The labor market is moving from binary to float.
Float requires measurement. SkillBench is the measurement layer — the only calibrated benchmark for human-AI collaboration, validated across controlled problems and real-world work.
Developers own their data. We own the distribution that makes it meaningful.
Four Compounding Layers
Dual Validation Architecture
Controlled Problems
- Coding challenge platforms (e.g. Codewars) — 3-5M developers
- Known difficulty + verified test suites
- Assessment platforms (HackerRank, Qualified, etc.)
- SkillBench diagnostic instruments
Real-World Work
- Enterprise deployments with partner organizations
- Production telemetry at scale
- Complex, proprietary workflows
- Ecological validity across industries
Why This Can't Be Replicated
Structural Conflict
Model companies can't be neutral arbiters of "how well are you using AI?" when the answer might be "use a different model"
Dual Dataset
Controlled + real-world. The gap between them is diagnostic insight only we can see
Temporal Depth
Longitudinal behavioral patterns can't be fast-forwarded. Two years of data >> day-one snapshot
Causal Data
Paired A/B structure from coaching interventions. The only causally structured human-AI optimization dataset
Partnership Value
What SkillBench Enables for Partners
Training & Development
- Instrument learners during real work — not synthetic assessments
- Measure impact: leverage gains × capability growth
- Scale coaching via transferable agent skills
- Model-agnostic: works across Claude, Codex, Copilot, Gemini
Matching & Assessment
- Skill-in-context signals (Python for SWE ≠ Python for analysis)
- Evidence-based capability, not self-reported keywords
- Continuous validation — not point-in-time testing
- Authorship attribution: who did the work?
Marketplace & Credentials
- Portable proof-of-work records for developers
- Verified work record that travels with the person
- Calibrated benchmark makes individual signals meaningful
- Network effect: more users → better benchmarks → more value
Privacy Architecture
Developer trust is a design constraint, not a feature.
All telemetry stays local until the developer explicitly reviews and pushes it. No silent collection. No employer surveillance. No raw data leaves the device without consent. Group-level insights are available to organizations — never individual-level data without the developer's opt-in. Developers can export or delete their data at any time.