social-media▌
langchain-ai/deepagents · updated May 22, 2026
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Drafts platform-specific social media posts with research-backed content and generated companion images.
- ›Supports LinkedIn posts (1,300 characters with professional tone) and Twitter/X threads (280 characters per tweet with 1/🧵 format)
- ›Requires delegating research to a subagent before writing, then reading findings to ensure accuracy and relevance
- ›Generates eye-catching social images automatically using generate_social_image tool with bold, high-contrast compositions optimized for s
Social Media Content Skill
Research First (Required)
Before writing any social media content, you MUST delegate research:
- Use the
tasktool withsubagent_type: "researcher" - In the description, specify BOTH the topic AND where to save:
task(
subagent_type="researcher",
description="Research [TOPIC]. Save findings to research/[slug].md"
)
Example:
task(
subagent_type="researcher",
description="Research renewable energy trends in 2025. Save findings to research/renewable-energy.md"
)
- After research completes, read the findings file before writing
Output Structure (Required)
Every social media post MUST have both content AND an image:
LinkedIn posts:
linkedin/
└── <slug>/
├── post.md # The post content
└── image.png # REQUIRED: Generated visual
Twitter/X threads:
tweets/
└── <slug>/
├── thread.md # The thread content
└── image.png # REQUIRED: Generated visual
Example: A LinkedIn post about "prompt engineering" → linkedin/prompt-engineering/
You MUST complete both steps:
- Write the content to the appropriate path
- Generate an image using
generate_imageand save alongside the post
A social media post is NOT complete without its image.
Platform Guidelines
Format:
- 1,300 character limit (show more after ~210 chars)
- First line is crucial - make it hook
- Use line breaks for readability
- 3-5 hashtags at the end
Tone:
- Professional but personal
- Share insights and learnings
- Ask questions to drive engagement
- Use "I" and share experiences
Structure:
[Hook - 1 compelling line]
[Empty line]
[Context - why this matters]
[Empty line]
[Main insight - 2-3 short paragraphs]
[Empty line]
[Call to action or question]
#hashtag1 #hashtag2 #hashtag3
Twitter/X
Format:
- 280 character limit per tweet
- Threads for longer content (use 1/🧵 format)
- No more than 2 hashtags per tweet
Thread Structure:
1/🧵 [Hook - the main insight]
2/ [Supporting point 1]
3/ [Supporting point 2]
4/ [Example or evidence]
5/ [Conclusion + CTA]
Image Generation
Every social media post needs an eye-catching image. Use the generate_social_image tool:
generate_social_image(prompt="A detailed description...", platform="linkedin", slug="your-post-slug")
The tool saves the image to <platform>/<slug>/image.png.
Social Image Best Practices
Social images need to work at small sizes in crowded feeds:
- Bold, simple compositions - one clear focal point
- High contrast - stands out when scrolling
- No text in image - too small to read, platforms add their own
- Square or 4:5 ratio - works across platforms
Writing Effective Prompts
Include these elements:
- Single focal point: One clear subject, not a busy scene
- Bold style: Vibrant colors, strong shapes, high contrast
- Simple background: Solid color, gradient, or subtle texture
- Mood/energy: Match the post tone (inspiring, urgent, thoughtful)
Example Prompts
For an insight/tip post:
Single glowing lightbulb floating against a deep purple gradient background, lightbulb made of interconnected golden geometric lines, rays of soft light emanating outward. Minimal, striking, high contrast. Square composition.
For announcements/news:
Abstract rocket ship made of colorful geometric shapes launching upward with a trail of particles. Bright coral and teal color scheme against clean white background. Energetic, celebratory mood. Bold flat illustration style.
For thought-provoking content:
Two overlapping translucent circles, one blue one orange, creating a glowing intersection in the center. Represents collaboration or intersection of ideas. Dark charcoal background, soft ethereal glow. Minimalist and contemplative.
Content Types
Announcement Posts
- Lead with the news
- Explain the impact
- Include link or next step
Insight Posts
- Share one specific learning
- Explain the context briefly
- Make it actionable
Question Posts
- Ask a genuine question
- Provide your take first
- Keep it focused on one topic
Quality Checklist
Before finishing:
- Post saved to
linkedin/<slug>/post.mdortweets/<slug>/thread.md - Image generated alongside the post
- First line hooks attention
- Content fits platform limits
- Tone matches platform norms
- Has clear CTA or question
- Hashtags are relevant (not generic)
How to use social-media on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add social-media
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches social-media from GitHub repository langchain-ai/deepagents and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate social-media. Access the skill through slash commands (e.g., /social-media) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★35 reviews- ★★★★★Alexander Abebe· Dec 16, 2024
social-media has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aditi Rao· Nov 23, 2024
social-media is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aditi Ghosh· Nov 7, 2024
social-media fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aditi Reddy· Oct 26, 2024
We added social-media from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aditi Gill· Oct 14, 2024
Keeps context tight: social-media is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Alexander Rahman· Sep 17, 2024
Useful defaults in social-media — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Emma Dixit· Sep 9, 2024
social-media has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Sep 1, 2024
social-media has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Emma Martin· Aug 28, 2024
Solid pick for teams standardizing on skills: social-media is focused, and the summary matches what you get after install.
- ★★★★★Ganesh Mohane· Aug 20, 2024
Solid pick for teams standardizing on skills: social-media is focused, and the summary matches what you get after install.
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