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.
/ quick answer
Run a fixed pipeline where an AI assistant handles breadth and drafting while you verify every number against market and on-chain sources, ending in a written thesis with an invalidation level. A repeatable research loop: turn a question into market data, on-chain evidence and a written risk view before any position is considered.
- 01Write the question as a decision, not a topic: 'should this token be a 1% position for 3 months?'
- 02AI pass 1 — breadth: have the model summarise the project, token model, competitors and the strongest bear case, with source links for each claim.
- 03Market data check: price history, liquidity depth, volume distribution across venues, derivatives open interest if it exists.
- 04On-chain check: holder distribution, top-wallet concentration, contract age, mint/upgrade authority, liquidity lock, recent large transfers.
- 05Cross-check: reject any AI claim you cannot verify in an explorer or analytics platform — treat unverifiable specifics as false.
- 06Risk write-up: position size, invalidation level, time horizon, what would make you exit early.
- 07Log the thesis with a date so you can review the decision quality later, independent of the outcome.
- Weekly recurring version: same pipeline run over a fixed watchlist and delivered as a Monday brief.
- Read-only version for people who never trade — useful purely as market literacy.
/ frequently asked
Does this workflow tell me what to buy?
No. It produces a structured view of a token's mechanics, liquidity, holder concentration and risks. The sizing and the decision remain yours, and no research process removes the possibility of total loss.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →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.
- →Crypto Risk Management
Risk management in crypto is position sizing plus custody hygiene: deciding what you can lose per trade and what a single compromise can reach.
- →Token Approval
A token approval grants a contract permission to move your tokens — persistent, often unlimited, and the most common source of avoidable losses.
- →Crypto Automation
Crypto automation is rule-based execution of monitoring, alerting and recurring on-chain actions, so decisions are made once and applied consistently.
Related workflows
Turn this into a repeatable process.
- →AI Token Research Workflow
Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.
- →Turn Deep Research Into a Weekly Executive Brief
Use an AI Deep Research agent every Monday to produce a cited market brief in 20 minutes.
- →Build a Research Automation Pipeline
Question in, sourced structured brief out — on a schedule.
- →Track A Whale Wallet
Monitor a single large address correctly: separate real position changes from custody moves before drawing any conclusion.
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.
- →AI Trading Stack
Adds an AI analysis and risk-review layer on top of a trading stack, keeping approval and execution human.
- →Crypto Automation Stack
Automation platform, data APIs, alerting and optional wallet execution — the operational layer for monitoring and recurring actions.
Related prompts
Reusable prompts for this job.
- →Token Research Prompt
Structures a full token due-diligence pass: mechanics, liquidity, concentration, bear case and unverifiable claims flagged explicitly.
- →Crypto Risk Analysis Prompt
Runs a pre-mortem on a position or protocol: enumerates failure modes, likelihood, impact and observable early warnings.
- →Crypto Market Analysis Prompt
Produces a structured market brief: regime, liquidity conditions, sector rotation, catalysts and what would change the view.
- →Crypto Research Agent Prompt
System prompt for a research agent that must cite sources, separate fact from inference, and refuse to predict prices.
Related use cases
How people apply it, and what came out.
- →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.
- →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.
- →Tech Creator Replaces a Research Assistant With a Workflow
A YouTuber cut their research time per video from 8 hours to 90 minutes.
Comparisons & alternatives
Pick between the options.
- →On-chain Data vs Exchange Data
On-chain data shows verifiable wallet-level behaviour; exchange data shows aggregate price discovery. Serious research needs both.
- →AI Research vs Traditional Research
AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.