Research and Write

End-to-end workflow: research a topic, then write a LinkedIn post from it. Chains the deep-research and linkedin-writer MCP servers.

Input Preparation

Gather from the user:

1. Topic — what to research
2. Guideline — how the post should be written (becomes guideline.md)

If the user only gives a topic, ask for the guideline details (angle, audience, key points, tone) or suggest a default based on the topic.

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.

Create guideline.md in the working directory:

# LinkedIn Post Guideline

## Topic
[Core topic]

## Angle
[Perspective]

## Target Audience
[Who reads this]

## Key Points to Cover
[3-5 bullets]

## Tone
[How it should sound]

Execution

Phase 1: Research

Load the research_workflow MCP prompt from the deep-research server and follow the workflow instructions using the available tools:

• deep_research — for web research queries
• analyze_youtube_video — for any YouTube URLs the user provides
• compile_research — to produce the final research.md

Use outputs/{slug}/ as the working_dir for all tool calls. This produces research.md.

Tell the user when research is complete.

Phase 2: Write

Read the WORKFLOW_INSTRUCTIONS from src/writing/routers/prompts.py and follow those steps exactly, using the linkedin-writer MCP tools. The working directory outputs/{slug}/ already has guideline.md and research.md from Phase 1.

The generate_post tool internally runs 4 evaluator-optimizer iterations (review + edit cycles) to refine the post before producing the final version.

After Completion

Present the final outputs/{slug}/post.md and outputs/{slug}/post_image.png to the user. Offer to edit with feedback.