# Research And Write

> End-to-end workflow: research a topic and then write a LinkedIn post about it. Use this skill whenever the user wants the full pipeline — from a topic idea to a finished LinkedIn post. Triggers on: 'research and write a post about', 'create a LinkedIn post about [topic]', 'I want to post about', 'write about [topic] for LinkedIn', or any request that implies both researching a subject and producing a LinkedIn post from it. This is the go-to skill when the user gives you a topic and expects a finished post.

- Skill: `thedixitjain/research-and-write` (Agent Skill)
- Install (CLI): `npx skillmds add thedixitjain/research-and-write`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/research-and-write/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: thedixitjain (https://skillmd.com/u/thedixitjain)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/thedixitjain/research-and-write

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# 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:

```markdown
# 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.

---

**Source:** [`Arindam200/awesome-ai-apps`](https://github.com/Arindam200/awesome-ai-apps) → `advance_ai_agents/deep_research_writing_agents_nebius_okahu/.agents/skills/research-and-write/SKILL.md`