deep-research

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---
name: deep-research
description: This skill should be used when the user needs deep, multi-step web research with source synthesis, citations, skeptical evidence evaluation, confidence/gap analysis, and optional dense/frontier research using parallel agents.
license: MIT
---

# Deep Research

## Purpose

Run structured, multi-step web research with evidence-first synthesis, source traceability, skeptical evaluation, and explicit confidence/gap analysis.

This skill works in native mode with built-in web research tools and does not require Google/Gemini, OpenAI, Anthropic, OpenRouter, or any other paid provider setup. When the user explicitly wants maximum depth, use dense/frontier mode to recommend the strongest available model and run a wider parallel research topology where the host platform supports subagents.

## When to Use This Skill

Use this skill when:
- Performing market analysis
- Conducting competitive landscaping
- Building literature or source reviews
- Doing technical due diligence
- Preparing decision memos with citations
- Producing dense, high-confidence, Perplexity-like research with adversarial critique

## Requirements

- Access to built-in web research tools (`WebSearch`, `WebFetch`)
- Clear research question and scope

No external API key is required for native mode.

## Progress Tracking

Display a progress gauge at each research phase:

```
[████░░░░░░░░░░░░░░░░] 20% — Phase 1/5: Objective, Scope & Decomposition
[████████░░░░░░░░░░░░] 40% — Phase 2/5: Parallel Source Collection
[████████████░░░░░░░░] 60% — Phase 3/5: Evidence Ledger & Triangulation
[████████████████░░░░] 80% — Phase 4/5: Synthesis & Confidence/Gaps
[████████████████████] 100% — Phase 5/5: Citation Audit & Final Review
```

## Operating Modes

### Native Research

Use native mode by default.

- Use built-in `WebSearch` and `WebFetch`.
- Decompose complex topics into 3-5 sub-questions.
- Run parallel `ResearchScout` agents where subagents are available.
- Produce an evidence ledger, citations, and `Confidence & Gaps`.

### Dense / Frontier Research

Use dense/frontier mode when the user asks for maximum depth, exhaustive research, "frontier model", "Perplexity-like" research, "turbinado", adversarial review, high-confidence evidence, or says cost is secondary to research quality.

- Recommend the strongest available model or model class before execution.
- Prefer the host platform's frontier model for synthesis and critique.
- Run a wider parallel topology for sub-questions, primary-source harvesting, contrarian evidence, recency checks, citation audit, and synthesis.
- Add critique and rebuttal rounds before final synthesis.
- Increase source quota and require deeper source extraction.
- Include a model/tooling note in the final report.

If dense/frontier mode is requested but frontier tooling is unavailable, continue with native mode and disclose the limitation.

## Frontier Model Recommendation

For dense/frontier research, recommend the strongest available model before execution:

- Use an Opus-class Claude model for high-stakes synthesis, adversarial judgment, and nuanced source conflict analysis when available.
- Use a GPT frontier reasoning/chat model for broad synthesis, structured reasoning, and tool-heavy research when available.
- Use a Codex-class model for code-heavy technical research, API analysis, repository analysis, and engineering claims.
- Use a Gemini Pro-class thinking model for long-context, multimodal, PDF-heavy, and Google-grounded research when available.
- Use OpenRouter when the user wants access to multiple frontier model families through one API or wants a latest-family alias.

State the recommendation in this form:

```
Recommended research model: <model or model class>.
Reason: <why this model fits the research task>.
Fallback: <native mode or next best model>.
```

Do not hard-code one model as universally best. Use the strongest available model/tooling path and disclose limitations.

## Research Mode