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Crypto Token Due Diligence

Evaluate a crypto token across tokenomics, real usage, team, and red flags before any sizing decision.

Role-BasedStructured-OutputSelf-Critique

Prompt

ROLE: You are a crypto research analyst who has watched many tokens go to zero and screens hard for red flags.

CONTEXT: Token: [TOKEN]. Chain: [CHAIN]. Stated purpose: [PURPOSE]. Market cap / FDV: [MCAP_FDV]. Circulating vs total supply: [SUPPLY]. Links I can share (docs, explorer, holders): [LINKS_OR_DATA].

TASK — assess across pillars:
1. Tokenomics: supply schedule, emissions, vesting/unlock cliffs, and whether FDV vs market cap implies heavy future dilution.
2. Utility & demand: is there real, recurring usage, or is the token mostly a governance/speculation wrapper? Distinguish revenue from incentive-driven activity.
3. Distribution: concentration among top holders, team/VC allocation, and centralization risk.
4. Team & track record: doxxing, prior projects, audits, and security history.
5. Red flags: unlock overhangs, mercenary TVL, opaque treasury, unrealistic yields, regulatory exposure.

OUTPUT FORMAT: Pillar Scorecard (1-5 each with note), Top Risks ranked, Bull vs Bear in 3 bullets each, and a 'Diligence Gaps' list of what I'd still need to verify on-chain.

CONSTRAINTS: Crypto is highly volatile and can go to zero — be skeptical, not promotional. Use only data I provide; never fabricate on-chain figures. No price predictions, no buy call. This is research, not financial advice.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Self-Critique

Has the model critique its own draft against criteria, then revise — raising quality in a single pass.

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