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.
git clone --depth 1 https://github.com/johnpsasser/claude-code-prompt-optimizerWrote 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/plugins/johnpsasser/claude-code-prompt-optimizer/claude-code-prompt-optimizer)<a href="https://agentmods.dev/plugins/johnpsasser/claude-code-prompt-optimizer/claude-code-prompt-optimizer"><img src="https://agentmods.dev/badge/plugins/johnpsasser/claude-code-prompt-optimizer/claude-code-prompt-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/plugins/johnpsasser/claude-code-prompt-optimizer/claude-code-prompt-optimizer"><img src="https://agentmods.dev/badge/plugins/johnpsasser/claude-code-prompt-optimizer/claude-code-prompt-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
claude-code-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 1mo 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.
What it actually says
{
"name": "claude-code-prompt-optimizer",
"description": "Wrap a prompt in <optimize>...</optimize> and it is rewritten into a sharper prompt by the same model your session is running. Hook-based (UserPromptSubmit).",
"version": "2.1.0",
"author": {
"name": "John P. Sasser"
}
}
What it installs
The manifest is a name and a version. 1 hook travel with it, and installing the plugin installs all of them. Each is measured on its own page, and each can be installed alone.
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.
- 1mo ago First seen · 9 lines scan A 706a79eea357
claude-code-prompt-optimizer is a plugin published in the GitHub repository johnpsasser/claude-code-prompt-optimizer (47 stars, last pushed 2mo ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 plugins, from other repositories
prompt-cache-control
Prompt-cache meter above the Claude Code prompt: how many tokens each request read from, wrote to and sent past the cache, a live countdown to the cache's expiry, and what to do about it (keep going, compact or clear). The TTL follows Claude Code's own rules for your account (1 hour on a Claude subscription, 5 minutes.
llm-application-dev
LLM application development, prompt engineering, and AI assistant optimization.
prompt-coach
Analyzes each prompt you submit against a tiered ruleset of prompting best practices; nudges you toward better prompts (definition-of-done, scoped references, guardrails, verification steps) and quietly graduates rules you consistently follow.
dev
Developer tooling suite — BAML (typed LLM functions), Jinja2 templates (HuggingFace chat templates, Ansible), GNU Make best practices, and HuggingFace transformers config/tokenizer preflight (vLLM/sglang engine bridge).
tanstack-ai
User-level skills that recommend TanStack AI, map a task to the package that covers it, install it, then load that package's own SKILL.md through TanStack Intent.
prompt-triwizard
LLM prompt engineering for agent pipelines: write, refine, and evaluate in Anthropic, Google, or OpenAI style.