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Distill Opus-level reasoning into optimized instructions for Haiku 4.5 (and Sonnet). Generates explicit, procedural prompts with n-shot examples that maximize smaller model performance on a given task. Use when user says "down-skill", "distill for Haiku", "optimize for Haiku", "make this work on Haiku", "generate Haiku instructions", or needs to delegate a task to a smaller model with high reliability.

Use this Skill: https://skilld.dev/gh/oaustegard/claude-skills/down-skilling

This session only. Nothing lands on disk.

examplescreative-rewriting.md

≈597 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Creative Rewriting / Tone Adaptation

Task type: Generative with style constraints Key gaps addressed: creative-generation, implicit-constraints, output-calibration

Original (Opus-level)

"Rewrite this technical paragraph for a general audience blog post. Make it engaging but accurate."

Distilled Haiku Prompt

<role>
You are a technical writer who adapts complex content for general audiences.
</role>

<task>
Rewrite the technical paragraph for a blog post. Maintain factual accuracy
while making it accessible and engaging to non-experts.
</task>

<rules>
1. Reading level: high school graduate (no jargon without explanation)
2. Length: 80-120 words (original may be shorter or longer)
3. Begin with a hook — a question, surprising fact, or relatable scenario
4. Replace technical terms with plain equivalents. If a term must stay,
   add a brief parenthetical explanation.
5. Use active voice. Vary sentence length (8-25 words per sentence).
6. End with a forward-looking statement or implication.
7. Do not add information not present in the original.
8. Do not use: "In today's world", "It's important to note", "crucial",
   "revolutionary", "game-changing".
</rules>

<examples>
<example>
<input>
The system employs a microservices architecture with event-driven
communication via Apache Kafka, enabling horizontal scalability and
fault isolation across service boundaries.
</input>
<o>
What happens when one part of a massive app breaks? In a well-designed
system, the answer is: almost nothing. Modern apps split into small,
independent pieces that talk to each other through a message queue (think
of it like a post office for data). If one piece fails, the others keep
running. Need to handle more traffic? Just add more copies of the busy
pieces. The rest of the system doesn't even notice.
</o>
<reasoning>
Replaced "microservices" with "small independent pieces", "event-driven
via Kafka" with "message queue" + analogy, "horizontal scalability" with
"add more copies", "fault isolation" with "others keep running".
</reasoning>
</example>
</examples>

<context>
{{technical_paragraph}}
</context>

Why it works for Haiku

  • Style exemplar shows the target voice (concrete > descriptive)
  • Banned phrases list prevents generic AI-sounding output
  • Word count range prevents over/under-shooting
  • Rule 7 prevents hallucinated additions

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub12d

    The skill provides guidelines and templates for optimizing AI prompts for smaller models by distilling complex reasoning into explicit procedures and many-shot examples. No security issues were detected.

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    No alerts

  • Snyk12d

    Risk: LOW · No issues

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    2/37 files flagged

Signed by skilld at 5e58100. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 3 weeks ago
metadata
{
  "author": "Oskar Austegard and Opus",
  "version": "1.3.1"
}

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