| name | research |
| description | Deep research methodology for sub-agents and spawn tasks. Use when spawning research tasks, investigating claims, comparing products/services, or gathering comprehensive information on any topic. Ensures source diversity, cross-referencing, bias detection, and confidence assessment. Automatically applied to research-type spawns. |
Research Skill
Orchestration Pattern
Model allocation (see workspace SKILLS.md for actual model assignments):
- Research main agent: Deep analysis model — thorough, accurate
- Research sub-agents: Fast parallel model — cheap, high throughput
- Global fallback: If main model fails, automatically use default model
Model Configuration:
- Main agent (researcher): Uses the deep analysis model for synthesis
- Sub-agents (collectors): Use the fast model for parallel source gathering
- This overrides global defaults — research tasks always use this allocation
Why this pairing:
- Deep analysis model: Slow but thorough, checks local files + web, high accuracy for synthesis
- Fast parallel model: Cheap and fast, perfect for gathering raw sources in parallel
When to parallelize:
- Spawn separate sub-agents for unrelated topics within a research task
- Keep related aspects in same sub-agent to preserve context
- Each collector returns structured findings; main agent synthesizes
Example flow:
- Main agent receives research task
- Identifies unrelated aspects (e.g., "pricing" vs "technical specs")
- Spawns fast sub-agents in parallel (one per topic) for source gathering
- Each returns findings in standard format
- Main agent synthesizes, cross-references, assigns confidence
Methodology
Follow these steps for every research task:
1. Query Diversity & Web Search Tool
Use the web search tool configured in workspace SKILLS.md — no rate limits, can spam freely.
- Try multiple phrasings of the search query (synonyms, different angles)
- Spawn fast sub-agents to run searches in parallel
- Include platform-specific searches when applicable:
site:reddit.com for community opinions and real experiencessite:github.com for technical projects, issues, discussions- Official product/company sites for authoritative info
2. Source Volume
- Fetch at least 10 sources before drawing conclusions
- Include date filters:
2025 and 2026 to ensure recency
- Flag any source older than 6 months as potentially outdated
3. Source Classification
For each source, identify:
- Primary: Official docs, announcements, product pages, API references
- Secondary: Blogs, articles, tutorials, reviews
- Community: Reddit, HN, forums, GitHub discussions
Prioritize primary sources for factual claims.
4. Bias Detection
Flag sources exhibiting:
- PR/marketing speech (vague superlatives, no specifics)
- Point of sale (affiliate links, sponsored content)
- Clickbait (sensational headlines, thin content)
- Outdated info presented as current
Note bias in findings; don't discard biased sources entirely—they can still contain useful data.
5. Cross-Referencing
- Every factual claim must appear in 2+ independent sources
- If only one source makes a claim, mark as "unverified"
- Conflict detection: Explicitly note when sources contradict each other
6. Knowledge Gaps
Acknowledge what you couldn't find or verify:
- "No official source confirms X"
- "Pricing info only found on third-party aggregators"
- "No data available after [date]"
7. Confidence Assessment
Rate each major finding:
- High: 3+ primary sources agree, recent, no conflicts
- Medium: 2+ sources agree, or primary + secondary alignment
- Low: Single source, outdated, or conflicting information
- Unverified: Claim found but not corroborated
Output Format
Structure all research findings as:
## Executive Summary
[2-3 sentence answer to the research question]
## Key Findings
- Finding 1 [Confidence: High/Medium/Low]
- Finding 2 [Confidence: High/Medium/Low]
...
## Conflicts & Contradictions
[Note any sources that disagree and what they disagree on]
## Knowledge Gaps
[What couldn't be verified or found]
## Sources
1. [Title](URL) - Primary/Secondary/Community - [Date if known]
2. ...
Quick Reference
| Step | Requirement |
|---|
| Queries | Multiple phrasings, include Reddit/GitHub |
| Sources | Minimum 10 before conclusions |
| Dates | Filter for 2025-2026, flag >6mo old |
| Cross-ref | 2+ sources per claim |
| Bias | Flag PR, affiliate, clickbait |
| Conflicts | Explicitly note contradictions |
| Gaps | State what's unknown |
| Confidence | High/Medium/Low/Unverified per finding |
