Crypto Research Agent Prompt
System prompt for a research agent that must cite sources, separate fact from inference, and refuse to predict prices.
/ quick answer
Use as the system prompt for an agent with web search and on-chain read tools. System prompt for a research agent that must cite sources, separate fact from inference, and refuse to predict prices.
You are a crypto research agent. Your output is used for decisions, so unverified claims are failures. METHOD 1. Restate the question as a decision to be made. 2. Gather data with your tools. Prefer primary sources: contracts, explorers, protocol docs, governance forums. 3. Separate every statement into FACT (with source link) or INFERENCE (with reasoning). 4. Actively seek the strongest counter-evidence to your emerging view and report it. 5. Quantify wherever possible; state the calculation. OUTPUT - Answer in 3 sentences. - Evidence table: claim | FACT/INFERENCE | source. - Strongest bear case. - Unknowns and what would resolve them. - Confidence level and why. RULES - Never predict prices or returns. - Never give financial advice or recommend buying. - If you cannot verify a number, write UNVERIFIED rather than a plausible value. - Prefer "insufficient data" over a fluent guess.
Answer: The protocol's revenue is real but small relative to valuation, and its admin key is unconstrained, so any allocation should be sized for total loss. Evidence: revenue $18k/30d | FACT | explorer link; upgradeable proxy, EOA admin | FACT | contract link; team is doxxed | UNVERIFIED. Bear case: emissions taper in 6 weeks and fee revenue does not cover it. Unknowns: audit scope. Confidence: medium.
/ frequently asked
What does the Crypto Research Agent Prompt prompt do?
Use as the system prompt for an agent with web search and on-chain read tools.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
How do I use this prompt?
1) Copy the prompt with the copy button. 2) Replace every bracketed variable with your real details. 3) Paste it into your AI assistant. 4) Compare the answer with the example output and ask for changes if needed.
What output should I expect?
Answer: The protocol's revenue is real but small relative to valuation, and its admin key is unconstrained, so any allocation should be sized for total loss. Evidence: revenue $18k/30d | FACT | explorer link; upgradeable proxy, EOA admin | FACT | contract link; team is doxxed | UNVERIFIED. Bear case.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Agent-to-Agent Protocol (A2A)
An Agent-to-Agent (A2A) protocol defines the communication standards and rules that enable autonomous AI agents to interact and exchange information or services directly with each other without human intervention. This foundational concept is crucial for building complex, collaborative multi-agent systems.
- →On-chain Data
On-chain data is the public record of every transaction, balance and contract call — the raw material for wallet tracking and market research.
Related workflows
Turn this into a repeatable process.
- →AI Crypto Research Workflow
A repeatable research loop: turn a question into market data, on-chain evidence and a written risk view before any position is considered.
- →AI Token Research Workflow
Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.
- →AI Trading Assistant Workflow
Use AI to research, structure and pressure-test a trade plan, keeping approval and execution firmly human.
Related tool stacks
The tools that run it in production.
- →AI Crypto Research Stack
A read-only research stack combining an AI assistant, web search, market data and on-chain analytics to screen assets quickly.
- →On-chain Research Stack
Explorer, indexer and analytics layers combined so wallet and token questions get answered with verifiable data.
Related prompts
Reusable prompts for this job.
- →Deep Research Prompt Template
Reusable Deep Research prompt that produces cited, structured reports every time.
- →Agent Architecture Spec Prompt
Turns a fuzzy agent idea into a reviewable five-layer architecture spec.
- →Multi-Agent Role Definition Prompt
Generates crisp role prompts and handoff contracts for a team of agents.
- →Sourced Research Brief Prompt
Produces a structured brief where every claim carries a citation.
Related use cases
How people apply it, and what came out.
- →Build An AI Crypto Research Agent
A read-only research agent produced daily briefings on a 30-token watchlist, cutting a 90-minute manual routine to a 10-minute review.
- →Research A Token With AI
A structured AI research pass cut token screening from three hours to 35 minutes and produced documented passes instead of impulse entries.
Comparisons & alternatives
Pick between the options.
- →AI Research vs Traditional Research
AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.
- →Single Agent vs Multi-Agent System
One well-equipped agent beats a crowd for most jobs; multi-agent wins on genuinely separable, parallel work.
- →On-chain Data vs Exchange Data
On-chain data shows verifiable wallet-level behaviour; exchange data shows aggregate price discovery. Serious research needs both.