PaperclipOrg
Claude Agent Skill · by 199 Biotechnologies

Deep Research

Deep Research conducts enterprise-grade research that synthesizes multiple sources, tracks citations, and scores source credibility to produce verified, citatio

Install
Terminal · npx
$npx skills add https://github.com/199-biotechnologies/claude-deep-research-skill --skill deep-research
Works with Paperclip

How Deep Research fits into a Paperclip company.

Deep Research drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

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SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md108 lines
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---name: deep-researchdescription: 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.--- # 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](./reference/methodology.md) for detailed phase instructions2. **Phase 8 (Report):** Load [report-assembly.md](./reference/report-assembly.md) for progressive generation3. **HTML/PDF output:** Load [html-generation.md](./reference/html-generation.md)4. **Quality checks:** Load [quality-gates.md](./reference/quality-gates.md)5. **Long reports (>18K words):** Load [continuation.md](./reference/continuation.md) **Templates:**- Report structure: [report_template.md](./templates/report_template.md)- HTML styling: [mckinsey_report_template.html](./templates/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.