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.
curl -O https://raw.githubusercontent.com/iusztinpaul/designing-real-world-ai-agents-workshop/main/.agents/skills/research-and-write/SKILL.mdgit clone --depth 1 https://github.com/iusztinpaul/designing-real-world-ai-agents-workshopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/research-and-write)<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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00120 | $0.00637 |
| Opus 5.5 | $0.00048 | $0.00255 |
| Sonnet 5.5 | $0.00024 | $0.00127 |
| Haiku 4.5 | $0.00012 | $0.00064 |
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 2mo 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.
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.
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:
- 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 queriesanalyze_youtube_video— for any YouTube URLs the user providescompile_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.
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.
- 2mo ago First seen · 69 lines · 120 tokens per session scan A 8930244b1689
research-and-write is a skill published in the GitHub repository iusztinpaul/designing-real-world-ai-agents-workshop (517 stars, last pushed 4mo ago), licensed MIT. It adds 120 tokens to every session and 637 once invoked, about $0.0005 per session on Opus 5.5. 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.
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