Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Install with Tessl CLI
npx tessl i github:forrestchang/andrej-karpathy-skills --skill karpathy-guidelines86
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillAgent success when using this skill
Validation for skill structure
Surgical code edits with orphan cleanup
Orphaned import removed
0%
0%
Non-orphaned import kept
100%
100%
Dead function preserved
100%
100%
Unchanged functions untouched
100%
100%
Style matched
100%
100%
Dead code mentioned
100%
100%
No added annotations
100%
100%
Without context: $0.1212 · 24s · 7 turns · 8 in / 1,267 out tokens
With context: $0.2514 · 51s · 14 turns · 14 in / 2,633 out tokens
Simplicity first, no over-engineering
No class abstraction
100%
100%
No extra CLI flags
100%
100%
No config file
100%
100%
No speculative error handling
100%
100%
No extra output files
100%
100%
No unrequested features
100%
100%
Concise implementation
100%
100%
No logging framework
100%
100%
No type annotations on simple code
100%
100%
Without context: $0.1929 · 35s · 14 turns · 11 in / 2,178 out tokens
With context: $0.2887 · 58s · 17 turns · 17 in / 2,545 out tokens
Goal-driven execution with assumption surfacing
Multiple interpretations listed
33%
100%
Chosen interpretation stated
91%
100%
Reasoning given for choice
83%
100%
Numbered implementation plan
33%
83%
Per-step verification
0%
100%
Simpler path noted
90%
100%
No gold-plating
83%
100%
Existing functions unchanged
100%
100%
Without context: $0.2625 · 1m · 14 turns · 14 in / 3,215 out tokens
With context: $0.3122 · 1m 12s · 15 turns · 16 in / 3,682 out tokens
Table of Contents
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