Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jamkris/everything-gemini-code/prompt-optimizernpx skills add Jamkris/everything-gemini-code --skill prompt-optimizergit clone --depth 1 https://github.com/Jamkris/everything-gemini-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jamkris/everything-gemini-code/prompt-optimizer)<a href="https://agentmods.dev/skills/jamkris/everything-gemini-code/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/jamkris/everything-gemini-code/prompt-optimizer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00179 | $0.04149 |
| Opus 5 | $0.00089 | $0.02074 |
| Sonnet 5 | $0.00036 | $0.00830 |
| Haiku 4.5 | $0.00018 | $0.00415 |
Grade A, and why
prompt-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 398 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer
Analyze a draft prompt, critique it, match it to ECC ecosystem components, and output a complete optimized prompt the user can paste and run.
When to Use
- User says "optimize this prompt", "improve my prompt", "rewrite this prompt"
- User says "help me write a better prompt for..."
- User says "what's the best way to ask Gemini CLI to..."
- User says "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令"
- User pastes a draft prompt and asks for feedback or enhancement
- User says "I don't know how to prompt for this"
- User says "how should I use ECC for..."
- User explicitly invokes
/egc-prompt-optimize
Do Not Use When
- User wants the task done directly (just execute it)
- User says "优化代码", "优化性能", "optimize this code", "optimize performance" — these are refactoring tasks, not prompt optimization
- User is asking about ECC configuration (use
configure-eccinstead) - User wants a skill inventory (use
skill-stocktakeinstead) - User says "just do it" or "直接做"
How It Works
Advisory only — do not execute the user's task.
Do NOT write code, create files, run commands, or take any implementation action. Your ONLY output is an analysis plus an optimized prompt.
If the user says "just do it", "直接做", or "don't optimize, just execute", do not switch into implementation mode inside this skill. Tell the user this skill only produces optimized prompts, and instruct them to make a normal task request if they want execution instead.
Run this 6-phase pipeline sequentially. Present results using the Output Format below.
Analysis Pipeline
Phase 0: Project Detection
Before analyzing the prompt, detect the current project context:
- Check if a
GEMINI.mdexists in the working directory — read it for project conventions - Detect tech stack from project files:
package.json→ Node.js / TypeScript / React / Next.jsgo.mod→ Gopyproject.toml/requirements.txt→ PythonCargo.toml→ Rustbuild.gradle/pom.xml→ Java / Kotlin / Spring BootPackage.swift→ SwiftGemfile→ Rubycomposer.json→ PHP*.csproj/*.sln→ .NETMakefile/CMakeLists.txt→ C / C++cpanfile/Makefile.PL→ Perl
- Note detected tech stack for use in Phase 3 and Phase 4
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 398 lines · 179 tokens per session scan A 73173d43c78b
prompt-optimizer is a skill published in the GitHub repository Jamkris/everything-gemini-code (87 stars, last pushed 3mo ago), licensed MIT. It adds 179 tokens to every session and 4,149 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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This skill should be used when the user wants to learn "prompt engineering", "few-shot prompting", "zero-shot prompting", "chain of thought prompting", "structured output prompting", "role prompting", "system prompt design", "prompt best practices", "CoT prompting", "Pydantic structured output", "prompt iteration"…
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This skill should be used when the user wants to "chain prompts", "build a pipeline", "break down complex tasks into steps", "sequential LLM calls", "multi-step reasoning", "pipeline pattern", "sequential agent pipeline", "multi-step prompt pipeline", "LLM chain", "step-by-step agent", "prompt pipeline", "decompose…
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Use this skill when asked to create, refine, analyze, or optimize prompts for Large Language Models (LLMs). This skill ensures adherence to prompt engineering best practices and enforces a rigorous design workflow.
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Detect and remove hollow AI filler language, empty superlatives, and vague boosters that degrade Seedance 2.0 prompt quality. Use when a prompt feels generic, over-written, or 'AI-sounding', or when generation output looks bland and needs a quality pass.