How do I install the Research And Write skill?

Run npx skillmds@latest add gabrielmoreira/research-and-write in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Research And Write safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Research And Write?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Research And Write free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Research And Write?

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Research And Write by gabrielmoreira

Name: Research And Write
Rating: 4.2 (5 reviews)
Author: gabrielmoreira

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.

gabrielmoreira Updated Sep 9, 2026 17 repo stars

File contents

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:

Topic — what to research
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.

---name: research-and-writedescription: 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.--- # 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.

Frequently asked questions