Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill acts as your writing partner, helping you research, outline, draft, and refine content while maintaining your unique voice and style.
Create a dedicated folder for your article:
mkdir ~/writing/my-article-title
cd ~/writing/my-article-title
Create your draft file:
touch article-draft.md
Open Claude Code from this directory and start writing.
Help me create an outline for an article about [topic]
Research [specific topic] and add citations to my outline
Here's my introduction. Help me make the hook more compelling.
I just finished the "Why This Matters" section. Review it and give feedback.
Review the full draft for flow, clarity, and consistency.
When a user requests writing assistance:
Understand the Writing Project
Ask clarifying questions:
Collaborative Outlining
Help structure the content:
# Article Outline: [Title]
## Hook
- [Opening line/story/statistic]
- [Why reader should care]
## Introduction
- Context and background
- Problem statement
- What this article covers
## Main Sections
### Section 1: [Title]
- Key point A
- Key point B
- Example/evidence
- [Research needed: specific topic]
### Section 2: [Title]
- Key point C
- Key point D
- Data/citation needed
### Section 3: [Title]
- Key point E
- Counter-arguments
- Resolution
## Conclusion
- Summary of main points
- Call to action
- Final thought
## Research To-Do
- [ ] Find data on [topic]
- [ ] Get examples of [concept]
- [ ] Source citation for [claim]
Iterate on outline:
Conduct Research
When user requests research on a topic:
Example output:
## Research: AI Impact on Productivity
Key Findings:
1. **Productivity Gains**: Studies show 40% time savings for
content creation tasks [1]
2. **Adoption Rates**: 67% of knowledge workers use AI tools
weekly [2]
3. **Expert Quote**: "AI augments rather than replaces human
creativity" - Dr. Jane Smith, MIT [3]
Citations:
[1] McKinsey Global Institute. (2024). "The Economic Potential
of Generative AI"
[2] Stack Overflow Developer Survey (2024)
[3] Smith, J. (2024). MIT Technology Review interview
Added to outline under Section 2.
Improve Hooks
When user shares an introduction, analyze and strengthen:
Current Hook Analysis:
Suggested Alternatives:
Option 1: [Bold statement]
[Example] Why it works: [explanation]
Option 2: [Personal story]
[Example] Why it works: [explanation]
Option 3: [Surprising data]
[Example] Why it works: [explanation]
Questions to hook:
Provide Section-by-Section Feedback
As user writes each section, review for:
# Feedback: [Section Name]
## What Works Well ✓
- [Strength 1]
- [Strength 2]
- [Strength 3]
## Suggestions for Improvement
### Clarity
- [Specific issue] → [Suggested fix]
- [Complex sentence] → [Simpler alternative]
### Flow
- [Transition issue] → [Better connection]
- [Paragraph order] → [Suggested reordering]
### Evidence
- [Claim needing support] → [Add citation or example]
- [Generic statement] → [Make more specific]
### Style
- [Tone inconsistency] → [Match your voice better]
- [Word choice] → [Stronger alternative]
## Specific Line Edits
Original:
> [Exact quote from draft]
Suggested:
> [Improved version]
Why: [Explanation]
## Questions to Consider
- [Thought-provoking question 1]
- [Thought-provoking question 2]
Ready to move to next section!
Preserve Writer's Voice
Important principles:
Ask periodically:
Citation Management
Handle references based on user preference:
Inline Citations:
Studies show 40% productivity improvement (McKinsey, 2024).
Numbered References:
Studies show 40% productivity improvement [1].
[1] McKinsey Global Institute. (2024)...
Footnote Style:
Studies show 40% productivity improvement^1
^1: McKinsey Global Institute. (2024)...
Maintain a running citations list:
## References
1. Author. (Year). "Title". Publication.
2. Author. (Year). "Title". Publication.
...
