github/awesome-copilot vs multica-ai/andrej-karpathy-skills
github/awesome-copilot scores higher overall: 71/100 (A-tier) against 61/100 (Experimental-tier). github/awesome-copilot leads on reliability, skill leverage, documentation; multica-ai/andrej-karpathy-skills leads on adoption. Scored 2026-07-07, methodology v1.0.
Dimension by dimension
| Dimension | awesome-copilot | karpathy-skills |
|---|---|---|
| Reliability | 64 | 60 |
| Skill Leverage | 72 | 70 |
| Documentation | 80 | 68 |
| Maintenance | 92 | 50 |
| Safety / Governance | 62 | 62 |
| Evaluation Readiness | 50 | 35 |
| Composability | 78 | 55 |
| Adoption (capped) | 82 | 90 |
| Overall | 71 · A | 61 · Experimental |
The entries
GitHub's first-party collection of Copilot customizations: instruction files, agent definitions, skills, and chat modes,…
A single CLAUDE.md distilling Andrej Karpathy's public observations on LLM coding pitfalls into instruction-file rules. An instruction set,…
Frequently asked questions
github/awesome-copilot or multica-ai/andrej-karpathy-skills?
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.