designing-real-world-ai-agents-workshop: Skill for Claude Code

.agents/skills/research-and-write/SKILL.md

research-and-write is a skill for Claude Code, Codex from iusztinpaul/designing-real-world-ai-agents-workshop. It costs 120 tokens per session (637 once invoked), scanned A, a copy of research-and-write, MIT.

A workflow for researching a topic and turning the findings into a LinkedIn post, a short professional post shared on LinkedIn.

In plain words
What is it for?
Use it when you want to start with a topic idea, research it, and produce a finished LinkedIn post.
Why use it?
It connects information gathering with writing, so the research and the finished post follow one planned topic, audience, angle, and tone.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is iusztinpaul/designing-real-world-ai-agents-workshop's own configuration. It tells Claude Code and Codex how to work on designing-real-world-ai-agents-workshop itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything designing-real-world-ai-agents-workshop configures →

Reuse

Borrowing it

Nothing to install: this file belongs to iusztinpaul/designing-real-world-ai-agents-workshop. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/iusztinpaul/designing-real-world-ai-agents-workshop/main/.agents/skills/research-and-write/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/iusztinpaul/designing-real-world-ai-agents-workshop

Made for: Claude Code, Codex.

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README.md
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agentmods 80×15 button for research-and-write

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<a href="https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/research-and-write"><img src="https://agentmods.dev/badge/skills/iusztinpaul/designing-real-world-ai-agents-workshop/research-and-write.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 120 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 637 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00120 $0.00637
Opus 5 $0.00060 $0.00318
Sonnet 5 $0.00024 $0.00127
Haiku 4.5 $0.00012 $0.00064

Measured 13d ago against content hash 8930244b1689, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

research-and-write scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to research-and-write — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/research-and-write/SKILL.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.mdmy-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.

Read the full file on GitHub · 69 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 69 lines · 120 tokens per session scan A 8930244b1689

Subscribe to this mod's changes

research-and-write is a skill published in the GitHub repository iusztinpaul/designing-real-world-ai-agents-workshop (505 stars, last pushed 3mo ago), licensed MIT. It adds 120 tokens to every session and 637 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to research-and-write, differing in 0 lines, and is treated as a copy.

Related

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