Token Research Prompt
Structures a full token due-diligence pass: mechanics, liquidity, concentration, bear case and unverifiable claims flagged explicitly.
/ quick answer
Use after collecting explorer data for a token. Works with any research-capable model; paste the on-chain facts you gathered so the model reasons over data instead of memory. Structures a full token due-diligence pass: mechanics, liquidity, concentration, bear case and unverifiable claims flagged explicitly.
You are a crypto research analyst. Analyse the token below for an investor deciding position size. Be specific and sceptical. Never invent numbers. TOKEN Name/ticker: [TOKEN] Chain + contract: [CHAIN + ADDRESS] Market cap / FDV: [VALUES] Liquidity (pools + depth): [DATA] Top-10 holder share (excluding known contracts): [DATA] Contract facts (mint authority, upgradeability, fees, lock): [DATA] Unlock schedule: [DATA] Produce: 1. What the token actually does and how value accrues to it (or state that it does not). 2. Token mechanics: supply, emissions, unlocks, and their dilution effect. 3. Liquidity assessment: estimated price impact of exiting a $10k, $50k and $250k position. 4. Concentration risk from holder data. 5. Contract risk from the facts given. 6. The strongest bear case in three sentences. 7. Red flags list, ordered by severity. 8. UNVERIFIED: every claim you could not support with the data provided. End with a maximum position size expressed as a % of a portfolio that a risk-aware investor could justify, plus the single condition that would invalidate the thesis. Do not predict price. Do not give financial advice.
Utility: fee-share on protocol revenue, currently $18k/month — negligible vs a $240M FDV. Liquidity: $1.4M across two pools; a $250k exit implies ~9% impact. Concentration: top-10 hold 41% ex-contracts, with 12% unlocking in 40 days. Red flags: upgradeable proxy with a non-timelocked admin key; emissions equal 2.3x current revenue. UNVERIFIED: team identity, audit scope. Max justified size: 0.5% with the unlock date as the invalidation trigger.
What does the Token Research Prompt prompt do?
Use after collecting explorer data for a token. Works with any research-capable model; paste the on-chain facts you gathered so the model reasons over data instead of memory.
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.
What output should I expect?
Utility: fee-share on protocol revenue, currently $18k/month — negligible vs a $240M FDV. Liquidity: $1.4M across two pools; a $250k exit implies ~9% impact. Concentration: top-10 hold 41% ex-contracts, with 12% unlocking in 40 days. Red flags: upgradeable proxy with a non-timelocked admin key; emis.
/ 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 Automation
Crypto automation is rule-based execution of monitoring, alerting and recurring on-chain actions, so decisions are made once and applied consistently.
- →Gas Fee
A gas fee is the network payment for computation and storage in a transaction, priced by demand rather than by trade size.
- →Smart Money
Smart money is a label for wallets with a documented history of profitable, early positioning — a research filter, not a signal to copy blindly.
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.
- →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.
- →Track A Whale Wallet
Monitor a single large address correctly: separate real position changes from custody moves before drawing any conclusion.
- →Analyze A Wallet With AI
Turn a raw transaction history into a readable profile: strategy, holding periods, risk behaviour and realised performance.
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.
- →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.
- →Crypto Research Agent Prompt
System prompt for a research agent that must cite sources, separate fact from inference, and refuse to predict prices.
- →Deep Research Prompt Template
Reusable Deep Research prompt that produces cited, structured reports every time.
- →Sourced Research Brief Prompt
Produces a structured brief where every claim carries a citation.
- →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.
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.