
sc-research
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.
- Installs
- 69
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/tony363/superclaude --skill sc-research- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads sc-research automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
# 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
- Parse - Extract topic, depth, output path, model count
- Decompose - Break topic into 3-7 sub-questions for comprehensive coverage
- Discover - Find web search tools via Rube MCP
- Search - Execute web research in parallel batches
- Analyze - Deep analysis of findings via PAL ThinkDeep
- Validate - Multi-model consensus via PAL Consensus
- Report - Generate structured markdown report
- 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:
- Record session_id — reuse for all subsequent Rube calls
- Check connection status for returned toolkits
- If no active connection, call
RUBE_MANAGE_CONNECTIONSand present auth link - Identify best tools for web search and URL content extraction
- If tools return
schemaRef, callRUBE_GET_TOOL_SCHEMASfor 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:
- Parse and collect all results
- Identify most relevant URLs
- For medium/deep: extract full content from top 3-5 URLs
- 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 themes and patterns across sources
- Flag contradictions between sources
- Assess source credibility and biases
- Identify well-supported vs. poorly-supported claims
- Note significant information gaps
- Synthesize preliminary narrative
Step 2 — Refine:
- Resolve contradictions with evidence-based reasoning
- Rank findings by confidence (high/medium/low)
- Produce structured outline for final report
- Identify 3-5 most important takeaways
Phase 5: Multi-Model Consensus (PAL Consensus)
5a. Discover Models
Call mcp__pal__listmodels. Select top N by score, preferring different providers for diversity.
5b. Run Consensus
Use mcp__pal__consensus with for/against/neutral stances:
| Stance | Purpose |
|---|---|
| for | Evaluate findings charitably, look for strengths |
| against | Critically evaluate, look for weaknesses and gaps |
| neutral | Balanced evaluation, weigh strengths and weaknesses |
5c. Incorporate Consensus
Synthesize:
- Areas of agreement — high confidence findings
- Areas of disagreement — flag for nuance
- Missing perspectives — gaps identified by any model
- Confidence adjustments — raise/lower based on feedback
Phase 6: Generate Report
# Deep Research Report: <Topic>
**Generated**: <date>
**Depth**: <shallow|medium|deep>
**Sources consulted**: <N>
**Models consulted**: <list>
---
## Executive Summary
<3-5 paragraph synthesis for general audience>
---
## Research Question
<Original topic and how it was decomposed>
---
## Key Findings
### Finding 1: <Title>
**Confidence**: High | Medium | Low
**Consensus**: Agreed | Mixed | Disputed
<Detailed finding with inline source citations>
**Sources**: [Source 1](url), [Source 2](url)
---
## Analysis
### Themes and Patterns
<Cross-cutting themes across sources>
### Contradictions and Debates
<Where sources/models disagreed>
### Information Gaps
<What remains unclear>
---
## Model Consensus
| Model | Stance | Confidence | Key Feedback |
|-------|--------|------------|--------------|
| <model_1> | For | X/10 | <summary> |
| <model_2> | Against | X/10 | <summary> |
| <model_3> | Neutral | X/10 | <summary> |
**Agreement Areas**: <where all models agreed>
**Divergent Views**: <where models differed>
---
## Sources
| # | Title | URL | Relevance |
|---|-------|-----|-----------|
| 1 | <title> | <url> | <contribution> |
---
## Methodology
1. **Web search** via Rube MCP (<N> searches)
2. **Deep analysis** via PAL ThinkDeep
3. **Multi-model consensus** via PAL Consensus (<N> models)
Phase 7: Output
- If
--output <filepath>: Write report to file, confirm path - Otherwise: Display full report in console
End with summary:
Research complete:
- Topic: <topic>
- Sources: <N> web sources
- Models: <N> reached consensus
- Confidence: <overall assessment>
- Key takeaway: <1-sentence summary>
MCP Integration
PAL MCP
| Tool | Phase | Purpose |
|---|---|---|
mcp__pal__thinkdeep | Analysis | Multi-stage hypothesis testing |
mcp__pal__consensus | Validation | Multi-model cross-validation |
mcp__pal__listmodels | Discovery | Available models for consensus |
mcp__pal__challenge | Validation | Critical thinking on controversial claims |
Rube MCP
| Tool | Phase | Purpose |
|---|---|---|
mcp__rube__RUBE_SEARCH_TOOLS | Discovery | Find search/scraping tools |
mcp__rube__RUBE_GET_TOOL_SCHEMAS | Discovery | Load full schemas if needed |
mcp__rube__RUBE_MULTI_EXECUTE_TOOL | Search | Parallel web searches |
