LLM Prompt Optimizer

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/llm-prompt-optimizer

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

LLM Prompt Optimizer

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Prompt Audit Checklist, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when a prompt returns inconsistent, vague, or hallucinated results
• Use when you need structured/JSON output from an LLM reliably
• Use when designing system prompts for AI agents or chatbots
• Use when you want to reduce token usage without sacrificing quality
• Use when implementing chain-of-thought reasoning for complex tasks
• Use when prompts work on one model but fail on another

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Problem - Symptom - Fix
2. Too vague - Generic, unhelpful answers - Add role + context + constraints
3. No structure - Unformatted, hard-to-parse output - Specify output format explicitly
4. Hallucination - Confident wrong answers - Add "say I don't know if unsure"
5. Inconsistent - Different answers each run - Add few-shot examples
6. Too long - Verbose, padded responses - Add length constraints
7. R — Role: Who is the AI in this interaction?

Imported Workflow Notes

Imported: Step-by-Step Guide

1. Diagnose the Weak Prompt

Before optimizing, identify which problem pattern applies:

2. Apply the RSCIT Framework

Every optimized prompt should have:

• R — Role: Who is the AI in this interaction?
• S — Situation: What context does it need?
• C — Constraints: What are the rules and limits?
• I — Instructions: What exactly should it do?
• T — Template: What should the output look like?

Before (weak prompt):

Explain machine learning.

After (optimized prompt):

You are a senior ML engineer explaining concepts to a junior developer.

Context: The developer has 1 year of Python experience but no ML background.

Task: Explain supervised machine learning in simple terms.

Constraints:
- Use an analogy from everyday life
- Maximum 200 words
- No mathematical formulas
- End with one actionable next step

Format: Plain prose, no bullet points.

3. Chain-of-Thought (CoT) Pattern

For reasoning tasks, instruct the model to think step-by-step:

Solve this problem step by step, showing your work at each stage.
Only provide the final answer after completing all reasoning steps.

Problem: [your problem here]

Thinking process:
Step 1: [identify what's given]
Step 2: [identify what's needed]
Step 3: [apply logic or formula]
Step 4: [verify the answer]

Final Answer:

4. Few-Shot Examples Pattern

Provide 2-3 examples to establish the pattern:

Classify the sentiment of customer reviews as POSITIVE, NEGATIVE, or NEUTRAL.

Examples:
Review: "This product exceeded my expectations!" -> POSITIVE
Review: "It arrived broken and support was useless." -> NEGATIVE  
Review: "Product works as described, nothing special." -> NEUTRAL

Now classify:
Review: "[your review here]" ->

5. Structured JSON Output Pattern

Extract the following information from the text below and return it as valid JSON only.
Do not include any explanation or markdown — just the raw JSON object.

Schema:
{
  "name": string,
  "email": string | null,
  "company": string | null,
  "role": string | null
}

Text: [input text here]

6. Reduce Hallucination Pattern

Answer the following question based ONLY on the provided context.
If the answer is not contained in the context, respond with exactly: "I don't have enough information to answer this."
Do not make up or infer information not present in the context.

Context:
[your context here]

Question: [your question here]

7. Prompt Compression Techniques

Reduce token count without losing effectiveness:

# Verbose (expensive)
"Please carefully analyze the following code and provide a detailed explanation of 
what it does, how it works, and any potential issues you might find."

# Compressed (efficient, same quality)
"Analyze this code: explain what it does, how it works, and flag any issues."

Imported: Overview

This skill transforms weak, vague, or inconsistent prompts into precision-engineered instructions that reliably produce high-quality outputs from any LLM (Claude, Gemini, GPT-4, Llama, etc.). It applies systematic prompt engineering frameworks — from zero-shot to few-shot, chain-of-thought, and structured output patterns.

Imported: Prompt Audit Checklist

Before using a prompt in production:

• Does it have a clear role/persona?
• Is the output format explicitly defined?
• Are edge cases handled (empty input, ambiguous data)?
• Is the length appropriate (not too long/short)?
• Has it been tested on 5+ varied inputs?
• Is hallucination risk addressed for factual tasks?

Examples

Example 1: Ask for the upstream workflow directly

Use @llm-prompt-optimizer to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @llm-prompt-optimizer against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @llm-prompt-optimizer for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @llm-prompt-optimizer using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• ✅ Do: Always specify the output format (JSON, markdown, plain text, bullet list)
• ✅ Do: Use delimiters (```, ---) to separate instructions from content
• ✅ Do: Test prompts with edge cases (empty input, unusual data)
• ✅ Do: Version your system prompts in source control
• ✅ Do: Add "think step by step" for math, logic, or multi-step tasks
• ❌ Don't: Use negative-only instructions ("don't be verbose") — add positive alternatives
• ❌ Don't: Assume the model knows your codebase context — always include it

Imported Operating Notes

Imported: Best Practices

• ✅ Do: Always specify the output format (JSON, markdown, plain text, bullet list)
• ✅ Do: Use delimiters (```, ---) to separate instructions from content
• ✅ Do: Test prompts with edge cases (empty input, unusual data)
• ✅ Do: Version your system prompts in source control
• ✅ Do: Add "think step by step" for math, logic, or multi-step tasks
• ❌ Don't: Use negative-only instructions ("don't be verbose") — add positive alternatives
• ❌ Don't: Assume the model knows your codebase context — always include it
• ❌ Don't: Use the same prompt across different models without testing — they behave differently

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/llm-prompt-optimizer

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

Problem: Model ignores format instructions Solution: Move format instructions to the END of the prompt, after examples. Use strong language: "You MUST return only valid JSON."

Problem: Inconsistent results between runs Solution: Lower the temperature setting (0.0-0.3 for factual tasks). Add more few-shot examples.

Problem: Prompt works in playground but fails in production Solution: Check if system prompt is being sent correctly. Verify token limits aren't being exceeded (use a token counter).

Problem: Output is too long Solution: Add explicit word/sentence limits: "Respond in exactly 3 bullet points, each under 20 words."

Related Skills

• @linear-claude-skill

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @linkedin-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @linkedin-cli

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @linkedin-profile-optimizer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.