Research Skill
SkillDev toolsCompany due diligence, technology deep-dives, market analysis, and topic exploration for cyber•Fund investment decisions, content creation, and personal projects. Supports 3 intensity levels (quick/standard/deep) for speed-quality tradeoffs.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Research Skill skill
What this skill tells your AI
The instructions your AI receives, as published by gerstep/cybos in .claude/skills/Research/SKILL.md and read by ahel’s review.
Company due diligence, technology deep-dives, market analysis, and topic exploration for cyber•Fund investment decisions, content creation, and personal projects.
Capabilities
- Company Research: Comprehensive DD on target companies
- Technology Research: Deep technical analysis of technologies
- Market Research: Market sizing, dynamics, and opportunity assessment
- Topic Research (Content): Ideas, narratives, people for essays/tweets
- Topic Research (Investment): Market dynamics and opportunities for investment thesis
Research Intensity Levels
- 🔍 Quick (10-30s): 1 agent
- 🔬 Standard (2-5m): 2-3 agents [DEFAULT]
- 🔎 Deep (5-15m): 3-5 agents + quality-reviewer
See shared/intensity-tiers.md for full specification.
Workflow
All research types use one universal workflow:
workflows/orchestrator.md
The orchestrator dynamically selects agents based on research type and intensity.
Agent Selection
See shared/agent-selection-matrix.md for full matrix.
| Research Type | Quick | Standard | Deep |
|---|---|---|---|
| Company DD | company | company + market + financial | +team +quality-reviewer |
| Technology | tech | tech + market | +company +quality-reviewer |
| Market | market | market + financial | +company +quality-reviewer |
| Topic-Content | content | content | +quality-reviewer |
| Topic-Investment | investment | investment + market | +financial +quality-reviewer |
Agents
Research agents (autonomous MCP access):
company-researcher: Business model, product, tractionmarket-researcher: TAM, dynamics, trendsfinancial-researcher: Funding, metrics, comparablesteam-researcher: Founder backgrounds, team assessmenttech-researcher: Technology deep-divescontent-researcher: Academic papers, social media, first-principles (for content)investment-researcher: Market dynamics, opportunities, timing (for investment)
Quality & Synthesis:
quality-reviewer: Gap analysis, contradiction detection (deep only, max 1 iteration)synthesizer: Consolidate parallel research outputs
Common References
shared/agent-selection-matrix.md- Dynamic agent selectionshared/investment-lens.md- cyber•Fund investment philosophyshared/mcp-strategy.md- MCP tool selectionshared/output-standards.md- Formats and emoji conventionsshared/intensity-tiers.md- 3-tier intensity spec
Output Locations
All research creates timestamped workspace:
~/CybosVault/private/deals/<company>~/CybosVault/private/research/MMDD-<slug>-YY/ # Company
~/CybosVault/private/research/<topic>/MMDD-<slug>-YY/ # Tech/Market/Topic
├── raw/ # Agent outputs
└── report.md # Final synthesis
Key Principles
- Agents do ALL data gathering - Main session orchestrates, agents make MCP calls
- No redundancy - Each agent makes its own calls autonomously
- Dynamic selection - Agents chosen based on research type + intensity
- Quality loop - Deep mode includes quality-reviewer (max 1 iteration)
Investment Context
All research applies cyber•Fund's investment philosophy:
- Path to $1B+ revenue (not niche $50M ARR outcomes)
- Defensible moat (data, network effects, hard tech)
- Clear business model (revenue > token speculation)
- Strong founders (high energy, sales DNA, deep expertise)
- Market timing ("why now?")
Signals
- GitHub stars
- 105
- Forks
- 22
- Last commit
- Sep 2026
- Hacker News mentions
- 20
Advanced
- Catalog kind
- skill
- Gateway key
research-gerstep- Source
- github.com/gerstep/cybos