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 skills/shobcoder/shob/deep-research-agentnpx skills add shobcoder/shob --skill deep-research-agentgit clone --depth 1 https://github.com/shobcoder/shobWhat 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 | $0.00082 | $0.01585 |
| Opus 5 | $0.00041 | $0.00792 |
| Sonnet 5 | $0.00016 | $0.00317 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
deep-research-agent 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 3d 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepResearch Agent
Autonomous multi-phase research agent that decomposes queries, gathers information from diverse sources, verifies facts, and synthesizes structured reports with 100+ source citations.
Core Workflow
Phase 1: Query Decomposition & Planning
Input: User's research query (natural language)
Process:
-
Analyze the query intent
- Identify the primary research objective
- Determine required expertise domains (History/Technology/Market/Challenges/Regulations etc.)
- Assess depth requirements (surface-level vs comprehensive)
-
Generate multi-dimensional search queries
- Historical context queries (when applicable)
- Technical specification queries
- Market/industry trend queries
- Challenge/pain point queries
- Regulatory/compliance queries (if applicable)
- Future outlook/prediction queries
-
Build investigation roadmap
- Define search priority order
- Identify cross-cutting themes
- Plan for iterative deep-diving
- Set minimum source targets per topic area
Output: research_plan object containing:
{
"primary_topic": "string",
"sub_topics": ["string"],
"search_queries": [{"query": "string", "domain": "string", "priority": 1}],
"target_sources": 100,
"timeline_phases": ["phase1", "phase2", "phase3"]
}
Phase 2: Autonomous Information Gathering
Tools Used: batch_web_search, extract_content_from_websites
Process:
-
Initial breadth search
- Execute parallel searches across all primary query dimensions
- Gather minimum 20-30 URLs per major topic area
- Prioritize authoritative sources (official docs, academic, established media)
-
Source classification
- Categorize by source type: News, Academic Papers, Whitepapers, Technical Documentation, Forums, Blogs
- Assess domain authority and reliability
- Flag sources requiring deeper analysis
-
Iterative deep-diving
- Extract key terms and concepts from initial results
- Generate follow-up queries using discovered terminology
- Expand search to related topics and subtopics
- Loop until saturation (no new significant information)
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.
- 3d ago First seen · 219 lines · 82 tokens per session scan A 4a05e5831976
deep-research-agent is a skill published in the GitHub repository shobcoder/shob (582 stars, last pushed 11d ago), licensed MIT. It adds 82 tokens to every session and 1,585 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
ask
Process-first advisor routing for Claude, Codex, Gemini, Antigravity, Grok, or Cursor via omc ask, with artifact capture and no raw CLI assembly.
external-context
Invoke parallel document-specialist agents for external web searches and documentation lookup.
reflect
Review recent work, find repeated workflow patterns, and suggest reusable skills, agents, commands, config changes, or playbooks. Use when the user asks to learn from past sessions, improve recurring workflows, or identify what should be turned into reusable agent instructions.
codemap
Generate comprehensive hierarchical codemaps for UNFAMILIAR repositories. Expensive operation - only use when explicitly asked for codebase documentation or initial repository mapping.
scientific-schematics
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways…
openpets
Use when the user asks to install, configure, verify, troubleshoot, or understand OpenPets; install or select a pet; connect Claude Code, OpenCode, Cursor, Codex, or MCP clients; configure a project to use a specific pet; or debug openpetsstatus, openpetsreact, or openpetssay.