Final Review and Polish
When draft is complete, provide comprehensive feedback:
# Full Draft Review
## Overall Assessment
**Strengths**:
- [Major strength 1]
- [Major strength 2]
- [Major strength 3]
**Impact**: [Overall effectiveness assessment]
## Structure & Flow
- [Comments on organization]
- [Transition quality]
- [Pacing assessment]
## Content Quality
- [Argument strength]
- [Evidence sufficiency]
- [Example effectiveness]
## Technical Quality
- Grammar and mechanics: [assessment]
- Consistency: [assessment]
- Citations: [completeness check]
## Readability
- Clarity score: [evaluation]
- Sentence variety: [evaluation]
- Paragraph length: [evaluation]
## Final Polish Suggestions
1. **Introduction**: [Specific improvements]
2. **Body**: [Specific improvements]
3. **Conclusion**: [Specific improvements]
4. **Title**: [Options if needed]
## Pre-Publish Checklist
- [ ] All claims sourced
- [ ] Citations formatted
- [ ] Examples clear
- [ ] Transitions smooth
- [ ] Call to action present
- [ ] Proofread for typos
Ready to publish! 🚀
User: "I'm writing an article about continuous discovery. Help me create an outline."
Process:
Result: Well-researched, properly cited article written in Teresa's voice with strong structure and flow.
User: "I'm writing about AI's impact on product management. Help me find current data and examples."
Output:
## Research Compiled
### Recent Data
- Gartner: 80% of PMs will use AI tools by 2025 [1]
- Survey: 45% report faster feature shipping [2]
- Case study: Airbnb's AI-assisted prioritization [3]
### Expert Quotes
- "AI amplifies PM judgment, not replaces it" - Marty Cagan
- [Additional quotes with citations]
### Real Examples
1. **Company A**: Used AI for user research synthesis
- Result: 60% time savings
- Source: [citation]
2. **Company B**: AI-powered roadmap analysis
- Result: Better stakeholder alignment
- Source: [citation]
All added to your outline with proper citations.
User's Original Hook:
"Product management is changing because of AI. In this article, I'll discuss some ways AI affects product managers."
Improved Options:
Option 1 (Data-driven):
"Last month, I asked AI to analyze 500 customer interviews. It took 30 minutes instead of 3 weeks. Product management will never be the same."
Option 2 (Question):
"What if you could talk to every customer, read every review, and analyze every support ticket—all before your morning coffee?"
Option 3 (Story):
"Sarah spent two weeks building the wrong feature. Not because she didn't understand her users, but because she couldn't process the hundreds of interviews fast enough to spot the pattern."
User: "Just finished my 'Common Mistakes' section. Thoughts?"
Response:
# Feedback: Common Mistakes Section
## What Works Well ✓
- Strong examples (the Slack notification story)
- Clear structure (3 mistakes, clean layout)
- Practical advice for each mistake
## Suggestions
### Make It More Specific
Your second point says "Bad prioritization" but could be stronger:
Current:
> "Many teams prioritize badly by focusing on features instead of outcomes."
Suggested:
> "I've watched teams ship 14 features in a quarter yet move none of their key metrics. They prioritized activity over progress."
### Add Data
The third mistake would benefit from evidence:
> "[Add citation]: Studies show teams without regular user contact are 3x more likely to build unused features [needs source]"
### Flow Improvement
Consider reordering: Mistake 3 → Mistake 2 → Mistake 1
This builds from small to big impact.
Ready for the next section!
Recommended structure for writing projects:
~/writing/article-name/
├── outline.md # Your outline
├── research.md # All research and citations
├── draft-v1.md # First draft
├── draft-v2.md # Revised draft
├── final.md # Publication-ready
├── feedback.md # Collected feedback
└── sources/ # Reference materials
├── study1.pdf
└── article2.md
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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Pull the ads a competitor is running right now on Meta (Facebook/Instagram) and Google, then extract the hooks, offers, CTAs and landing pages worth modelling. Use when writing ad copy, planning creative tests, choosing a landing page angle, or when the user asks "what is X advertising", "what ads does X run", "find competitor ad hooks". Reads live Ad Library and Ads Transparency data through the Tracetify MCP; falls back to a manual Ad Library walkthrough when the connector is absent.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.