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/artttj/rapid-stack/prompt-optimizernpx skills add artttj/rapid-stack --skill prompt-optimizergit clone --depth 1 https://github.com/artttj/rapid-stackWhat 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 | $0.00103 | $0.03933 |
| Opus 5 | $0.00051 | $0.01966 |
| Sonnet 5 | $0.00021 | $0.00787 |
| Haiku 4.5 | $0.00010 | $0.00393 |
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 2d ago.
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 — 393 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 Claude Code 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
/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
CLAUDE.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.
- 2d ago First seen · 393 lines · 103 tokens per session scan A a073d3092eb7
prompt-optimizer is a skill published in the GitHub repository artttj/rapid-stack (2 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 3,933 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
fixing-prompt
Prompt: Prompt Refinement and Optimization.
prompt-writer
Write maximally terse agent prompts from scratch. Use when creating agent specs, command prompts, or instruction sets with constitutional governance.
boost-prompt
Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.
context-fundamentals
This skill should be used to explain or reason about the foundational concepts of context engineering: what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…