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 instructions/ankitclassicvision/claude-code-deep-research/claude-mdgit clone --depth 1 https://github.com/AnkitClassicVision/Claude-Code-Deep-ResearchWrote 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/instructions/ankitclassicvision/claude-code-deep-research/claude-md)<a href="https://agentmods.dev/instructions/ankitclassicvision/claude-code-deep-research/claude-md"><img src="https://agentmods.dev/badge/instructions/ankitclassicvision/claude-code-deep-research/claude-md.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 | $0.04729 | $0.04729 |
| Opus 5 | $0.02364 | $0.02364 |
| Sonnet 5 | $0.00946 | $0.00946 |
| Haiku 4.5 | $0.00473 | $0.00473 |
Grade A, and why
Claude-Code-Deep-Research CLAUDE.md 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 4d 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 — 595 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Implementation Plan
Overview
This document outlines the implementation plan for conducting deep research using the methodologies from deepresearchprocess.md and the Graph of Thoughts (GoT) framework.
Understanding Deep Research
Deep research is an AI-driven approach that autonomously conducts multi-step research by:
- Iteratively searching, reading, and analyzing information
- Performing multi-step reasoning
- Compiling findings with explicit citations
- Using Graph of Thoughts for complex problem-solving
The 7-Phase Deep Research Process
Phase 1: Question Scoping
- Clarify the research question with the user
- Define output format and success criteria
- Identify constraints and desired tone
- Create unambiguous query with clear parameters
Phase 2: Retrieval Planning
- Break main question into subtopics
- Generate specific search queries
- Select appropriate data sources
- Create research plan for user approval
- Use GoT to model the research as a graph of operations
Phase 3: Iterative Querying
- Execute searches systematically
- Navigate and extract relevant information
- Formulate new queries based on findings
- Use multiple search modalities (web search, file analysis, etc.)
- Apply GoT operations for complex reasoning
Phase 4: Source Triangulation
- Compare findings across multiple sources
- Validate claims with cross-references
- Handle inconsistencies
- Assess source credibility
- Use GoT scoring functions to evaluate information quality
Phase 5: Knowledge Synthesis
- Structure content logically
- Write comprehensive sections
- Include inline citations for every claim
- Add data visualizations when relevant
- Use GoT to optimize information organization
Phase 6: Quality Assurance
- Check for hallucinations and errors
- Verify all citations match content
- Ensure completeness and clarity
- Apply Chain-of-Verification techniques
- Use GoT ground truth operations for validation
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.
- 4d ago First seen · 595 lines · 4,729 tokens per session scan A 09273d78d553
Claude-Code-Deep-Research CLAUDE.md is an instructions file published in the GitHub repository AnkitClassicVision/Claude-Code-Deep-Research (147 stars, last pushed 2mo ago), licensed MIT. It adds 4,729 tokens to every session, about $0.0236 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.
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