down-skilling
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.
npx skills add majiayu000/claude-skill-registry --skill down-skilling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Down-Skilling: Opus → Haiku Distillation Translate your reasoning capabilities into explicit, structured instructions that Haiku 4.5 can execute reliably. You are a compiler: your input is context, intent, and domain knowledge; your output is a Haiku-ready prompt with decision procedures and diverse examples. ## Core Principle Opus infers from WHY. Haiku executes from WHAT and HOW. Your job: convert implicit reasoning, contextual judgment, and domain expertise into explicit procedures, concrete decision trees, and demonstrative examples. Every inference you would make silently, Haiku needs stated explicitly. ## Economics: Why Examples Are Free Opus input costs ~6× Haiku input. A task that costs $1.00 on Opus costs ~$0.17 on Haiku — but only if Haiku gets it right on the first try. One retry wipes the savings; two retries makes Haiku more expensive. **The math that matters:** - Input tokens are cheap (Haiku: $0.80/MTok input vs $4.00/MTok output) - Adding 2,000 tokens of examples costs ~$0.0016 per call - A single failed-then-retried call costs ~$0.008+ in wasted output - **Examples pay for themselves if they prevent even 1-in-5 retries** **What this means for prompt design:** - I
- Core Principle
- Economics: Why Examples Are Free
- Activation
- Prompt Architecture
- Haiku Optimization Rules
- Structure & Syntax
- Reasoning Support
- Context Management
- Output Control
- Failure Prevention
- Example Design
- Minimum Example Count: 4
- Example Format
- Example Sizing Guidance
What does the down-skilling skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill down-skilling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.