Evidence-based patterns for configuring LLM parameters, based on APOLLO and Godel-Prover research.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionllm-tuning-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches llm-tuning-patterns from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate llm-tuning-patterns. Access via /llm-tuning-patterns in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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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:
| 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 |
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.
For hard proofs, use parallel sampling:
| Parameter | Value | Rationale |
|---|---|---|
| max_tokens | 2048 | Sufficient for most functions |
| temperature | 0.2-0.4 | Prefer deterministic output |
| Parameter | Value | Rationale |
|---|---|---|
| max_tokens | 4096 | Space for exploration |
| temperature | 0.8-1.0 | Maximum creativity |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
parcadei/continuous-claude-v3
parcadei/continuous-claude-v3
am-will/codex-skills
giuseppe-trisciuoglio/developer-kit
giuseppe-trisciuoglio/developer-kit
davila7/claude-code-templates
llm-tuning-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: llm-tuning-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for llm-tuning-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: llm-tuning-patterns is focused, and the summary matches what you get after install.
llm-tuning-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
llm-tuning-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend llm-tuning-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
llm-tuning-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend llm-tuning-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in llm-tuning-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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