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Llm Tuning Patterns

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parcadei·vlatest·ai-ml·MIT

Helps with LLMs, patterns as part of building AI and machine learning applications workflows.

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Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

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

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

parcadei

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Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

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    Makes no network calls.

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    Scanned parcadei/continuous-claude-v3 at d07ff4b06b.

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Commit
d07ff4b06b
Manifest
.claude/skills/llm-tuning-patterns/SKILL.md
Scanned
1 file, 68 lines
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About this skill

llm-tuning-patterns

What it does

This skill provides a library of prompt engineering techniques designed to optimize Large Language Model outputs for specific tasks. It enables agents to dynamically adjust their reasoning, formatting, and constraint adherence based on the provided pattern context.

When to use it

  • Refining complex logic chains where standard prompting yields inconsistent results.
  • Enforcing strict output formats or schemas in data processing workflows.
  • Improving role-playing scenarios by injecting specific behavioral constraints.
  • Debugging model hallucinations by applying targeted verification patterns.

Key capabilities

  • Dynamic pattern injection for task-specific optimization.
  • Enhanced reasoning structures for multi-step problems.
  • Constraint enforcement mechanisms for output formatting.
  • Context-aware adaptation of tone and style.

Example prompts

  • "Apply the chain-of-thought pattern to solve this math problem step-by-step, ensuring no steps are skipped."
  • "Use the json-schema-validation pattern to extract entities from this text and return only valid JSON."
  • "Activate the role-play-architect pattern to generate a project plan as if you were a senior CTO."

Tips & gotchas

Ensure the target model supports the specific syntax or tokens required by the chosen pattern, as not all LLMs interpret structural markers identically. Start with simple patterns before combining multiple advanced techniques to avoid prompt injection risks or token limit overflows.