deep-research

A process for researching a topic across multiple web sources and producing a report with citations, meaning links that show where the information came from.

In plain words
What is it for?
Use it for in-depth research, competitive analysis, technology comparisons, market estimates, company due diligence, and questions about the current state of a topic.
Why use it?
It helps replace a single unverified answer with findings compared across sources and tied to evidence.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/junmystery/agent-guidance-python/deep-research
Any agent
npx skills add JunMystery/Agent-Guidance-Python --skill deep-research
Clone the repo
git clone --depth 1 https://github.com/JunMystery/Agent-Guidance-Python

Made for: Claude Code, Codex.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,196 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
Origin 86% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.01196
Opus 5 $0.00024 $0.00598
Sonnet 5 $0.00010 $0.00239
Haiku 4.5 $0.00005 $0.00120

Measured 2d ago against content hash f85e06874ffd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

deep-research scanned grade B with 1 finding 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 2d 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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

Both together give the best coverage. Configure in `~/.claude.json` or `~/.codex/config.toml`.
Origin

This is a copy

86% identical to deep-research — 6 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.

skills/deep-research/SKILL.md · 161 lines

How it starts

The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Research

Drift-prone skill. Firecrawl/Exa MCP tool names, quotas, and result shapes change. Verify the configured MCP tools and current API docs before promising coverage or quoting live source counts.

Produce thorough, cited research reports from multiple web sources using firecrawl and exa MCP tools.

When to Activate

  • User asks to research any topic in depth
  • Competitive analysis, technology evaluation, or market sizing
  • Due diligence on companies, investors, or technologies
  • Any question requiring synthesis from multiple sources
  • User says "research", "deep dive", "investigate", or "what's the current state of"

MCP Requirements

At least one of:

  • firecrawlfirecrawl_search, firecrawl_scrape, firecrawl_crawl
  • exaweb_search_exa, web_search_advanced_exa, crawling_exa

Both together give the best coverage. Configure in ~/.claude.json or ~/.codex/config.toml.

Workflow

Step 1: Understand the Goal

Ask 1-2 quick clarifying questions:

  • "What's your goal — learning, making a decision, or writing something?"
  • "Any specific angle or depth you want?"

If the user says "just research it" — skip ahead with reasonable defaults.

Step 2: Plan the Research

Break the topic into 3-5 research sub-questions. Example:

  • Topic: "Impact of AI on healthcare"
    • What are the main AI applications in healthcare today?
    • What clinical outcomes have been measured?
    • What are the regulatory challenges?
    • What companies are leading this space?
    • What's the market size and growth trajectory?

Step 3: Execute Multi-Source Search

For EACH sub-question, search using available MCP tools:

With firecrawl:

firecrawl_search(query: "<sub-question keywords>", limit: 8)

With exa:

web_search_exa(query: "<sub-question keywords>", numResults: 8)
web_search_advanced_exa(query: "<keywords>", numResults: 5, startPublishedDate: "2025-01-01")

Search strategy:

  • Use 2-3 different keyword variations per sub-question
  • Mix general and news-focused queries
  • Aim for 15-30 unique sources total
  • Prioritize: academic, official, reputable news > blogs > forums

Read the full file on GitHub · 161 lines

Changes

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.

  1. 2d ago First seen · 161 lines · 48 tokens per session scan B f85e06874ffd

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,196 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). It is 86% identical to deep-research, differing in 6 lines, and is treated as a copy.

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