mcp__rube__RUBE_MANAGE_CONNECTIONS | Auth | Connect search integrations |
mcp__rube__RUBE_REMOTE_BASH_TOOL | Processing | Handle large response data |
Error Handling
| Scenario | Action |
|---|---|
| No search tools found | Fall back to WebFetch for direct URLs |
| Connection not active | Call RUBE_MANAGE_CONNECTIONS, present auth link |
| Search returns no results | Reformulate with broader terms |
| Tool schema missing | Call RUBE_GET_TOOL_SCHEMAS first |
| PAL MCP unavailable | Skip consensus, report from web research only |
| ThinkDeep fails | Continue with raw findings |
| Rate limiting | Reduce parallel batch size to 2-3 |
Guardrails
- Present research plan before executing searches
- Preserve all source URLs for verifiability
- Search multiple perspectives for controversial topics
--depth deepcan make 15-25+ API calls — use judiciously- If Rube unavailable but PAL available, degrade to analysis-only via
WebFetch
Tool Coordination
- WebFetch - Fallback URL fetching when Rube unavailable
- Write - Save report to file
- Bash - File operations
- PAL MCP - Analysis, consensus, challenge
- Rube MCP - Web search, URL extraction
FAQ
What does the sc-research skill do?
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.
How do I install sc-research?
Run npx skills add https://github.com/tony363/superclaude --skill sc-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sc-research/ (personal) or .claude/skills/sc-research/ (project).
Which AI agents can use sc-research?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is sc-research safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains sc-research?
It is published by tony363 in tony363/superclaude. It has 69 recorded installs.
More from tony363/superclaude
agent-performance-engineer
tony363/superclaude
Expert performance engineer specializing in system optimization, bottleneck identification, and scalability engineering. Masters performance testing, profiling, and tuning across applications, databases, and infrastructure with focus on achieving optimal response times and resource efficiency.
agent-security-engineer
tony363/superclaude
Expert infrastructure security engineer specializing in DevSecOps, cloud security, and compliance frameworks. Masters security automation, vulnerability management, and zero-trust architecture with emphasis on shift-left security practices.
sc-estimate
tony363/superclaude
Provide development estimates for tasks, features, or projects with intelligent analysis. Use when planning timelines, assessing complexity, or scoping resources.
agent-fullstack-developer
tony363/superclaude
End-to-end feature owner with expertise across the entire stack. Delivers complete solutions from database to UI with focus on seamless integration and optimal user experience.
sc-design
tony363/superclaude
Design system architecture, APIs, and component interfaces with comprehensive specifications. Use when planning architecture, designing APIs, creating component interfaces, or modeling databases.
agent-technical-writer
tony363/superclaude
Expert technical writer specializing in clear, accurate documentation and content creation. Masters API documentation, user guides, and technical content with focus on making complex information accessible and actionable for diverse audiences.
Popular productivity & automation skills
agent-browser
vercel-labs/agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
lark-doc
open.feishu.cn
飞书云文档(Docx / Wiki)内容操作:读取、创建、编辑文档,插入或下载图片附件,以及操作思维笔记。用户提供文档 URL/token(包括 doubao.com 的 /docx/、/wiki/)时使用;按 URL 路径/token 而非域名路由;/page/ 是妙搭应用,非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive;随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。
lark-base
open.feishu.cn
飞书多维表格(Base)操作:建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式(BaseApp/AppMode 页面与组件)、Workspace 目录、workflow、角色权限、模板中心(多维表格模板分类/列表/搜索);遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps;文件导入/导出转 lark-drive,认证/授权转 lark-shared。
lark-drive
open.feishu.cn
飞书云空间(云盘/云存储):管理 Drive 文件和文件夹,包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签(secure labels)和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题,或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用;doubao.com 云空间 URL/token 也按资源路径和 token 路由,不回退 WebFetch。不负责:文档内容编辑(走 lark-doc)、表格/Base 表内数据操作(走 lark-sheets/lark-base)、知识空间节点/成员管理(走 lark-wiki)、原生 Markdown 文件读写/patch/diff(走 lark-markdown)。
lark-shared
open.feishu.cn
Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.
lark-im
open.feishu.cn
飞书即时通讯:收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片(Interactive Card)、监听卡片按钮回调(card.action.trigger)。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶(添加/移除/查询置顶会话)、管理标签数据、处理卡片回调时使用。