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/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)<a href="https://agentmods.dev/skills/iusztinpaul/designing-real-world-ai-agents-workshop/research"><img src="https://agentmods.dev/badge/skills/iusztinpaul/designing-real-world-ai-agents-workshop/research/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"><img src="https://agentmods.dev/badge/skills/iusztinpaul/designing-real-world-ai-agents-workshop/research.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 | $0.00078 | $0.00317 |
| Opus 5 | $0.00039 | $0.00159 |
| Sonnet 5 | $0.00016 | $0.00063 |
| Haiku 4.5 | $0.00008 | $0.00032 |
Grade A, and why
research 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 12d 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 — 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.
What it actually says
Research
Research a topic using the deep-research MCP server.
Working Directory
All output goes into outputs/{slug}/ relative to the project root. Derive the slug from:
- The dataset seed 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.
Execution
- Load the
research_workflowMCP prompt from thedeep-researchserver. - Follow the workflow instructions to research the user's topic 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 theworking_dirfor all tool calls.
After Completion
Show the user the path to outputs/{slug}/research.md and a brief summary of what was found.
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.
- 12d ago First seen · 30 lines · 78 tokens per session scan A e8e2e7764bac
research 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 78 tokens to every session and 317 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to research, differing in 0 lines, and is treated as a copy.
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