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 commands/giuseppe-trisciuoglio/developer-kit/devkit.prompt-optimizegit clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kitWrote 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/commands/giuseppe-trisciuoglio/developer-kit/devkit.prompt-optimize)<a href="https://agentmods.dev/commands/giuseppe-trisciuoglio/developer-kit/devkit.prompt-optimize"><img src="https://agentmods.dev/badge/commands/giuseppe-trisciuoglio/developer-kit/devkit.prompt-optimize.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 | $0.00037 | $0.00824 |
| Opus 5 | $0.00018 | $0.00412 |
| Sonnet 5 | $0.00007 | $0.00165 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
devkit.prompt-optimize 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 4d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimization
Overview
Provides expert prompt optimization using advanced techniques (CoT, few-shot, constitutional AI) for LLM performance enhancement. Use when you need to improve prompt quality or optimize LLM interactions. You are a prompt engineering expert specializing in transforming basic instructions into production-ready prompts using advanced techniques.
Usage
/devkit.prompt-optimize $ARGUMENTS
Arguments
| Argument | Description |
|---|---|
$ARGUMENTS |
Combined arguments passed to the command |
Execution Instructions
Agent Selection: To execute this prompt optimization task, use the following agent with fallback:
- Primary:
developer-kit-ai:prompt-engineering-expert - If not available: Use
developer-kit-ai:prompt-engineering-expertor fallback togeneral-purposeagent with prompt engineering expertise
Instructions
1. Analyze the Prompt
Extract and optimize the prompt provided in the arguments: $ARGUMENTS
Target Model: $2 (default: claude-3.5-sonnet) Optimization Level: $3 (default: standard)
Available optimization levels:
basic- Quick improvements (structure, clarity, basic CoT)standard- Comprehensive enhancement (CoT, few-shot, safety)advanced- Production-ready (full optimization with testing framework)
2. Use the prompt-engineering-expert Agent
Apply the prompt-engineering-expert agent to optimize the prompt using:
Advanced Techniques:
- Chain-of-Thought (CoT): Step-by-step reasoning for complex tasks
- Few-Shot Learning: Strategic examples with edge cases
- Constitutional AI: Self-critique and safety principles
- Structured Output: JSON/XML formats for consistency
- Meta-Prompting: Dynamic prompt generation
Model-Specific Optimization:
- Claude 3.5/4: XML tags, thinking blocks, constitutional alignment
- GPT-4/GPT-4o: Structured sections, JSON mode, function calling
- Gemini Pro/Ultra: Bold headers, process-oriented instructions
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.
- 4d ago First seen · 130 lines · 37 tokens per session scan A ff18f91c2881
devkit.prompt-optimize is a command published in the GitHub repository giuseppe-trisciuoglio/developer-kit (338 stars, last pushed 16d ago), licensed MIT. It adds 37 tokens to every session and 824 once invoked, about $0.0002 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 2026-08-30.
Other commands, from other repositories
create-meta-prompt
Create optimized prompts for Claude-to-Claude pipelines (research -> plan -> implement).
metaprompt
Generate a complete, ready-to-use prompt engineered for a specific model and harness.
guard
Analyze and prepare prompts with JSON for safe context submission.
prompt-compile
Compile the YAML artifact into PROMPT.md with deterministic structure. A final agent pass polishes for fluidity, conciseness, and correctness.
prompt-optimizer
Analyze and rewrite a prompt to maximize clarity, specificity, and output quality.
upgrade
Upgrade the skillshare CLI binary and/or the built-in skillshare skill.