parcadei/continuous-claude-v3

llm-tuning-patterns

LLM Tuning Patterns

First seen Jan 22, 2026

Installation

$ npx skills add parcadei/continuous-claude-v3 --skill llm-tuning-patterns

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from parcadei/continuous-claude-v3 · top by installs.

npx skills add parcadei/continuous-claude-v3

Browse all from parcadei/continuous-claude-v3

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Also listed on

Alternate registries and mirrors of this skill.

Repository health

Stars 3.9K
License LICENSE
Default branch main
Open issues 30
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,923 B
  • docs SUMMARY.md 46 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 472 installs

SKILL.md

LLM Tuning Patterns

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

Pattern

Different tasks require different LLM configurations. Use these evidence-based settings.

Theorem Proving / Formal Reasoning

Based on APOLLO parity analysis:

Parameter Value Rationale
max_tokens 4096 Proofs need space for chain-of-thought
temperature 0.6 Higher creativity for tactic exploration
top_p 0.95 Allow diverse proof paths

Proof Plan Prompt

Always request a proof plan before tactics:

Given the theorem to prove:
[theorem statement]

First, write a high-level proof plan explaining your approach.
Then, suggest Lean 4 tactics to implement each step.

The proof plan (chain-of-thought) significantly improves tactic quality.

Parallel Sampling

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

Code Generation

Parameter Value Rationale
max_tokens 2048 Sufficient for most functions
temperature 0.2-0.4 Prefer deterministic output

Creative / Exploration Tasks

Parameter Value Rationale
max_tokens 4096 Space for exploration
temperature 0.8-1.0 Maximum creativity

Anti-Patterns

  • 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

Source Sessions

  • 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