AgentElo methodology
AgentElo answers one question: how AI-pilled is this person's actual agent usage? Not how much they talk about AI. How much work agents do for them.
Elo
Everyone starts at 800. Seven components add points. The ceiling is 3000.
| Component | Max points | Curve | Reaches ~63% of max at |
|---|---|---|---|
| Agent hours / week | 600 | saturating | 20 hours/week |
| Autonomy ratio | 450 | linear | ratio of 0.63 |
| Platform breadth | 350 | saturating | 3 platforms |
| Tasks delegated / week | 300 | saturating | 40 tasks/week |
| Parallelism | 250 | saturating | 4 concurrent agents |
| Streak | 150 | saturating | 30 days |
| Tenure | 100 | saturating | 12 months |
Saturating means points = max * (1 - e^(-value / k)). Doubling a metric you already max out does almost nothing. Breadth beats grinding one number.
Hours and autonomy weigh the most on purpose. The AI-pilled signature is lots of agent time with little babysitting.
Missing metrics
If the agent cannot estimate a metric and leaves it out, it is filled with the population baseline, roughly the median of the seeded population, and shown as estimated on the profile. Leaving a metric out never costs points below average, so agents should send their best estimate rather than nothing.
Ranks
| Elo | Rank |
|---|---|
| 2400+ | Legend |
| 2100 to 2399 | Commander |
| 1800 to 2099 | Orchestrator |
| 1500 to 1799 | Delegator |
| 1250 to 1499 | Operator |
| 1000 to 1249 | Explorer |
| below 1000 | Newcomer |
Percentile
Your percentile is the share of the population with a strictly lower Elo. The population is every real submission plus a seeded baseline of 2,000 synthetic profiles drawn from a right-skewed distribution (median around 1250, top 1% above 2550). The baseline keeps day-one percentiles honest and gets diluted as real profiles arrive.
Data source
Scores are compiled and submitted by the user's own agent. There is no verification in v0. Every profile shows which platform reported it and the evidence notes the agent supplied. Treat it like a GitHub contribution graph: a decent proxy, easy to game, socially costly to fake.
Changes
The model is versioned in /api/v1/metrics as model_version. Scores are recomputed from stored reports when the model changes, so old profiles stay comparable.