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 onceMisery/seahorse-agent --skill deep-researchgit clone --depth 1 https://github.com/onceMisery/seahorse-agentWrote 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/oncemisery/seahorse-agent/deep-research)<a href="https://agentmods.dev/skills/oncemisery/seahorse-agent/deep-research"><img src="https://agentmods.dev/badge/skills/oncemisery/seahorse-agent/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/oncemisery/seahorse-agent/deep-research"><img src="https://agentmods.dev/badge/skills/oncemisery/seahorse-agent/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 | $0.00071 | $0.01733 |
| Opus 5 | $0.00036 | $0.00866 |
| Sonnet 5 | $0.00014 | $0.00347 |
| Haiku 4.5 | $0.00007 | $0.00173 |
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 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 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Skill
Overview
This skill provides a systematic methodology for conducting thorough web research. Load this skill BEFORE starting any content generation task to ensure you gather sufficient information from multiple angles, depths, and sources.
When to Use This Skill
Always load this skill when:
Research Questions
- User asks "what is X", "explain X", "research X", "investigate X"
- User wants to understand a concept, technology, or topic in depth
- The question requires current, comprehensive information from multiple sources
- A single web search would be insufficient to answer properly
Content Generation (Pre-research)
- Creating presentations (PPT/slides)
- Creating frontend designs or UI mockups
- Writing articles, reports, or documentation
- Producing videos or multimedia content
- Any content that requires real-world information, examples, or current data
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:
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 · 199 lines · 71 tokens per session scan A 04712f4daa79
deep-research is a skill published in the GitHub repository onceMisery/seahorse-agent (18 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 71 tokens to every session and 1,733 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 deep-research, differing in 0 lines, and is treated as a copy.
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