199-biotechnologies

Claude Deep Research Skill — Data skill for Claude Code

Data community

Enterprise-grade deep research skill for Claude Code with 8-phase pipeline, source credibility scoring, and automated validation.

How to install Claude Deep Research Skill

This entry records only its repository, not the path inside it, so there is no exact command to give. Open 199-biotechnologies/claude-deep-research-skill and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Claude Deep Research Skill does

Enterprise-grade deep research skill for Claude Code with 8-phase pipeline, source credibility scoring, and automated validation. Outperforms OpenAI, Gemini, and Claude Desktop in quality and verification.

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README

Deep Research Skill for Claude Code

Enterprise-grade research engine for Claude Code. Produces citation-backed reports with source credibility scoring, multi-provider search, and automated validation.

Installation

# Clone into Claude Code skills directory
git clone https://github.com/199-biotechnologies/claude-deep-research-skill.git ~/.claude/skills/deep-research

No additional dependencies required for basic usage.

Optional: search-cli (multi-provider search)

For aggregated search across Brave, Serper, Exa, Jina, and Firecrawl:

brew tap 199-biotechnologies/tap && brew install search-cli
search config set keys.brave YOUR_KEY  # configure at least one provider

Usage

deep research on the current state of quantum computing
deep research in ultradeep mode: compare PostgreSQL vs Supabase for our stack

Research Modes

Mode Phases Duration Best For
Quick 3 2-5 min Initial exploration
Standard 6 5-10 min Most research questions
Deep 8 10-20 min Complex topics, critical decisions
UltraDeep 8+ 20-45 min Comprehensive reports, maximum rigor

Pipeline

Scope → Plan → **Retrieve** (parallel search + agents) → Triangulate → Outline Refinement → Synthesize → Critique (with loop-back) → Refine → Package

Key features:

  • Step 0: Retrieves current date before searches (prevents stale training-data year assumptions)
  • Parallel retrieval: 5-10 concurrent searches + 2-3 focused sub-agents returning structured evidence objects
  • First Finish Search: Adaptive quality thresholds by mode
  • Critique loop-back: Phase 6 can return to Phase 3 with delta-queries if critical gaps found
  • Multi-persona red teaming: Skeptical Practitioner, Adversarial Reviewer, Implementation Engineer (Deep/UltraDeep)
  • Disk-persisted citations: sources.json survives context compaction and cont