Multi-agent parallel research orchestrator. Decomposes any research topic into 3-6 specialized angles using MECE principles, spawns Opus sub-agents to cover each angle simultaneously, runs gap analysis, then synthesizes findings into one comprehensive vault document.
Anthropic’s own multi-agent research system outperforms single-agent by 90.2%. This skill applies those same proven patterns.
For detailed sub-agent prompt templates by research type: read references/agent-templates.md.
For the full research on multi-agent orchestration patterns: read references/orchestration-patterns.md.
Olle Dyberg — AI content creator and consultant (@olleai on TikTok, ~9K followers, 1.1M+ views) building around Claude Code, AI agents, and context engineering. Research serves: content creation, meta-prompts, consulting prep, and product development.
Every research session follows this shape:
[Broad: Decompose question]
/ \
[Narrow: 3-5 parallel agents] ← Wave 1
\ /
[Evaluate: Gap analysis]
/ \
[Deep: 1-2 targeted agents] ← Wave 2 (if needed)
\ /
[Synthesize: Final report]
Start wide, go narrow in parallel, identify gaps, go deep on gaps, synthesize.
Before anything else, note today’s date. You can get it from the system prompt or by running date. Inject this date into every sub-agent prompt so they search for current information and properly date the final document. This is critical — sub-agents without date context will search for and cite outdated information.
Before spawning any agents:
05 - Resources/People/Olle.md — understand who the research is for_Index.md — scan for existing vault content on the topicAskUserQuestion tool to ask:
Break the topic into Mutually Exclusive, Collectively Exhaustive angles. Each angle:
Common decomposition patterns:
| Research Type | Typical Angles |
|---|---|
| Content/Platform | General best practices, Niche-specific (AI/tech), Real examples with metrics, Psychology/copywriting, Vault knowledge, X discourse |
| Technology | Current state/ecosystem, Real shipped code (grep MCP), Community sentiment (X), Comparisons/alternatives, Vault knowledge, Implementation patterns |
| Business | Market data/benchmarks, Niche-specific practices, Strategy frameworks, Vault knowledge, X practitioner discourse |
Scale effort to complexity:
Spawn minimum 3, ideally 4-5 sub-agents in parallel using the Task tool. Always use model: "opus" for all research sub-agents. Research quality depends on reasoning depth — sonnet is not sufficient.
Every sub-agent prompt MUST include these 6 elements:
WHO — “This research is for Olle Dyberg, a 25-year-old Swedish AI content creator and consultant (@olleai, ~5.5K TikTok followers, 1.1M+ views). His niche is AI tools — Claude Code, agents, context engineering.”
WHY — The specific purpose. “…because Olle will feed this into a meta-prompt” or “…because this becomes a vault reference document.” Agents that know WHY produce dramatically better results.
WHAT ANGLE — Specific scope AND explicit boundaries: “Cover X. Do NOT cover Y — another agent handles that.”
HOW — Which tools to use (see Tools Reference below)
SEARCH STRATEGY — “Start with SHORT, BROAD queries (2-4 words). Evaluate results. Then progressively narrow focus. Do NOT start with long, specific queries — they return poor results.”
SOURCE QUALITY — “Prefer: practitioner blogs, official docs, academic papers, primary sources. Avoid: SEO content farms, listicles, aggregator sites.”
You are researching [ANGLE] for Olle Dyberg, a 25-year-old Swedish AI content creator
and consultant (@olleai, ~5.5K TikTok followers, 1.1M+ views). His niche is AI tools —
Claude Code, AI agents, and context engineering. He also does AI consulting and builds
digital products.
TODAY'S DATE: [INSERT CURRENT DATE, e.g. 2026-02-10]
PURPOSE: [WHY this research matters — what Olle will do with it]
YOUR ANGLE: [SPECIFIC SCOPE — what you cover]
BOUNDARIES: [What you do NOT cover — other agents handle those angles]
SEARCH STRATEGY:
- Start with short, broad queries (2-4 words)
- Evaluate what's available, then progressively narrow
- Cross-reference claims across multiple sources
- Aim for 2+ independent sources per key finding
SOURCE QUALITY: Prefer practitioner blogs, official docs, engineering posts, primary
sources. Avoid SEO content farms and listicles.
