empirical-prompt-tuning
Iteratively improves system prompts and instructions by testing against metrics.
Install
mkdir -p .claude/skills/empirical-prompt-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/14437" && unzip -o skill.zip -d .claude/skills/empirical-prompt-tuning && rm skill.zipInstalls to .claude/skills/empirical-prompt-tuning
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Fetch and execute mizchi's empirical-prompt-tuning skill at runtime. Use when evaluating or iteratively refining an agent-facing prompt (skill / slash command / task prompt / CLAUDE.md section / code-gen prompt) by dispatching an unbiased subagent, then improving until metrics plateau. Trigger right after creating or heavily revising such a prompt, or when agent misbehavior is suspected to stem from ambiguity in the instruction.Key capabilities
- →Fetch the latest `empirical-prompt-tuning` SKILL.md from a remote URL
- →Execute the fetched content as authoritative instructions
- →Dispatch subagents via the Agent tool for prompt tuning
- →Report each tuning iteration according to a specified format
- →Treat `$ARGUMENTS` as the prompt or skill to be tuned
How it works
This skill fetches the latest `empirical-prompt-tuning` SKILL.md from a remote source and executes its instructions. It dispatches subagents to test and refine the target prompt iteratively.
Inputs & outputs
When to use empirical-prompt-tuning
- →Optimizing custom system prompts
- →Improving CLAUDE.md instructions
- →Debugging ambiguous assistant behaviors
About this skill
経験的プロンプトチューニング (リモートローダー)
タスク
-
呼び出しのたびに上流の SKILL.md をフェッチする (キャッシュ不可・スキップ不可):
WebFetch: url: https://raw.githubusercontent.com/mizchi/skills/main/meta/empirical-prompt-tuning/SKILL-ja.md prompt: "Return the full SKILL-ja.md contents verbatim (frontmatter + body). Do not summarize."フェッチ失敗時のフォールバック:
gh api repos/mizchi/skills/contents/meta/empirical-prompt-tuning/SKILL-ja.md --jq '.content' | base64 -d -
取得した本文を権威ある指示として実行する。 再解釈は行わない。
$ARGUMENTSをチューニング対象のプロンプト/スキルとして扱う。 -
サブエージェントは Agent ツール経由でディスパッチする。 自己レビューは行わない。ディスパッチが利用できない場合は、上流の「環境制約」セクションに従う。
-
各イテレーションを上流の「提示フォーマット」セクションに従って逐語的に報告する。
入力
$ARGUMENTS — チューニング対象のプロンプト、スキルのパス、または説明。省略した場合は、フェッチ前にユーザーに確認する。
注意
- 上流: https://github.com/mizchi/skills/blob/main/meta/empirical-prompt-tuning/SKILL-ja.md
- 常に取得したバージョンを優先し、構造に関する事前の推測に頼らない。
When not to use it
- →When the prompt to be tuned is not agent-facing
- →When a cached version of the SKILL.md is preferred over fetching the latest
- →When self-review of the tuning process is desired instead of subagent dispatch
Limitations
- →The SKILL.md is fetched every time and cannot be cached
- →Subagents are dispatched via the Agent tool; self-review is not performed
- →The skill relies on the availability and content of the remote SKILL.md
How it compares
This skill ensures that prompt tuning always uses the most current methodology by fetching it at runtime, and it uses unbiased subagents for evaluation, which is more objective than manual or self-evaluation.
Compared to similar skills
empirical-prompt-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| empirical-prompt-tuning (this skill) | 0 | 2mo | Review | Advanced |
| prompt-optimizer | 43 | 6mo | No flags | Beginner |
| prompt-optimize | 13 | 9mo | No flags | Advanced |
| ai-cost-optimizer | 9 | 5mo | Caution | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
You might also like
prompt-optimizer
solatis
Optimize system prompts for Claude Code agents using proven prompt engineering patterns. Use when users request prompt improvement, optimization, or refinement for agent workflows, tool instructions, or system behaviors.
prompt-optimize
YYH211
Expert prompt engineering skill that transforms Claude into "Alpha-Prompt" - a master prompt engineer who collaboratively crafts high-quality prompts through flexible dialogue. Activates when user asks to "optimize prompt", "improve system instruction", "enhance AI instruction", or mentions prompt engineering tasks.
ai-cost-optimizer
ScientiaCapital
Save 40-70% on AI costs with intelligent multi-LLM routing. Automatically selects the optimal model based on task complexity across 40+ models from 8 providers.
prompt-library
davila7
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
context-compression
muratcankoylan
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
prompt-lookup
f
Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.