simple language wins over clever vocabulary
- Include one concrete example per major point -- abstract advice is forgettable
- SEO basics: Target keyword in title, first paragraph, and 2-3 headers. Use related terms naturally
- End with a takeaway or CTA -- never trail off
- Word count targets: Blog post 800-1500 words, newsletter 400-600, article 1500-2500
Output Structure
When producing content, use this format:
# [Title -- includes keyword, under 60 chars]
*[Subtitle or meta description -- 150-160 chars]*
[Hook paragraph]
## [Section 1 Header]
[Content with examples]
## [Section 2 Header]
[Content with examples]
## [Section 3+ Headers]
[Content]
## Key Takeaways
- [Point 1]
- [Point 2]
- [Point 3]
---
*[CTA or closing line]*
Worked Examples
Example 1 -- Blog Post
Input: Topic: "Why most AI projects fail". Audience: tech managers. Tone: authoritative but accessible. Keyword: "AI project failure".
Reasoning: Tech managers have seen failed projects. Start with a stat that validates their experience. Structure as a listicle for easy scanning.
Output:
# Why 85% of AI Projects Fail (And How to Beat the Odds)
*Most AI initiatives never make it to production. Here's what the survivors do differently.*
You've seen it before: a team spends six months building an AI model, demos it to leadership, gets applause -- and then nothing happens. The model sits in a notebook, never deployed, slowly becoming irrelevant.
You're not alone. According to Gartner, 85% of AI projects fail to deliver business value. But the 15% that succeed share three patterns.
## 1. They Start With the Problem, Not the Tech
The most common mistake is building a model and then looking for a use case. Successful teams flip this: they identify a $1M+ problem first, then ask whether AI is the right solution.
**Example**: A logistics company didn't start with "let's use ML." They started with "we lose $3M/year to misrouted packages." The AI solution was obvious once the problem was clear.
## 2. They Ship a Crappy V1 in 4 Weeks
Perfectionism kills AI projects. The winning teams deploy a minimal model fast -- even if it's only 70% accurate -- and improve in production with real data.
## 3. They Have an Ops Plan From Day One
Model accuracy is 20% of the work. Monitoring, retraining, data pipelines, and incident response are the other 80%.
## Key Takeaways
- Start with the business problem, not the technology
- Ship fast, iterate with real data
- Budget 80% of effort for operations, not modeling
---
*Building an AI project? Start with an AI Readiness Checklist -- it takes 10 minutes and saves months.*
Example 2 -- Newsletter
Input: Topic: weekly tech digest. Audience: developers. Tone: casual, insider voice. Length: 500 words.
Reasoning: Developers want signal, not noise. Lead with the most impactful news. Use informal language.
Output:
# This Week in Dev: Claude Gets Agents, React 20 Ships
*Your 3-minute developer news roundup*
Happy Friday. Three things you should know:
**Claude's agent SDK is wild.** Anthropic shipped a framework for building autonomous agents that can use tools, maintain context across sessions, and self-correct. Early reviews say it's significantly less janky than LangChain. Worth trying this weekend.
**React 20 is official.** The big change: Server Components are now the default. If you're still client-rendering everything, this is your nudge to learn RSC. Migration guide is actually good for once.
**GitHub Copilot got expensive.** New pricing: $19/mo individual, $39/seat for teams. The free tier now limits completions to 50/day. Cursor and Cody are looking more attractive.
## Quick Links
- [Claude Agent SDK docs](link)
- [React 20 migration guide](link)
- [Copilot pricing comparison spreadsheet](link)
---
*Reply to this email if you want us to cover something next week.*
Edge Cases
- No keyword provided: Focus on readability and value; skip SEO optimization
- Highly technical topic: Include code snippets or diagrams. Define acronyms on first use
- Controversial topic: Present multiple perspectives. Take a clear position but acknowledge counterarguments
- Very short request (<200 words): Format as a LinkedIn post or tweet thread instead
Quality Checklist
