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llm-tuning-patterns

parcadei/continuous-claude-v3

Evidence-based patterns for configuring LLM parameters, based on APOLLO and Godel-Prover research.

Installs157
Install command
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns
About this skill
Evidence-based patterns for configuring LLM parameters, based on APOLLO and Godel-Prover research. Different tasks require different LLM configurations. Use these evidence-based settings. Based on APOLLO parity analysis: Always request a proof plan before tactics: The proof plan (chain-of-thought) significantly improves tactic quality. For hard proofs, use parallel sampling: - Generate N=8-32 candidate proof attempts - Use best-of-N selection - Each sample at temperature 0.6-0.8 - Too low tokens for proofs : 512 tokens truncates chain-of-thought - Too low temperature for proofs : 0.2 misses creative tactic paths - No proof plan : Jumping to tactics without planning reduces success rate - This session: APOLLO parity - increased max_tokens 512->4096, temp 0.2->0.6 - This session: Added proof plan prompt for chain-of-thought before tactics

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FAQ
What does llm-tuning-patterns do?

Evidence-based patterns for configuring LLM parameters, based on APOLLO and Godel-Prover research.

Is llm-tuning-patterns good?

llm-tuning-patterns does not have approved reviews yet, so SkillJury cannot publish a community verdict.

What agent does llm-tuning-patterns work with?

llm-tuning-patterns currently lists compatibility with codex, gemini-cli, opencode, cursor, amp, github-copilot.

What are alternatives to llm-tuning-patterns?

Skills in the same category include telegram-bot-builder, flutter-app-size, sharp-edges, iterative-retrieval.

How do I install llm-tuning-patterns?

npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns

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