<!-- Generated from workflows/research-answer.md by tools/generate_integrations.py. Edit the source file, not this one. This is an execution WORKFLOW packaged as an Agent Skill so it is discoverable and directly invocable ("/research-answer") in every compatible tool. -->

Workflow: Research-then-Answer

When to Use

Criteria	Match
Intention	information_query, fact_check, research
Complexity	varies: from simple FAQ to deep research
Power Mode	Eco (simple) or Balanced/Deep (complex)
Clarity	user wants information, not code changes

Required Skills

• web-search-research
• browser-automation (if interactive browsing is needed)
• communication-tone
• safety-profiles (default; relaxed if OSINT/security research)

Flow

Step 0: Thinking Stage (Pre-Action)

• What exactly is the user asking? Factual question, comparison, deep research?
• Is this time-sensitive? (current events, recent changes = MUST search)
• What do I already know vs what do I need to verify?
• Never fabricate citations or sources: if unsure, search

Step 1: Determine Research Depth

• Simple fact: single search, direct answer
• Moderate topic: search → read → synthesize
• Deep research: multi-query chain, cross-reference, evaluate sources

Step 2: Search

• Use dedicated search tools (not google.com via browser)
• For time-sensitive queries: search before answering regardless
• For niche topics: use specific queries
• Chain searches: use findings to inform next query

# Simple: one search
search_web("current CEO of OpenAI 2026")

# Deep: chain searches
search_web("transformer architecture limitations long context")
→ find "attention quadratic complexity"
search_web("sparse attention methods long context 2025 2026")
→ compare approaches
search_web("RingAttention vs sparse attention benchmarks")
→ get performance data

Step 3: Read & Extract

• Use search results as starting points
• Visit authoritative sources directly
• Extract relevant facts with source attribution
• For long pages: use text extraction over scrolling

Step 4: Synthesize

• Compare multiple sources
• Resolve contradictions (flag if can't)
• Distinguish: confirmed facts vs likely vs speculation
• Organize by relevance to user's question

Step 5: Cite & Answer

• Every factual claim gets a citation: [source:N]
• Answer directly: don't start with "Great question!"
• Structure: summary first, then details
• For complex topics: use headings to organize
• Offer to go deeper if user wants more

Step 5b: Sanity Check

• Is every factual claim supported by a real citation?
• Are citations inline (not a bibliography at end)?
• Did I search before answering if info may have changed?
• Am I distinguishing confirmed facts from speculation or uncertainty?
• Have I presented multiple viewpoints for contested topics?
• Never cite fabricated IDs: verify each citation exists

Step 6: Final Answer

• Wrap final answer in <answer> tags
• NEVER include bibliography at end: citations are inline only
• NEVER cite fabricated information

Token Budget

• Simple fact: 1K-3K tokens
• Moderate research: 5K-15K tokens
• Deep research: 20K-50K tokens
• Warning threshold: >50K → offer to continue in next session

Hallucination Watchpoints

• Fabricating citations (most dangerous error)
• Overconfident claims from single source
• Confirmation bias: only citing sources that agree
• Presenting speculation as fact
• Not searching when info is time-sensitive
• Relying on training data for current events