research

Research — Structured Investigation

Systematic research methodology for deep investigations. Provides structured workflows for ML research, competitor analysis, domain intelligence, market research, and data source discovery. Uses /firecrawl for web search and scraping.

Note: If a project-specific research config exists at .claude/skills/research/project-context.md, load it for project context. Individual projects may define domain-specific research briefs, search queries, and competitor lists that extend these generic templates.

When to Use

• Investigating ML techniques (papers, Kaggle solutions, new algorithms)
• Analyzing competitors in your market
• Researching domain changes (regulations, industry trends)
• Finding new data sources (APIs, databases, public datasets)
• Market research (market size, user demographics, pricing)
• Any multi-source investigation that needs synthesis

When NOT to Use

• Simple factual lookups (just use firecrawl search directly)
• Code-level debugging or implementation (use other tools)
• News scraping for content publishing (use /firecrawl + /seo-content)

Research Methodology

Phase 1: Scope

Define the research question clearly before searching.

## Research Brief
- **Question**: [Specific question to answer]
- **Why it matters**: [Impact on the project — model improvement, revenue, UX, strategy]
- **Depth**: Quick (15 min) | Standard (30 min) | Deep (1+ hr)
- **Output**: [What deliverable — findings doc, code prototype, decision recommendation]
- **Known context**: [What we already know — avoid re-searching]

Phase 2: Search Strategy

Plan searches before executing. Different research types need different strategies.

Academic / ML Research:

# arXiv papers
firecrawl search "[topic] machine learning site:arxiv.org" --limit 10 -o .firecrawl/research/arxiv-results.json --json

# Google Scholar (via web search)
firecrawl search "[technique] prediction ranking 2024 2025" --limit 10 -o .firecrawl/research/scholar-results.json --json

# Papers with Code
firecrawl scrape "https://paperswithcode.com/task/[task-name]" -o .firecrawl/research/pwc.md

# Kaggle
firecrawl search "kaggle [domain] prediction winning solution" --limit 10 -o .firecrawl/research/kaggle-results.json --json

Competitor Research:

# Map competitor sites
firecrawl map https://competitor-a.com --search "[feature]" --limit 50 -o .firecrawl/research/competitor-a-urls.txt
firecrawl map https://competitor-b.com --search "[feature]" --limit 50 -o .firecrawl/research/competitor-b-urls.txt

# Scrape key pages
firecrawl scrape "https://competitor.com/pricing" --only-main-content -o .firecrawl/research/competitor-pricing.md

# Search for competitors
firecrawl search "[product category] [market/country]" --limit 20 -o .firecrawl/research/competitors.json --json

Domain Research:

# Industry news and regulation changes
firecrawl search "[industry] regulations changes 2025 2026" --limit 10 -o .firecrawl/research/regulations.json --json

# Industry trends
firecrawl search "[industry] statistics trends" --limit 10 -o .firecrawl/research/industry.json --json

# International parallels
firecrawl search "[similar product/market] international comparison" --limit 10 -o .firecrawl/research/international.json --json

Market Research:

# Market size
firecrawl search "[market] market size revenue 2025" --limit 10 -o .firecrawl/research/market.json --json

# User behavior
firecrawl search "[product category] user demographics behavior" --limit 10 -o .firecrawl/research/users.json --json

Phase 3: Gather & Read

Execute searches, then read and extract key findings. Always use .firecrawl/research/ for organization.

# Create research directory for this investigation
mkdir -p .firecrawl/research/[topic-slug]

# Search → identify promising URLs → scrape the best ones
firecrawl search "query" --limit 10 -o .firecrawl/research/[topic]/search.json --json

# Read search results, pick top 3-5 URLs
# Scrape each in parallel
firecrawl scrape "https://url1" --only-main-content -o .firecrawl/research/[topic]/source1.md &
firecrawl scrape "https://url2" --only-main-content -o .firecrawl/research/[topic]/source2.md &
firecrawl scrape "https://url3" --only-main-content -o .firecrawl/research/[topic]/source3.md &
wait

Reading large scraped files:

# Never read entire files — use targeted extraction
wc -l .firecrawl/research/[topic]/source1.md
head -50 .firecrawl/research/[topic]/source1.md
grep -n "keyword" .firecrawl/research/[topic]/source1.md
grep -A 10 "## Relevant Section" .firecrawl/research/[topic]/source1.md

Phase 4: Triangulate

Cross-verify findings across sources. Don't trust a single source.

• Consensus: Do 2+ sources agree? -> High confidence finding
• Contradiction: Sources disagree? -> Note the disagreement, investigate further
• Single source: Only one source? -> Flag as unverified, lower confidence
• Recency: Prefer 2024-2026 sources over older material
• Credibility: Academic papers > industry blogs > forum posts > AI-generated content

Phase 5: Synthesize & Report

Produce actionable findings, not literature summaries. Every finding should answer: "So what? What should we do differently?"

Output Format

Save findings to tasks/research_findings.md (append, don't overwrite).

## [Research Topic] — [Date]

### Question
[What we investigated]

### Key Findings

**Finding 1: [Actionable title]**
- Evidence: [What sources say, with URLs]
- Confidence: HIGH/MEDIUM/LOW
- Action: [Specific implementation step or decision]

**Finding 2: [Actionable title]**
- Evidence: [...]
- Confidence: [...]
- Action: [...]

### Contradictions / Open Questions
- [Unresolved disagreements between sources]
- [Things we couldn't verify]

### Sources
1. [Title] — [URL] — [Credibility: Academic/Industry/Blog/Forum]
2. [...]

### Recommended Next Steps
1. [Most impactful action]
2. [Second priority]
3. [Third priority]

Research Templates

ML Research

## Research Brief
- Question: Can [technique] improve [model metric] beyond [current baseline]?
- Known context: Current model is [type], [N] features, [metric] = [value]
- Search targets:
  1. arXiv papers on [technique] for ranking/prediction
  2. Kaggle competitions using [technique]
  3. Blog posts with implementation details
  4. GitHub repos with working code
- Output: Findings + prototype feasibility assessment

Key searches for ML research:

• "learning to rank" [domain] prediction — ranking models
• "isotonic regression" vs "platt scaling" calibration — calibration methods
• XGBoost [technique] 2024 2025 — latest improvements
• "feature engineering" tabular prediction competition — Kaggle patterns
• [domain] prediction deep learning transformer — neural approaches
• "model combination" probability ensemble — ensemble/blending research

Competitor Analysis

## Research Brief
- Question: What do competitors in [market] offer vs our product?
- Search targets:
  1. All sites ranking for [primary keyword] in [target market]
  2. Their pricing, features, accuracy claims
  3. Their content strategy (frequency, format, depth)
  4. Their social media presence
- Output: Competitive landscape matrix + feature gaps

Competitor analysis framework:

| Dimension | What to Find | |-----------|-------------| | Product | Free vs paid features, unique capabilities | | Pricing | Monthly/annual, tiers, free tier scope | | Content | Article frequency, depth, expert profiles | | Data | What analytics/stats do they show? | | Technology | Do they claim AI/ML? What kind? | | UX | Mobile experience, speed, design quality | | Trust | Track record claims, transparency, social proof | | SEO | What keywords do they rank for? Content volume |

Market Research