multica-ai/andrej-karpathy-skills vs github/awesome-copilot
github/awesome-copilot scores higher overall: 71/100 (A-tier) against 61/100 (Experimental-tier). multica-ai/andrej-karpathy-skills leads on adoption; github/awesome-copilot leads on reliability, skill leverage, documentation. Scored 2026-07-07, methodology v1.0.
Dimension by dimension
| Dimension | karpathy-skills | awesome-copilot |
|---|---|---|
| Reliability | 60 | 64 |
| Skill Leverage | 70 | 72 |
| Documentation | 68 | 80 |
| Maintenance | 50 | 92 |
| Safety / Governance | 62 | 62 |
| Evaluation Readiness | 35 | 50 |
| Composability | 55 | 78 |
| Adoption (capped) | 90 | 82 |
| Overall | 61 · Experimental | 71 · A |
The entries
A single CLAUDE.md distilling Andrej Karpathy's public observations on LLM coding pitfalls into instruction-file rules. An instruction set,…
GitHub's first-party collection of Copilot customizations: instruction files, agent definitions, skills, and chat modes,…
Frequently asked questions
multica-ai/andrej-karpathy-skills or github/awesome-copilot?
github/awesome-copilot scores higher overall (71 vs 61, methodology v1.0). But they are the same category, so the dimension table below is the real answer.
How is this comparison generated?
Both scorecards come from the same public rubric with evidence notes, scored by the same editorial process. This page presents them side by side; it adds no new judgments beyond the scores themselves.