AI Token Research Workflow
Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.
/ quick answer
Run a fixed checklist where AI drafts and structures while explorer data supplies the facts, producing a red-flag list rather than a score. Screen a token in under 30 minutes: contract facts, liquidity structure, holder concentration and a written risk verdict.
- 01Collect identity: contract address on the correct chain, deployment date, deployer address, verified source code.
- 02Contract facts: mint authority, upgrade proxy, blacklist or fee-on-transfer functions, ownership renouncement.
- 03Liquidity: pool depth, whether liquidity is locked and for how long, price impact of a realistic order size.
- 04Holders: top-10 concentration excluding known contracts, number of holders funded from the same source, unlock schedule.
- 05AI synthesis: feed the collected facts to the model and ask for the strongest bear case and the exit-liquidity risk.
- 06Verdict: red flags, maximum position size that could be exited in one day, or a documented pass.
- 07If proceeding, execution happens on-chain through a DEX — the only step requiring a Web3 wallet.
/ frequently asked
What does the AI Token Research Workflow workflow do?
Run a fixed checklist where AI drafts and structures while explorer data supplies the facts, producing a red-flag list rather than a score.
What problem does AI Token Research Workflow solve?
Token pages show price and a logo. The information that determines whether a token can be exited — liquidity, concentration, mint authority — is one layer deeper.
How many steps does AI Token Research Workflow take?
7 steps. It starts with collect identity: contract address on the correct chain, deployment date, deployer address, verified source code. and ends with if proceeding, execution happens on-chain through a dex — the only step requiring a web3 wallet..
How do I set up AI Token Research Workflow step by step?
1) Collect identity: contract address on the correct chain, deployment date, deployer address, verified source code. 2) Contract facts: mint authority, upgrade proxy, blacklist or fee-on-transfer functions, ownership renouncement. 3) Liquidity: pool depth, whether liquidity is locked and for how long, price impact of a realistic order size. 4) Holders: top-10 concentration excluding known contracts, number of holders funded from the same source, unlock schedule. 5) AI synthesis: feed the collected facts to the model and ask for the strongest bear case and the exit-liquidity risk. 6) Verdict: red flags, maximum position size that could be exited in one day, or a documented pass. 7) If proceeding, execution happens on-chain through a DEX — the only step requiring a Web3 wallet.
Which tools does AI Token Research Workflow need?
It uses onchain-research-stack, ai-crypto-research-stack — each linked below with its own node.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Liquidity Pool
A liquidity pool is a smart contract holding two or more assets that traders swap against, with prices set by the pool's formula rather than an order book.
- →Smart Contract
A smart contract is code deployed to a blockchain that executes deterministically when called, holding balances and enforcing rules without an operator.
- →Slippage
Slippage is the difference between the quoted price and the executed price, caused by pool depth and by other transactions landing first.
- →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.
- →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.
- →On-chain Research Stack
Explorer, indexer and analytics layers combined so wallet and token questions get answered with verifiable data.
- →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.
- →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.
- →Smart Contract Risk Prompt
Reviews contract facts for the patterns that let a deployer or attacker take user funds.
- →Crypto Research Agent Prompt
System prompt for a research agent that must cite sources, separate fact from inference, and refuse to predict prices.
- →Crypto Market Analysis Prompt
Produces a structured market brief: regime, liquidity conditions, sector rotation, catalysts and what would change the view.
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.
- →AI Research vs Traditional Research
AI research compresses breadth and drafting; traditional research supplies verification and accountability for the claims you act on.
- →On-chain Data vs Exchange Data
On-chain data shows verifiable wallet-level behaviour; exchange data shows aggregate price discovery. Serious research needs both.