rpe

rpe is a skill for Claude Code from Nivl/Config. It costs 80 tokens per session (456 once invoked), scanned A, original, no licence file.

A request-refinement process that turns a vague or incomplete prompt into a precise task description for a language model before implementation begins.

In plain words
What is it for?
It is for refining prompts marked with [rpe] or requests where the user explicitly asks for clarification, refinement, or a task specification.
Why use it?
It reduces ambiguity before coding starts, so the requested work and expected result are clearer.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Install

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.

agentmods
npx agentmods add skills/nivl/config/rpe
Any agent
npx skills add Nivl/Config --skill rpe
Clone the repo
git clone --depth 1 https://github.com/Nivl/Config

Made for: Claude Code.

Wrote 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.

agentmods badge for rpe

README.md
[![agentmods](https://agentmods.dev/badge/skills/nivl/config/rpe.svg)](https://agentmods.dev/skills/nivl/config/rpe)
Your own site
<a href="https://agentmods.dev/skills/nivl/config/rpe"><img src="https://agentmods.dev/badge/skills/nivl/config/rpe.svg" alt="Measured on agentmods" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 456 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00080 $0.00456
Opus 5 $0.00040 $0.00228
Sonnet 5 $0.00016 $0.00091
Haiku 4.5 $0.00008 $0.00046

Measured 5d ago against content hash b1e047091d19, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

rpe 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 5d 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.

shared_config/.claude/skills/rpe/SKILL.md · 35 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 5d ago First seen · 35 lines · 80 tokens per session scan A b1e047091d19

Subscribe to this mod's changes

rpe is a skill published in the GitHub repository Nivl/Config (2 stars, last pushed 2d ago), with no licence file. It adds 80 tokens to every session and 456 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

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…

langwatch/langwatch · 105 tokens

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.

google-labs-code/stitch-skills · 41 tokens

prompt-engineer

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…

Jeffallan/claude-skills · 93 tokens

omh-model-optimization

This is a Hermes-native model-optimization workflow skill.

rlaope/oh-my-hermes · 82 tokens

hve-builder

Author, review, or validate Copilot prompt-engineering artifacts through independent review, behavior testing, and host checks.

microsoft/hve-core · 27 tokens