# multica-ai/andrej-karpathy-skills

Source: [GitHub](https://github.com/multica-ai/andrej-karpathy-skills)  
Feed7 permalink: https://feed7.dev/p/andrej-karpathy-skills-0jt1b7k  
Published: Unknown  
Trust: Needs Review (needs_review)

## Why Included

This CLAUDE.md turns common agent failure modes into four operating rules: surface uncertainty, minimize code, avoid unrelated edits, and define verifiable completion criteria.

## Source Summary

The repository packages **four principles** into one CLAUDE.md: expose assumptions and tradeoffs, write the minimum code, limit edits to the request, and drive work with verifiable goals. A Cursor rule carries the same guidance.

## Practical Implication

Merge the rules with project-specific instructions, then add concrete tests and acceptance criteria. The strongest operational pattern is **tests-first verification**, giving an agent a target it can check rather than an underspecified command.

## Agent-Ready Context

The repository packages **four principles** into one CLAUDE.md: expose assumptions and tradeoffs, write the minimum code, limit edits to the request, and drive work with verifiable goals. A Cursor rule carries the same guidance.

Merge the rules with project-specific instructions, then add concrete tests and acceptance criteria. The strongest operational pattern is **tests-first verification**, giving an agent a target it can check rather than an underspecified command.

These rules intentionally favor caution and may add friction to trivial fixes. They are behavioral instructions, not evidence that every model or task will improve, so judge them by diff scope, clarification timing, and test results.

## Connected Context

Feed7 judgment across 551 accumulated Signals:

This condenses conservative coding-agent behavior into a portable instruction layer: surface uncertainty, minimize scope and code, and define outcomes the agent can verify. It reinforces bounded execution as a reliability mechanism, but narrows the claim to a testable operating hypothesis; usefulness must be measured through clarification quality, diff size, and passing acceptance checks, and the rules may be excessive for trivial work.

- [Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution](https://feed7.dev/p/2607-13034v1-03g7ghx) — E3 provides controlled evidence for the same minimum-path principle: estimate scope, attempt the smallest solution, and expand only when verification fails.
- [Evolution of agentic surfaces — Gagan Bhat & Isabella Kai He, Anthropic](https://feed7.dev/p/evolution-of-agentic-surfaces-gagan-bhat-isabella-kai-he-anthropic-0mtshqh) — The model-specific harness warning makes these rules something to reevaluate across model upgrades rather than treat as permanently beneficial scaffolding.
- [How Forward Deployed Engineering is done at Factory — Eno Reyes](https://feed7.dev/p/how-forward-deployed-engineering-is-done-at-factory-eno-reyes-0zgscmd) — Factory’s validators and explicit completion criteria operationalize the repository’s verifiable-goal principle and show that measurable boundaries determine feasible autonomy.
- [How to build an AI-Native Health Company — Dan Feng, Maven Clinic](https://feed7.dev/p/how-to-build-an-ai-native-health-company-dan-feng-maven-clinic-0hr5j7m) — Maven’s small, reviewable changes and repeated tests reinforce minimum scope and verification as team practices, while confirming that human judgment remains necessary.

## Context Map

- Layer: agent
- Domains: coding
- Topics: harness-engineering, agent-reliability, context-engineering

## Uncertainty

- These rules intentionally favor caution and may add friction to trivial fixes. They are behavioral instructions, not evidence that every model or task will improve, so judge them by diff scope, clarification timing, and test results.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.