How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does llm-tuning-patterns support?

llm-tuning-patterns works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is llm-tuning-patterns free to use?

Yes. llm-tuning-patterns is free to install and use. It is available from the open explainx.ai skill registry published by parcadei.

Where can I read ratings and reviews for llm-tuning-patterns?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install llm-tuning-patterns?

Run `npx skills add https://github.com/parcadei/continuous-claude-v3 --skill llm-tuning-patterns` in your terminal. You need to have run `npx skills init` once in your project first.

AI/ML

llm-tuning-patterns▌

parcadei/continuous-claude-v3 · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

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

0 commentsdiscussion

summary

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

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

how to use llm-tuning-patterns

How to use llm-tuning-patterns on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add llm-tuning-patterns

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches llm-tuning-patterns from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ── always included ────

│ • Amp

│ • Antigravity

│ • Cline

│ • Codex

│ ●Cursor(selected)

│ • Cursor

│ • Windsurf

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/llm-tuning-patterns

Reload or restart Cursor to activate llm-tuning-patterns. Access the skill through slash commands (e.g., /llm-tuning-patterns) or your agent's skill management interface.

⚠

Security & Verification Notice

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.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub ›Skills CLI documentation ›Learn more about Cursor ›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

1.Install skill using provided installation command
2.Test with simple use case relevant to your work
3.Evaluate output quality and relevance
4.Iterate on prompts to improve results
5.Integrate into regular workflow if valuable

Common Pitfalls

⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns

✗ Don't

−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment

💡 Pro Tips

★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise

When to Use This▌

✓ 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.

Learning Path▌

1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)

No comments yet — start the thread.

general reviews

Ratings

4.6★★★★★26 reviews

★★★★★Sakshi Patil· Nov 11, 2024
llm-tuning-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
★★★★★Chaitanya Patil· Oct 2, 2024
Keeps context tight: llm-tuning-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
★★★★★Piyush G· Sep 25, 2024
Registry listing for llm-tuning-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
★★★★★Isabella Sanchez· Sep 21, 2024
Solid pick for teams standardizing on skills: llm-tuning-patterns is focused, and the summary matches what you get after install.
★★★★★Shikha Mishra· Aug 16, 2024
llm-tuning-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
★★★★★Xiao Rao· Aug 12, 2024
llm-tuning-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
★★★★★Yash Thakker· Jul 7, 2024
I recommend llm-tuning-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
★★★★★Isabella Sharma· Jul 3, 2024
llm-tuning-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
★★★★★Arjun Yang· Jul 3, 2024
I recommend llm-tuning-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
★★★★★Dhruvi Jain· Jun 26, 2024
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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