| name | deep-research |
| description | Multi-lens research engine — one question, 9 angles, synthesized analysis. Uses ~/research-skill-graph/ as the knowledge base. Load this skill when given a research question and use it to produce deep, structured analysis. Invoke by saying "do deep research on [question]". |
| keywords | ["deep-research","deep research","analysis","multi-lens","research","synthesis","strategy"] |
| version | 1.2.0 |
| author | Hermes Community |
| license | mit |
| related_skills | ["last30days"] |
Deep Research
A local research engine that takes ONE question and produces multi-angle analysis no single Google search or prompt could match.
Knowledge base: ~/research-skill-graph/
Invocations: say "do deep research on [your question]" or "/skill deep-research" then ask your question
How It Works
The system forces structured thinking through 9 research lenses, each rethinking the question from a fundamentally different angle. Lenses are defined in the skill graph folder and evolve over time.
The 9 Lenses (in execution order):
- technical — mechanics, data, hard numbers. Strip away narrative.
- economic — money flows, incentives, cost structures, who pays/profits.
- historical — patterns, precedent, what failed before.
- business — competitive landscape, unit economics, who's winning/losing.
- strategic — key moves, leverage points, game theory. What matters in 3-10 years.
- customer — real buyer vs. user, JTBD, trust signals, purchase blockers.
- product — capabilities, limits, failure modes, MVPs.
- contrarian — stress-test the consensus. Who benefits from the current narrative?
- first-principles — rebuild from ground truth. Forget assumptions.
Execution Protocol
Execution Protocol
When you receive a research question:
Step 1: Read the command center at ~/research-skill-graph/index.md — it contains the full briefing template and node map.
Step 2: Read methodology/research-frameworks.md to pick the right approach for the question type:
- "Is X true?" → Verification framework
- "Why is X happening?" → Causal analysis framework
- "What happens if X?" → Scenario planning framework
- "What should I do about X?" → Decision support framework
Step 3: Read methodology/source-evaluation.md — apply the 5-tier trust system to every source:
- Tier 1: Primary data (raw datasets, peer-reviewed studies)
- Tier 2: Expert analysis (research institutions, long-form journalism)
- Tier 3: Informed commentary (expert blogs, think tank reports)
- Tier 4: General media (major news, Wikipedia — verify upstream)
- Tier 5: Social/anecdotal (Twitter, Reddit — signal detection only)
Step 4: Run ALL 9 lenses. For each lens:
a. Read the lens file
b. Research the topic THROUGH that lens only
c. Record findings, sources, and confidence level
d. Note contradictions with previous lenses
Step 5: Read methodology/contradiction-protocol.md — resolve or document disagreements between lenses. Contradictions are features, not bugs.
Step 6: Read methodology/synthesis-rules.md — combine findings across lenses without flattening nuance.
Step 7: Produce all 4 output files inside projects/[project-name]/:
- executive-summary.md — 500 words max. What did we learn? What does it mean? What's unknown?
- deep-dive.md — Full analysis organized by lens, cross-references and contradictions highlighted.
- key-players.md — People, organizations, countries that matter most.
- open-questions.md — What we STILL don't know. Often more valuable than findings.
Step 8: Update knowledge/concepts.md and knowledge/data-points.md with everything learned.
Execution Mode (Critical)
DO: Live visible research for the user.
When the user says "do deep research," they want to SEE you working — live searches, visible reasoning, real-time synthesis. Show the moves, the choices, the findings. This is how trust is built. The user can course-correct mid-stream when they can see your thinking.
DON'T: Background delegation for Deep Research.
Background subagent delegation via delegate_task has proven unreliable on some model setups — subagents can get interrupted before completing. Only use background agents after getting explicit buy-in from the user.
Exception: For IMPLEMENTATION after research is done (building skills, writing files), background delegation is fine — that's mechanical work, not reasoning work.
Mid-Research Course Correction (Important Pattern):
Occasionally a single search result or source fundamentally changes the research thesis mid-flight. Example: researching "AI agent reputation protocols" → discovers ERC-8004 already deployed Jan 2026 with identical core concept. The thesis shifts from "should you build this?" to "pivot to analytics layer on top of ERC-8004." When this happens:
- Note the discovery explicitly ("Finding X changes the premise")
- Adjust the remaining lenses to test the new hypothesis, not the original
- Update the executive summary to reflect what changed and why
- Document the shift in the deep-dive under the lens that triggered it
This is a FEATURE of live research, not a failure. The structured lens system handles the course correction gracefully.
Payments in Crypto/Web3 Projects (Critical Rule):
When producing a spec for any crypto or web3 product, do NOT default to Stripe, credit cards, email auth, or any fiat infrastructure — even if it seems like the obvious solution. Crypto products require crypto-native payments. Default to:
- x402 (HTTP 402) for API payments: wallet signature, no account, no KYC, per-request billing
- No accounts required for read access; anonymity is a first principle, not a feature
- No Google Analytics — use Plausible Analytics or a self-hosted alternative
- No fiat on-ramps in the spec unless explicitly requested
If Stripe or any fiat payment appears in a draft spec and the project is blockchain/crypto/web3 adjacent, it will be rejected. Confirm the payment model BEFORE including it in a spec.
Critical Rules
- Each lens must RETHINK the question, not just add more information. Technical and contrarian should feel like two researchers who disagree.
- The tension between lenses IS the insight. Don't resolve it away.
- Never present a single-lens finding as a conclusion.
- Separate "what the data shows" from "what I interpret."
- [[open-questions]] is as important as [[executive-summary]].
Folder Structure (lived in ~/research-skill-graph/)
research-skill-graph/
├── index.md # Command center (start here)
├── research-log.md # All past projects with key findings
├── methodology/
│ ├── research-frameworks.md # How to pick the right approach
│ ├── source-evaluation.md # 5-tier trust system
│ ├── synthesis-rules.md # How to combine findings
│ └── contradiction-protocol.md # How to handle disagreements
├── lenses/ # The 9 research lenses
│ ├── technical.md
│ ├── economic.md
│ ├── historical.md
│ ├── business.md
│ ├── strategic.md
│ ├── customer.md
│ ├── product.md
│ ├── contrarian.md
│ └── first-principles.md
├── projects/ # One subfolder per research project
│ └── [project-name]/
│ ├── executive-summary.md
│ ├── deep-dive.md
│ ├── key-players.md
│ └── open-questions.md
├── sources/
│ └── source-template.md # Copy for each major source
└── knowledge/
├── concepts.md # Accumulates across ALL projects
└── data-points.md # Verified numbers, always with attribution
The Compound Effect
This system gets better over time:
knowledge/concepts.md and knowledge/data-points.md accumulate across ALL projects- After 5 projects, the AI starts with 200+ verified data points and 50+ defined concepts
research-log.md tracks every project — the 10th project starts from everything already learned- [[open-questions]] from one research become seeds for the next
When to Use Each Depth Level
Level 1 (30 min): 3 lenses max, top 5 sources. Directional understanding.
Level 2 (2-3 hrs): All 9 lenses, 15-25 sources. Informed opinion backed by evidence.
Level 3 (1-2 days): All 9 lenses with sub-questions, 50+ sources including primary data. Publishable analysis.
