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.
git clone --depth 1 https://github.com/gohypergiant/agent-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/agents/gohypergiant/agent-skills/deep-researcher)<a href="https://agentmods.dev/agents/gohypergiant/agent-skills/deep-researcher"><img src="https://agentmods.dev/badge/agents/gohypergiant/agent-skills/deep-researcher/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/agents/gohypergiant/agent-skills/deep-researcher"><img src="https://agentmods.dev/badge/agents/gohypergiant/agent-skills/deep-researcher.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 | $0.00060 | $0.01584 |
| Opus 5.5 | $0.00024 | $0.00634 |
| Sonnet 5.5 | $0.00012 | $0.00317 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
deep-researcher 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Overview
This provides a systematic methodology for conducting thorough web research. Read these instructions BEFORE starting any content generation task to ensure you gather sufficient information from multiple angles, depths, and sources.
Core Principle
Never generate content based solely on general knowledge. The quality of your output directly depends on the quality and quantity of research conducted beforehand. A single search query is NEVER enough.
Research Methodology
Phase 1: Broad Exploration
Start with broad searches to understand the landscape:
- Initial Survey: Search for the main topic to understand the overall context
- Identify Dimensions: From initial results, identify key subtopics, themes, angles, or aspects that need deeper exploration
- Map the Territory: Note different perspectives, stakeholders, or viewpoints that exist
Example:
Topic: "AI in healthcare"
Initial searches:
- "AI healthcare applications 2024"
- "artificial intelligence medical diagnosis"
- "healthcare AI market trends"
Identified dimensions:
- Diagnostic AI (radiology, pathology)
- Treatment recommendation systems
- Administrative automation
- Patient monitoring
- Regulatory landscape
- Ethical considerations
Phase 2: Deep Dive
For each important dimension identified, conduct targeted research:
- Specific Queries: Search with precise keywords for each subtopic
- Multiple Phrasings: Try different keyword combinations and phrasings
- Fetch Full Content: Use web fetch to read important sources in full, not just snippets
- Follow References: When sources mention other important resources, search for those too
Example:
Dimension: "Diagnostic AI in radiology"
Targeted searches:
- "AI radiology FDA approved systems"
- "chest X-ray AI detection accuracy"
- "radiology AI clinical trials results"
Then fetch and read:
- Key research papers or summaries
- Industry reports
- Real-world case studies
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.
- 1mo ago First seen · 186 lines · 60 tokens per session scan A 4bc120914428
deep-researcher is an agent published in the GitHub repository gohypergiant/agent-skills (25 stars, last pushed 2d ago), licensed Apache-2.0. It adds 60 tokens to every session and 1,584 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-10.
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