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 | $0.00060 | $0.00318 |
| Sonnet 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 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.
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.
- 13d 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 (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.
Other skills, from other repositories
report-helper
A Chinese-language research workflow that searches the internet and produces a formatted PDF report about a specified topic.
happiness-skill
A Chinese-language guide to happiness based on reducing unmet wants, focusing on the present, and treating happiness as a trainable skill.
docx-comment-reply
Reply to comments (批注) in Word .docx/.doc files: extract comment context, draft replies, write threaded replies back, and validate OOXML.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.
asc-subscription-localization
Bulk-localize subscription, subscription-group, and in-app purchase display names across App Store locales using asc, including API 4.4.1 version-scoped v2 resources. Use when filling or updating subscription/IAP names and descriptions without App Store Connect UI work.
influence-psychology
Apply the seven principles of ethical persuasion (reciprocity, commitment, social proof, authority, liking, scarcity, unity) to product design, copy, and sales. Use when the user mentions "social proof", "persuasive copy", "why users dont convert", "ethical persuasion", "reciprocity", "scarcity tactics", "commitment…