sc-research

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: sc-research
description: Deep research on any topic combining real-time web search (Rube MCP) with multi-model consensus analysis (PAL MCP). Produces structured reports with sourced findings and confidence assessments.
---

# Deep Research Skill

Conduct comprehensive research on any topic by combining real-time web search (via Rube MCP) with multi-model deep analysis and consensus synthesis (via PAL MCP). Produces structured research reports with sourced findings, cross-validated analysis, and confidence assessments.

## Quick Start

```bash
# Quick overview
/sc:research "quantum computing developments" --depth shallow

# Balanced research (default)
/sc:research "Impact of AI regulation on open-source development"

# Exhaustive deep dive saved to file
/sc:research "carbon capture technologies" --depth deep --output reports/carbon.md

# More models for consensus
/sc:research "PostgreSQL vs CockroachDB for write-heavy workloads" --models 4
```

## Behavioral Flow

1. **Parse** - Extract topic, depth, output path, model count
2. **Decompose** - Break topic into 3-7 sub-questions for comprehensive coverage
3. **Discover** - Find web search tools via Rube MCP
4. **Search** - Execute web research in parallel batches
5. **Analyze** - Deep analysis of findings via PAL ThinkDeep
6. **Validate** - Multi-model consensus via PAL Consensus
7. **Report** - Generate structured markdown report
8. **Output** - Save to file or display in console

## Flags

| Flag | Type | Default | Description |
|------|------|---------|-------------|
| `--depth` | string | medium | Research depth: shallow, medium, deep |
| `--output` | string | - | Save report to file path |
| `--models` | int | 3 | Number of models for consensus (2-5) |

## Depth Levels

| Depth | Sub-questions | Searches/question | Follow-ups |
|-------|--------------|-------------------|------------|
| shallow | 2-3 | 1 | 0 |
| medium | 3-5 | 1-2 | 1 per gap |
| deep | 5-7 | 2-3 | 2-3 per gap |

## Phase 1: Parse and Plan

Extract topic from arguments. Decompose into sub-questions that provide comprehensive coverage when answered together.

Present research plan before proceeding:

```
Research Topic: <topic>
Depth: <level>
Sub-questions:
  1. <sub-question>
  2. <sub-question>
  ...
Estimated searches: ~N
```

## Phase 2: Discover Search Tools

Use `mcp__rube__RUBE_SEARCH_TOOLS` to find web search and URL extraction tools:

```
RUBE_SEARCH_TOOLS:
  session: { generate_id: true }
  queries:
    - use_case: "search the web for information about a topic"
    - use_case: "scrape and extract content from a web page URL"
```

From the response:
1. **Record session_id** — reuse for all subsequent Rube calls
2. **Check connection status** for returned toolkits
3. If no active connection, call `RUBE_MANAGE_CONNECTIONS` and present auth link
4. **Identify best tools** for web search and URL content extraction
5. If tools return `schemaRef`, call `RUBE_GET_TOOL_SCHEMAS` for full schemas

## Phase 3: Execute Web Research

Batch independent searches in parallel (up to 5 per call) via `RUBE_MULTI_EXECUTE_TOOL`:

**Search query formulation:**
- Rephrase sub-questions as effective search queries
- Use specific, factual language
- For controversial topics, search multiple perspectives explicitly
- Include date qualifiers if recency matters

**After initial searches:**
1. Parse and collect all results
2. Identify most relevant URLs
3. For medium/deep: extract full content from top 3-5 URLs
4. Identify information gaps

**For medium/deep — follow-up searches:**
- Generate refined queries targeting gaps
- Execute follow-up searches
- Extract additional URL content

**Organize raw findings:**

```
Sub-question 1: <question>
  Sources:
    - [Source Title](URL) - Key finding: <summary>
  Gaps: <what's still unclear>

Sub-question 2: <question>
  Sources:
    - ...
```

## Phase 4: Deep Analysis (PAL ThinkDeep)

Use `mcp__pal__thinkdeep` for systematic analysis:

**Step 1 — Analyze:**
- Identify key t