luiscrsilveira/llm-rigor
SUMMARY AI summary by gpt-5-mini
llm-rigor is a single RULES.md intended to be installed as the config file for coding assistants so they behave with more rigor and less ingratiation. It targets common LLM coding failures (wrong assumptions, silent errors, overengineering, sycophancy, and buried answers) and is based on Andrej Karpathy’s notes and related work. Who uses it: engineers and tool authors who run LLM coding agents (examples: Claude Code, Codex, GitHub Copilot, Gemini CLI, Cursor, Windsurf, Aider, etc.). Key features: seven concise principles — (1) pushback scales with user certainty, (2) lead with the answer, (3) say when you don’t know, (4) state assumptions before acting, (5) surgical changes only, (6) minimum viable code, (7) verifiable execution — plus concrete do/don’t examples for coding behavior and tone.
DETECTED Detected AI stack
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Language breakdown (by bytes)
Why this is classified AI-related
The AI relevance score checks four places for AI keywords and adds the weight of each one that matches. Full methodology
Total AI relevance score: 50 / 100
Position among AI repos in the same language
We track 4,088 JavaScript repos, of which 3,156 score 40 or above on AI relevance. 43 have more stars than this one (top 1.1%), and 1,327 score higher (top 32.5%).
Owner
Software engineer. Spec-Driven engineering, harness engineering, coding agents. AI agent development, RAG, MCP, and AI automation. AI-first companies advocate.
Dates
| Created on GitHub | 2026-05-09 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-08-17 16:19 |
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