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Политика конфиденциальностиСогласие на обработку ПДн|ИНН 623412173261
    Deep Research — Скилл для ИИ-агентов | AI Рассвет

    Deep Research

    Use when the user needs multi source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1 2 searches.

    Скиллы для операций#GitHub#199-biotechnologies/claude-deep-research-skill#skills.sh
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    Установить скилл

    Добавьте инструмент одной командой или скачайте проверенный архив версии.

    npx skills add 199-biotechnologies/claude-deep-research-skill --skill deep-research
    Скачать ZIP
    Версия
    1.0.0+f2f2c0fa4e76
    Автор
    Владимир Ломтев
    Репозиторий
    199-biotechnologies/claude-deep-research-skill
    GitHub: 199-biotechnologies/claude-deep-research-skill

    Как установить

    1. 1Скопируйте команду из блока установки.
    2. 2Запустите её в терминале из каталога проекта.

    Документация

    Deep Research

    Core Purpose

    Deliver citation-tracked research reports through a structured pipeline with evidence persistence, source identity management, claim-level verification, and progressive context management.

    Autonomy Principle: Operate independently. Infer assumptions from context. Only stop for critical errors or incomprehensible queries. Surface high-materiality assumptions explicitly in the Introduction and Methodology rather than silently defaulting.


    Decision Tree

    Request Analysis
    +-- Simple lookup? --> STOP: Use WebSearch
    +-- Debugging? --> STOP: Use standard tools
    +-- Complex analysis needed? --> CONTINUE
    
    Mode Selection
    +-- Initial exploration --> quick (3 phases, 2-5 min)
    +-- Standard research --> standard (6 phases, 5-10 min) [DEFAULT]
    +-- Critical decision --> deep (8 phases, 10-20 min)
    +-- Comprehensive review --> ultradeep (8+ phases, 20-45 min)
    

    Default assumptions: Technical query = technical audience. Comparison = balanced perspective. Trend = recent 1-2 years.


    Workflow Overview

    Phase Name Quick Std Deep Ultra
    1 SCOPE Y Y Y Y
    2 PLAN - Y Y Y
    3 RETRIEVE Y Y Y Y
    4 TRIANGULATE - Y Y Y
    4.5 OUTLINE REFINEMENT - Y Y Y
    5 SYNTHESIZE - Y Y Y
    6 CRITIQUE - - Y Y
    7 REFINE - - Y Y
    8 PACKAGE Y Y Y Y

    Note: Phases 3-5 operate as an evidence loop per section (retrieve → evidence store → refine outline → draft → verify claims → delta-retrieve if needed), not as strict sequential gates.


    Execution

    On invocation, load relevant reference files:

    1. Phase 1-7: Load methodology.md for detailed phase instructions
    2. Phase 8 (Report): Load report-assembly.md for progressive generation
    3. HTML/PDF output: Load html-generation.md
    4. Quality checks: Load quality-gates.md
    5. Long reports (>18K words): Load continuation.md

    Templates:

    • Report structure: report_template.md
    • HTML styling: mckinsey_report_template.html

    Scripts:

    • python scripts/validate_report.py --report [path]
    • python scripts/verify_citations.py --report [path]
    • python scripts/md_to_html.py [markdown_path]

    Output Contract

    Required sections:

    • Executive Summary (200-400 words)
    • Introduction (scope, methodology, assumptions)
    • Main Analysis (4-8 findings, 600-2,000 words each, cited)
    • Synthesis & Insights (patterns, implications)
    • Limitations & Caveats
    • Recommendations
    • Bibliography (COMPLETE - every citation, no placeholders)
    • Methodology Appendix

    Output files (all to ~/Documents/[Topic]_Research_[YYYYMMDD]/):

    • Markdown (primary source of truth)
    • sources.jsonl — stable source registry with canonical IDs
    • evidence.jsonl — append-only evidence store with quotes and locators
    • claims.jsonl — atomic claim ledger with support status
    • run_manifest.json — query, mode, assumptions, provider config
    • HTML (McKinsey style, auto-opened)
    • PDF (professional print, auto-opened)

    Quality standards:

    • 10+ sources, 3+ per major claim (cluster-independent, not just count)
    • All factual claims cited immediately [N] with evidence backing in evidence.jsonl
    • Claim-support verification mandatory: no unsupported factual claims pass delivery
    • No placeholders, no fabricated citations
    • Prose-first (>=80%), bullets sparingly

    When to Use / NOT Use

    Use: Comprehensive analysis, technology comparisons, state-of-the-art reviews, multi-perspective investigation, market analysis.

    Do NOT use: Simple lookups, debugging, 1-2 search answers, quick time-sensitive queries.

    Требования и возможности

    Источник пакета
    https://github.com/199-biotechnologies/claude-deep-research-skill/tree/f2f2c0fa4e7617ca84c86b63f4bb40f77a746933

    Файлы версии

    ПутьРазмерSHA256
    README.md492427d8c58f22baca25...
    SKILL.md43430c8e777eb13d37b4...
    reference/continuation.md46020ed19331d7c7f577...
    reference/html-generation.md2919a8ebc64d52b7cd9a...
    reference/methodology.md16505d17cab6a258c2b5f...

    Частые вопросы

    Как установить Deep Research?
    Используйте команду npx skills add 199-biotechnologies/claude-deep-research-skill --skill deep-research или скачайте ZIP-архив.
    Можно ли скачать Deep Research бесплатно?
    Да, опубликованную версию можно скачать из маркетплейса бесплатно.

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    Установить скилл

    Добавьте инструмент одной командой или скачайте проверенный архив версии.

    npx skills add 199-biotechnologies/claude-deep-research-skill --skill deep-research
    Скачать ZIP
    Версия
    1.0.0+f2f2c0fa4e76
    Автор
    Владимир Ломтев
    Репозиторий
    199-biotechnologies/claude-deep-research-skill
    GitHub: 199-biotechnologies/claude-deep-research-skill