Volume Is Noise. Yield Is Signal.

The short version: Tokscale

Tokscale aggregates token usage across 40+ AI tools and publishes a community leaderboard ranked by total volume. It does the aggregation layer well: wide coverage, big board, active community. But it ranks by the wrong axis. Volume is a measure of activity, not skill. An operator who re-sends the same context every turn and burns 50M input tokens will outrank one who compounds cached context and burns 5M. Same tool, ten times the waste, higher rank.

SigRank takes the same four token pillars and asks a different question: is the cascade compounding or burning? The headline metric, Υ Yield = cache_read × output / input², rewards the operator who reuses cached context efficiently and penalizes the one who burns fresh input without leverage. Tokscale counts the fuel; SigRank measures the lap time. Both matter. Only one tells you who is winning.

Feature comparison

Why raw token counts aren't enough

Tokscale answers "who burned the most tokens?" That is a popularity contest, not a skill ranking. Two operators can spend the same 50K input tokens and get wildly different outcomes. One reuses cached context efficiently and produces 30K output tokens; the other re-sends the same context every turn and produces 3K. Same spend, ten-fold difference in signal. On a volume leaderboard, they tie. On a Yield leaderboard, the gap is obvious.

SigRank's headline metric, Υ = cache_read × output / input², measures exactly that gap. It rewards the operator who compounds cached context into output and penalizes the one who burns fresh input without leverage. Tokscale gives you the four integers and sums them; SigRank tells you whether the cascade they describe is compounding or burning.

From volume to Yield

If you already run Tokscale, you have the logs. SigRank reads the same telemetry and adds the scoring layer volume rankings never had:

npm install -g sigrank
sigrank enroll      # create your operator identity
sigrank submit      # reads logs, scores, signs, publishes

Prefer to inspect before you submit? Run sigrank me --dry-run to see your scored payload locally, or paste your token counts into the /score calculator to compute your Υ Yield, class tier, and compression ratio instantly, no account, no submission, just the numbers.

Frequently asked questions

Is SigRank a Tokscale alternative?

They overlap on data collection but diverge on the question they answer. Tokscale aggregates token usage across 40+ tools and ranks operators by total volume burned. SigRank takes the same token telemetry and ranks by Yield efficiency: Υ = cache_read × output / input². If you want the biggest board by headcount, Tokscale wins. If you want to know who is actually efficient, SigRank is the answer. You can run both, they read the same logs.

What does Tokscale not measure that SigRank does?

Tokscale reports total tokens burned and ranks by that single number. SigRank derives the cascade architecture from the same four pillars: Υ Yield (is signal compounding or burning?), compression ratio (output per input), SNR (signal density), Leverage (how much cached context amplifies your input), and Velocity (tokens per unit time). Tokscale tells you who burned the most; SigRank tells you who got the most signal per token spent. Volume rewards the operator who never stops typing; Yield rewards the one who compounds.

Can I use both Tokscale and SigRank?

Yes, and many operators do. Tokscale gives you the widest tool coverage and the largest community board. SigRank gives you the efficiency layer that volume rankings cannot. Run `sigrank submit` to publish your cascade score to the SigRank leaderboard, and keep your Tokscale profile for the volume crowd. The two are complementary, not mutually exclusive. The same local logs feed both.

Which is better for finding the most skilled AI operators?

SigRank. Volume leaderboards conflate activity with skill. An operator who burns 50M tokens re-sending the same context every turn will outrank one who burns 5M tokens but compounds cached context into high-yield output. Yield filters out that noise: it rewards the operator whose cascade is compounding, not the one whose burn rate is highest. If you want to find the best drivers, look at lap times, not fuel consumption.

Tokscale has 1,797 users and 40+ tools. Why is SigRank's board smaller?

Because SigRank measures a harder thing. Counting tokens is easy and scales to anyone with a log file. Computing a signed, cascade-scored, class-tiered snapshot requires the four pillars to be present and the operator to enroll an ed25519 identity. The SigRank board is smaller by design: every entry is a verified, scored, comparable signal, not a raw volume counter. Bigger is not better when bigger means noisier.