SniperIntel scores Pump.fun developer wallets so operators can judge attention, crowding, and relative setup quality before they trust the surface signal. This page is the public framework — what we measure, how to read it, and where the model's limits are.
This page is the public, answer-ready explanation of the SniperIntel scoring framework. It is not the full model, and it is not a feature tour. It exists so a skeptical operator can land here cold and leave with a clear idea of what the score means and what it does not.
The signal families we score, how they combine in principle, and how to interpret a resulting score responsibly.
Where the model can be wrong, where coverage thins, and the conditions under which a high score still fails.
Weights, thresholds, and the complete rule stack are not published — deliberately. Public methodology, private internals.
The score is a research aid. It never replaces your own live context check, entry rule, or risk discipline.
SniperIntel is not a universal chain explorer. It is narrow by design — a scoring surface for Pump.fun developer wallets. Everything below is measured continuously across tracked creators and compiled into a relative score.
The score is a joined read across those families — not a single metric, not a leaderboard of one axis.
A clean scoring system for Pump.fun creators is not a sorting problem — it is a context problem. Any single metric can look flattering until the surrounding signals move against it.
Creator wallets are noisy. Deployment volume, reused addresses, and repeated throwaway launches drown out the actual patterns that matter.
One isolated metric is not enough. Outcome history without crowding is misleading. Fees without trend context are a half read.
Generic dashboards show fragments, not a joined decision model. You get rows of numbers, not a framework that weighs them against each other.
A creator can look clean until crowding, fees, trend, or risk change the picture. Strong history doesn't survive a bad current setup.
These are the public components of the score. They are explainable, but their weights, thresholds, and interaction logic are not published. Treat this section as the vocabulary of the score, not its recipe.
How often a creator moves beyond initial launch noise into stronger post-launch outcomes. A creator that reliably clears early thresholds scores higher on this family than one who churns through launches without resolution.
How prior tokens behaved across tracked windows and quality thresholds. We read outcomes as distributions — consistency and depth matter more than any single spike.
How fee behavior compares across a creator's launches and whether deterioration is measurably weakening the setup. A wallet degrading on fees tends to degrade on outcomes.
Whether too many bots or snipers are already compressing the opportunity. A high-quality creator in a fully crowded setup is a different trade than the same creator early.
Bundle-style, suspicious, insider-style, or clearly low-quality patterns that can invalidate a setup before momentum matters. These markers can suppress an otherwise attractive score.
Whether the developer is strengthening, fading, or too stale to trust. A strong historical score attached to a cold wallet is not the same signal as a strong score trending upward.
The signal families above are public because you need them to interpret the score. Everything that turns those signals into a ranking is deliberately not published. The public framework is explainable enough to audit; the internals are not commoditized.
Every scoring system is a compression of reality. Here is where SniperIntel's compression can mislead you, and the conditions you should keep in your head whenever you read a score.
A high score means the creator looks stronger relative to measurable public factors. It does not mean the next token is automatically tradable. Here is the same wallet read two different ways — first by a naive scanner, then correctly against live context.
A strong score is not a green light on its own. Here, the wallet is genuinely strong — but crowding and fee drift mean the setup is no longer clean. The correct read is "watch and wait for a better entry," not "buy the next launch." Crowding, fees, and stale recency each have independent power to kill a high-score setup.
Methodology answers the framework question. The other public surfaces answer different ones — workflow, proof, and the actual product.
The operator field manual. How to use scored wallets, crowding, fees, and recency inside a live workflow — not just read about them.
Open Guide ProofThe public proof surface. How scored cohorts behave over time, measured out in the open rather than claimed.
Inspect Results ProductThe actual product surface. Scored developer feed, wallet drill-down, crowding context, and alerts — the place the framework above lives.
Launch DashboardThe methodology is the public half. The dashboard and the results cluster are where it resolves into a decision.
SniperIntel provides on-chain intelligence and developer wallet scoring for informational purposes only. This is not financial advice. Trading memecoins on Pump.fun involves extreme risk, including the total loss of invested capital. Past performance of scored wallets does not guarantee future results. Developer scores are based on historical on-chain data and proprietary analysis; they represent statistical patterns, not certainties. By using SniperIntel, you acknowledge that you are solely responsible for your own trading decisions.