Find your landing page message
Connect a product’s value to a specific audience and write a clear first-screen message.
Generate a LinkedIn post using the LinkedIn Writer MCP server. Use this skill whenever the user wants to write a LinkedIn post, create social media content, draft a post from research, or generate a post with an image. Triggers on: 'write a post', 'create a LinkedIn post', 'draft a post about', '...
---
name: write-post-a171ed0c5005
description: "Generate a LinkedIn post using the LinkedIn Writer MCP server. Use this skill whenever the user wants to write a LinkedIn post, create social media content, draft a post from research, or generate a post with an image. Triggers on: 'write a post', 'create a LinkedIn post', 'draft a post about', 'turn this into a post', 'generate a post', or any request involving LinkedIn content creation. Also use when the user has a guideline.md and research.md ready."
---
# Write LinkedIn Post
Generate a LinkedIn post using the `linkedin-writer` MCP server.
## Working Directory
All output goes into `outputs/{slug}/` relative to the project root. Derive the slug from:
- The dataset seed/guideline filename if the user references one (e.g., `my-topic_seed.md` → `my-topic`)
- Otherwise, slugify the topic (lowercase, hyphens, no special chars, max 60 chars)
Create the directory if it doesn't exist.
## Input Preparation
The working directory needs `guideline.md` and `research.md`.
If the user provides raw text for the guideline, create `guideline.md` in the working directory:
```markdown
# LinkedIn Post Guideline
## Topic
[What the post is about]
## Angle
[What perspective or approach to take]
## Target Audience
[Who this post is for]
## Key Points to Cover
[3-5 bullet points]
## Tone
[How it should sound]
```
If `research.md` is in a different location, copy it into the working directory.
## Execution
Read the `WORKFLOW_INSTRUCTIONS` from `src/writing/routers/prompts.py` and follow those steps exactly, using the `linkedin-writer` MCP tools. Pass `outputs/{slug}/` as the working directory path to each tool.
## After Completion
Present the final `outputs/{slug}/post.md` content to the user. If an image was generated, mention `outputs/{slug}/post_image.png`.
---
name: write-post-a171ed0c5005
description: "Generate a LinkedIn post using the LinkedIn Writer MCP server. Use this skill whenever the user wants to write a LinkedIn post, create social media content, draft a post from research, or generate a post with an image. Triggers on: 'write a post', 'create a LinkedIn post', 'draft a post about', 'turn this into a post', 'generate a post', or any request involving LinkedIn content creation. Also use when the user has a guideline.md and research.md ready."
---
# Write LinkedIn Post
Generate a LinkedIn post using the `linkedin-writer` MCP server.
## Working Directory
All output goes into `outputs/{slug}/` relative to the project root. Derive the slug from:
- The dataset seed/guideline filename if the user references one (e.g., `my-topic_seed.md` → `my-topic`)
- Otherwise, slugify the topic (lowercase, hyphens, no special chars, max 60 chars)
Create the directory if it doesn't exist.
## Input Preparation
The working directory needs `guideline.md` and `research.md`.
If the user provides raw text for the guideline, create `guideline.md` in the working directory:
```markdown
# LinkedIn Post Guideline
## Topic
[What the post is about]
## Angle
[What perspective or approach to take]
## Target Audience
[Who this post is for]
## Key Points to Cover
[3-5 bullet points]
## Tone
[How it should sound]
```
If `research.md` is in a different location, copy it into the working directory.
## Execution
Read the `WORKFLOW_INSTRUCTIONS` from `src/writing/routers/prompts.py` and follow those steps exactly, using the `linkedin-writer` MCP tools. Pass `outputs/{slug}/` as the working directory path to each tool.
## After Completion
Present the final `outputs/{slug}/post.md` content to the user. If an image was generated, mention `outputs/{slug}/post_image.png`.
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.