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/isabela-valonni/prompt-evaluatorWrote 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/isabela-valonni/prompt-evaluator/evaluate)<a href="https://agentmods.dev/commands/isabela-valonni/prompt-evaluator/evaluate"><img src="https://agentmods.dev/badge/commands/isabela-valonni/prompt-evaluator/evaluate.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 5.1 | $0.00013 | $0.00184 |
| Opus 5 | $0.00006 | $0.00092 |
| Sonnet 5 | $0.00003 | $0.00037 |
| Haiku 4.5 | $0.00001 | $0.00018 |
Grade A, and why
evaluate 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 7d 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
Evaluate the user's most recent prompt for prompting quality. If the user pasted text after the command, evaluate that text instead.
First, if user_prompting_profile.md exists in the working folder, read it so the evaluation reflects the user's recurring patterns rather than starting fresh.
Respond using exactly this format, rendered as a four-line blockquote:
PROMPTING FEEDBACK X / 10 — one-line rationale What worked: one specific observation To sharpen: one concrete suggestion, with an example
Keep it tight: one score, one strength, one fix. No paragraphs of advice, no theory.
After evaluating, update user_prompting_profile.md with the new observation and a "Pattern to watch" line linking this evaluation to earlier ones. Create the file if it does not exist.
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.
- 7d ago First seen · 19 lines · 13 tokens per session scan A f876c9bd6a6c
evaluate is a command published in the GitHub repository isabela-valonni/prompt-evaluator (5 stars, last pushed 2mo ago), licensed MIT. It adds 13 tokens to every session and 184 once invoked, about $0.0001 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 commands, from other repositories
batch-operations-prompt
Optimize prompts for multiple file operations, parallel processing, and efficient bulk changes across a codebase. This helps Claude Code work more efficiently with TodoWrite patterns.
hades-rebuild-graph
Trigger a full graph rebuild from scratch.
langchain-agent
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
prompt
A command that creates AI prompts and applies fixed rules for constraints, principles, and when the command may run.
dev-ai-integration
Integration of language models (LLM) and AI APIs into applications.
prompt-update
A command for updating and combining prompt-engineering guidance, meaning methods for writing clearer instructions for AI.