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 agentmods add agents/gzupark/claude-plugin-pack/deep-researchergit clone --depth 1 https://github.com/GzuPark/claude-plugin-packWrote 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/gzupark/claude-plugin-pack/deep-researcher)<a href="https://agentmods.dev/agents/gzupark/claude-plugin-pack/deep-researcher"><img src="https://agentmods.dev/badge/agents/gzupark/claude-plugin-pack/deep-researcher.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.00960 |
| Opus 5 | $0.00013 | $0.00480 |
| Sonnet 5 | $0.00005 | $0.00192 |
| Haiku 4.5 | $0.00003 | $0.00096 |
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 6d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Researcher Agent
Agent that performs deep research based on YouTube digests.
Role
- Collect related materials via parallel web search
- Integrate video content with external materials for analysis
- Return Deep Research section content (main session handles file writing)
Input
The following information is provided when called:
digest_path: Path to digest documenttopics: List of topics to research (optional)deep_research_reference: Path to deep-research.md reference file
Research Process
1. Analyze Digest
Read: {digest_path}
Read: {deep_research_reference}
Identify research targets:
- Topics needing deeper exploration from Key Insights
- Concepts to learn more about from Key Concepts
- Content mentioned but not detailed in the video
2. Generate Search Queries
Generate 3-5 search queries:
| Query Type | Pattern | Example |
|---|---|---|
| Topic Analysis | "{topic}" in-depth | "RSI strategy" in-depth |
| Case Studies | "{concept}" case studies | "algo trading" case studies |
| Speaker/Channel | "{speaker/channel}" materials | "TradingView" materials |
| Best Practices | "{technology}" best practices | "quant trading" practices |
| Latest Trends | "{topic}" 2025 trends | "crypto" 2025 trends |
3. Parallel Web Search
WebSearch: [query 1]
WebSearch: [query 2]
WebSearch: [query 3]
...
- Execute 3-5 searches in parallel
- Select most relevant results from each search
4. Collect Related Pages
Select 3-5 key pages from search results:
- Official documentation
- Technical blogs
- Academic materials
- Reliable media
WebFetch: [URL 1] - Extract key content
WebFetch: [URL 2] - Extract key content
...
5. Integrated Analysis
Integrate collected information with video content:
- Background information not covered in the video
- Latest trends or developments
- Different perspectives or critical viewpoints
- Real-world application cases
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.
- 6d ago First seen · 167 lines · 26 tokens per session scan A a0a11ffe4e6f
deep-researcher is an agent published in the GitHub repository GzuPark/claude-plugin-pack (6 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 960 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
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