TOOLS TO USE: [SPECIFIC tools for this angle]
QUALITY BAR: Be exhaustive. Extract every actionable insight, specific number, concrete
example, framework, and contrarian take. Density matters — thin research is useless.
Include sources/URLs for everything. If you find only 3 bullet points, you failed.
OUTPUT FORMAT:
## Key Findings
[Numbered list with inline source citations]
## Evidence Quality
[Which findings are well-sourced vs. speculative]
## Contradictions Found
[Any conflicting information between sources]
## Notable Quotes
[Direct quotes from authoritative sources with attribution]
## Sources
[Full list with URLs]
| Angle Type | subagent_type | model | Tools |
|---|---|---|---|
| Web research | general-purpose |
opus |
mcp__exa__web_search_exa, WebSearch, WebFetch |
| Vault search | Explore |
opus |
Glob, Grep, Read on vault path |
| Code examples | general-purpose |
opus |
mcp__grep__searchGitHub |
| X Research | Bash |
opus |
x-research CLI (see below) |
X Research CLI:
cd ~/clawd/skills/x-research && source ~/.config/env/global.env
bun run x-search.ts search "<query>" --quality --quick
After ALL sub-agents return (batch — do not process one-at-a-time to avoid anchoring bias):
Cross-reference all findings and produce ONE comprehensive document. Do NOT simply concatenate agent outputs — synthesize them into something greater than the sum of parts.
File location: /mnt/c/Users/olled/Documents/Obsidian/Notes/02 - Content/Research/[Topic Folder]/[Document Name] [Year].md
Topic folder organization: Group all research outputs into a topic subfolder within 02 - Content/Research/. If a research session produces multiple files (master synthesis + companion documents from sub-agents), they ALL go in the same topic folder. Create the folder if it doesn’t exist.
| Research Topic | Folder |
|---|---|
| YouTube strategy, algorithm, scripting, titles, SEO, case studies | Research/YouTube/ |
| TikTok hooks, growth, scripts, descriptions | Research/TikTok/ |
| Meta-prompting, prompt engineering, scriptwriter optimization | Research/Meta-Prompting/ |
| New topic that doesn’t fit existing folders | Research/[New Topic Name]/ |
| One-off research that doesn’t warrant its own folder | Research/Other/ |
Rules:
ls "02 - Content/Research/") — use an existing folder if the topic fits_Index.md, group entries under ### Research/[Folder]/ headersDocument structure:
# [Topic Name] [Year]
Research compiled for @olleai ([niche context]). [N] parallel research tracks synthesized.
**Purpose**: [What this research will be used for]
**Date**: [Current date]
**Sources**: [N] web sources, [N] X posts, [N] vault references, [N] code examples
---
## Executive Summary
[3-5 bullet points — the most important findings]
---
## Part N: [Angle Name]
### [Sub-topic]
[Dense, actionable content with specific numbers and examples]
---
## Niche-Specific Applications
[How findings apply to Olle's AI/tech niche specifically]
---
## Contradictions & Open Questions
[Where sources disagreed, what remains unresolved]
---
## Key Takeaways
[Numbered list of the most actionable insights]
---
## Sources
[All sources cited, organized by section]
Quality gate — do NOT save until ALL pass:
After saving, update _Index.md if a new file was created in a location not yet indexed.
| Tool | Use For | Notes |
|---|---|---|
mcp__exa__web_search_exa |
Current web information | Best for recent articles, guides |
WebSearch |
Quick web lookups | Good for current events, dates |
mcp__grep__searchGitHub |
Real code patterns from 1M+ repos | Search for actual code, not keywords |
WebFetch |
Deep-dive specific URLs | Use after finding promising links |
| x-research CLI | X/Twitter discourse | Creator opinions, recent changes |
| Glob/Grep on vault | Existing vault knowledge | Always check first |