down-skilling
SolidDistill 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.
Install
Quality Score: 84/100
Skill Content
Details
- Author
- oaustegard
- Repository
- oaustegard/claude-skills
- Created
- 10 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
llm-cost-discipline
Sonnet default (Opus needs comment), prompt caching for >1024-token prompts, batch API for non-interactive, max_tokens declared, bounded retries, no full prompts in logs. Use on LLM API calls.
cost-lean-orchestration
Token-efficient model routing for complex engineering sessions. The main model (Opus / Fable, high reasoning) never bulk-reads or explores — it delegates all exploration, bulk file reading, codebase search, log scanning, dependency tracing, and documentation reading to Sonnet subagents that return compact structured findings, and it spends its own tokens only on synthesis, architecture, judgment, and final code. Use this skill in EVERY session that involves reading more than a couple of files, exploring or mapping a codebase, investigating a bug, auditing call sites, reviewing a large diff, digesting logs or docs, or any multi-step engineering task — even if the user never mentions cost, tokens, subagents, or delegation. This is a standing operating discipline, not an on-request feature.
token-efficient-skill-optimizer
Audit and optimize an existing AI skill, system prompt, agent instruction set, or workflow for token/cost efficiency WITHOUT degrading quality or safety — evidence-backed rules, honest measurement (measured/estimated/projected labels enforced by a validator), reviewable diffs, before/after benchmarks. Use when the user wants to cut a skill's or prompt's token cost, context footprint, or API spend; audit why an agent is expensive; validate someone's claimed token savings; or batch-audit a skills directory. Triggers: "optimize this skill/prompt", "cut token costs", "why is this agent so expensive", "audit my skill", "is this optimization real", "לייעל את הסקיל", "לחסוך טוקנים", "כמה עולה הסקיל הזה", "בדוק את החיסכון". Do NOT use for one-off prompt-wording help that won't be saved as a reusable artifact (answer directly), for authoring a brand-new skill from scratch (use skill-creator / token-efficient-skill-builder), or for optimizing a skill whose purpose is harmful — refuse those.