Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add naveedharri/benai-skills --skill deep-researchgit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/deep-research)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/deep-research"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/deep-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/naveedharri/benai-skills/deep-research"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/deep-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 | $0.00067 | $0.02238 |
| Opus 5 | $0.00034 | $0.01119 |
| Sonnet 5 | $0.00013 | $0.00448 |
| Haiku 4.5 | $0.00007 | $0.00224 |
Grade A, and why
deep-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 11d 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 deep-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.
How it starts
The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Skill
Purpose
This skill enables comprehensive, internet-enabled research on any topic using OpenAI's Deep Research API (o4-mini-deep-research model). It intelligently enhances user research prompts through interactive clarifying questions, ensures research parameters are saved for reproducibility, and executes deep research with full web search capabilities.
When to Use This Skill
Trigger this skill when:
- User requests research on a specific topic
- User asks for analysis, investigation, or comprehensive information gathering
- User wants exploration of a subject with web search and reasoning
- User provides a brief research query that could be refined
- User wants to understand current state, trends, or comparisons in a field
Example user requests:
- "Research the most effective open-source RAG solutions with high benchmark performance"
- "What are the latest AI developments in 2025?"
- "I need a comprehensive analysis of distributed database systems"
- "Find best practices for implementing vector search"
- "Investigate how AI is impacting the software engineering industry"
Workflow Overview
User Input
↓
Assessment: Prompt too brief?
↓
YES → Ask Enhancement Questions → Collect Answers
↓ ↓
└───────→ Construct Enhanced Prompt ←──┘
↓
Save to Timestamped File
↓
Execute deep_research.py
↓
Output Report + Sources
↓
Present to User
How Claude Should Use This Skill
Important for Token Efficiency: Deep research takes 10-20 minutes to complete. The skill is designed to run synchronously (blocking) without intermediate status checks. This approach minimizes token usage during the wait. Claude should:
- Start the research
- Wait for completion (subprocess blocks automatically)
- Present final results once complete
No need for periodic polling or status updates during execution.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 281 lines · 67 tokens per session scan A 993c81b08e05
deep-research is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 7d ago), licensed MIT. It adds 67 tokens to every session and 2,238 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to deep-research, differing in 0 lines, and is treated as a copy.
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