Explainable risk methodology v1.1
How scores are calculated
Risk score and confidence score are separate. Higher risk means more observed warning signs. Higher confidence means more complete data. Missing data reduces confidence — it never makes a token look safer.
Risk labels
When confidence is below 35, the label becomes INSUFFICIENT DATA regardless of the score. A low score with low confidence means key checks were unavailable, not that the token is safe.
Category weights (caps)
| Category | Weight |
|---|---|
| Contract permissions | 22% |
| Liquidity risk | 18% |
| Holder concentration | 14% |
| Ownership / admin control | 14% |
| Honeypot / tax / trading restrictions | 10% |
| Token age / lifecycle | 10% |
| Deployer history | 8% |
| External provider signals | 4% |
Each category's observed points are capped at its weight; the risk score is the sum of capped categories (0–100). Narrative/slop analysis is scored separately and shown on reports without affecting the risk score.
Why this score?
Example factors only — every real report carries its own breakdown.
Blacklist function confirmed - 8 points - HIGH
Example report preview: transfer restrictions would require further verification before relying on this token.
Category: CONTRACT_PERMISSIONS. Source: example-contract-analysis. Status: OBSERVED. Timestamp: 2026-06-30T09:00:00.000Z. Confidence impact: 0.
Liquidity dropped over 30% - 9 points - HIGH
Example report preview: liquidity moved materially compared with the last snapshot.
Category: LIQUIDITY_RISK. Source: example-liquidity-history. Status: OBSERVED. Timestamp: 2026-06-30T09:00:00.000Z. Confidence impact: 0.
Holder data unavailable - 0 points - INFO
Example report preview: holder checks were unavailable, lowering confidence.
Category: HOLDER_CONCENTRATION. Source: example-holder-provider. Status: UNAVAILABLE. Timestamp: 2026-06-30T09:00:00.000Z. Confidence impact: -12.
Narrative quality / saturation analysis
Narrative analysis estimates whether a token's theme appears fresh, saturated, derivative, or artificially hyped. It does not predict profit. It is scored separately from technical scam risk: by default it never changes the risk score, and an optional "include narrative in total risk" setting only adds a small, clearly labelled adjustment to a separate combined score.
What is scored (0–100 each)
- Novelty — how new/uncommon the theme looks. New is information, not an endorsement: a fresh narrative can still be a rug.
- Saturation risk — how heavily recycled the theme is (dog/frog memes, baby forks, moon naming…).
- Momentum — market activity (volume vs liquidity); social momentum is added once social providers are configured.
- Originality — how derivative the name/theme is, including near-duplicates of well-known tokens.
- Manipulation risk — patterns consistent with artificial hype (e.g. heavy volume on thin liquidity or brand-new pairs).
- ScanZX Slop Score — a combined copycat/saturation read; higher = more slop-like.
- Narrative confidence — completeness of narrative data only. Missing social providers lower this score, never the technical confidence.
Categories and honesty rules
- Categories: original/emerging, fresh derivative, overused meme, copycat/slop, artificial hype signals, insufficient narrative data.
- An overused meme is not automatically a scam, and an original narrative is not automatically quality.
- Similarity currently uses string matching against known tokens and the ScanZX scan history; unavailable checks (social providers, external saturation) are always listed as not checked.
- The sniper profile highlights fresher narratives but never presents freshness as safety.
Data sources used
Each factor includes source and timestamp fields. Provider conflicts remain visible instead of being averaged away.
Unavailable checks
Unavailable checks reduce confidence and are listed on every report.
Versioning
Every scan stores methodologyVersion: v1.1, so old reports stay interpretable when weights change.