Guide agents through structured research including planning, multi-query execution, source analysis, and synthesis. Use for comprehensive topic research, deep investigation, or creating research reports. Keywords: research, investigate, deep dive, comprehensive, analysis, synthesis, report.
npx skills add https://github.com/jwynia/agent-skills --skill research-workflow
A structured methodology for conducting comprehensive research. This skill guides you through planning, executing, analyzing, and synthesizing research on any topic.
Use this skill when:
Do NOT use this skill when:
Before using this skill, ensure:
┌─────────────────────────────────────────────────────────────┐
│ RESEARCH WORKFLOW │
├─────────────────────────────────────────────────────────────┤
│ │
│ 1. PLANNING 2. EXECUTION │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Define │ │ Run searches │ │
│ │ questions │───>│ Evaluate │ │
│ │ Plan queries │ │ sources │ │
│ └──────────────┘ └──────────────┘ │
│ │ │ │
│ v v │
│ 3. ANALYSIS 4. SYNTHESIS │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Organize │ │ Create │ │
│ │ findings │───>│ coherent │ │
│ │ Find patterns│ │ output │ │
│ └──────────────┘ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Before any searches, establish a clear research plan.
Convert the topic into specific, answerable questions.
Example:
Break down the main topic into searchable components:
Create a search plan with query progression:
Use the template at assets/research-plan-template.md to document your plan.
Execute your search plan systematically.
Execute queries in order, using appropriate search parameters:
# Broad overview
web-search "AI in healthcare overview 2024"
# Specific deep dive
web-search "AI diagnostic imaging applications" --depth advanced
# Current news
web-search "AI healthcare regulations 2024" --topic news --time month
For each search, record:
Use the checklist at assets/source-evaluation-checklist.md to assess:
Credibility Indicators:
Quality Signals:
Research is not linear. Based on findings:
Organize and analyze your collected findings.
Organize results into categories:
Look for:
For each finding, determine confidence level:
Document:
Create coherent, useful output from your analysis.
Choose appropriate format based on use case:
Use the template at assets/research-report-template.md.
Key principles:
End with practical outputs:
Phase 1 - Planning:
Research Question: What are the best practices for API versioning?
Sub-questions:
1. What versioning strategies exist?
2. What are pros/cons of each?
3. What do major companies use?
4. What do experts recommend?
Search Plan:
- "API versioning strategies comparison"
- "REST API versioning best practices 2024"
- "API versioning header vs URL vs query parameter"
- "large companies API versioning approach"
Phase 2 - Execution:
Query 1: "API versioning strategies comparison"
- Found: URL versioning, header versioning, query parameter
- Key insight: URL versioning most common, header more "RESTful"
- Sources: REST API tutorial, Martin Fowler blog
Query 2: "REST API versioning best practices 2024"
- Found: Semantic versioning principles apply
- Key insight: Version only when breaking changes
- Sources: API design guides, Stack Overflow discussions
Phase 3 - Analysis:
Consensus Points:
- Version only for breaking changes
- Be consistent within an API
- Document version lifecycle
Conflicts:
- URL vs header placement (no clear winner)
- When to deprecate old versions
Gaps:
- Limited data on performance impact
- Few studies on developer experience
Phase 4 - Synthesis:
Key Findings:
1. Three main strategies exist (URL, header, query param)
2. URL versioning is most common and discoverable
3. Header versioning is considered more "pure" REST
4. Version only on breaking changes
5. Major companies split between approaches
Recommendations:
- Use URL versioning for public APIs (discoverability)
- Consider header versioning for internal APIs
- Document deprecation timeline clearly
- Use semantic versioning principles
Before completing research, verify:
For detailed guidance, see:
This workflow has the following limitations:
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This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
Research ideation partner. Generate hypotheses, explore interdisciplinary connections, challenge assumptions, develop methodologies, identify research gaps, for creative scientific problem-solving.
Manage and trigger pre-built Zapier workflows and MCP tool orchestration. Use when user mentions workflows, Zaps, automations, daily digest, research, search, lead tracking, expenses, or asks to "run" any process. Also handles Perplexity-based research and Google Sheets data tracking.
Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
Loop 2 of the Three-Loop Integrated Development System. META-SKILL that dynamically compiles Loop 1 plans into agent+skill execution graphs. Queen Coordinator selects optimal agents from 86-agent registry and assigns skills (when available) or custom instructions. 9-step swarm with theater detection and reality validation. Receives plans from research-driven-planning, feeds to cicd-intelligent-recovery. Use for adaptive, theater-free implementation.
Loop 1 of the Three-Loop Integrated Development System. Research-driven requirements analysis with iterative risk mitigation through 5x pre-mortem cycles using multi-agent consensus. Feeds validated, risk-mitigated plans to parallel-swarm-implementation. Use when starting new features or projects requiring comprehensive planning with <3% failure confidence and evidence-based technology selection.
Take jwynia/research-workflow from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.