Tweet in, four answers out
Six steps. Everything after the first is deterministic unless you ask for the LLM audit.
1 · Read the post
The post, its author, its timestamp and its replies. Classify intent first — roughly 31% of a KOL's token posts turn out not to be calls at all, and scoring those would poison every average.
2 · Resolve the token
Match the ticker or contract to a live market. Ambiguous tickers stop and ask rather than guessing; a wrong-token score attached to a named person is a dispute waiting to happen.
3 · Pull the chart
Candles either side of the post at four resolutions, plus liquidity and the pair's own pre-post baseline.
4 · Build the benchmark
Sample peer tokens on the same chain over the same window. Returns are scored in excess of that median, so a market-wide melt-up never reads as skill.
5 · Score the replies
Classify each reply by content first and account statistics second. Validated at 91% against hand-labelled posts.
6 · Place it
Compare against the corpus percentiles, assign the profile quadrant, and store the result stamped with the scoring version so an old report stays reproducible.
Four scores, never blended
They answer different questions for different people, and one of them has no measurable relationship with the others. Averaging would imply a link the data denies.
Was it a good call to follow?
Highest excess return over the peer benchmark within the window.
How far it fell from that peak. A round trip is not a good call.
Excess return still standing at the end of the window.
How early the entry was relative to the move.
Whether the gain persisted rather than spiking and vanishing.
Did it move the market?
Traded volume above the pair's own pre-post baseline.
Distinct trades, so one whale cannot carry the score.
Buy pressure against sell pressure in the attribution window.
How much of the move arrived in the first minutes.
Is the engagement real?
Reply content: substance, relevance, whether anyone engaged with the claim.
Giveaway entries, wallet drops, contentless praise.
Whether the same accounts reply to every post this KOL makes.
Account-level signals. Weighted low on purpose — see below.
Loud, or actually effective?
Not a score — a label. Engagement per thousand followers and volume per thousand followers, each compared against the corpus p75. The money axis uses raw volume, not the composite impact score, so a big account cannot buy its way into the good quadrant with reach alone.
What the percentiles are computed from
Scores are relative. A number out of 40 means nothing until you know what the rest of the distribution looks like — so here it is.
| Measure | p25 | p50 | p75 | p90 |
|---|---|---|---|---|
| Impact score | 5.1 | 8.6 | 12.8 | 18.9 |
| Engagement / 1k | 0.4 | 0.8 | 1.4 | 2.3 |
| Volume / 1k | $60 | $358 | $1,200 | $3,000 |
| Volume lift | 0.50x | 0.93x | 1.58x | 2.58x |
These are measured, and they are lower than intuition suggests. Before calibrating, the engagement threshold was guessed at 12 per thousand followers; the real p75 is 1.4. That single wrong number made two of the four profile quadrants structurally unreachable — which is why nothing here is a fixed constant, and why the thresholds are re-derived as the distribution moves.
The things that came out badly
These are measurements too. Publishing only the flattering ones would make every other number on this site worth less.