Claude Code Prompt Improver is a Claude Code hook that adds useful context and questions around a submitted prompt, tool call, or subagent start. It is for Claude Code users who want clearer requests, better task planning, and fewer correction rounds.
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 skills add severity1/claude-code-prompt-improver --skill prompt-improvergit clone --depth 1 https://github.com/severity1/claude-code-prompt-improverWrote 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/skills/severity1/claude-code-prompt-improver/prompt-improver)<a href="https://agentmods.dev/skills/severity1/claude-code-prompt-improver/prompt-improver"><img src="https://agentmods.dev/badge/skills/severity1/claude-code-prompt-improver/prompt-improver.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.01397 |
| Opus 5 | $0.00021 | $0.00698 |
| Sonnet 5 | $0.00008 | $0.00279 |
| Haiku 4.5 | $0.00004 | $0.00140 |
Grade A, and why
prompt-improver 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 8d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Improver Skill
Purpose
Transform vague, ambiguous prompts into actionable, well-defined requests through systematic research and targeted clarification. This skill is invoked when the hook has already determined a prompt needs enrichment.
When This Skill is Invoked
Automatic invocation:
- UserPromptSubmit hook evaluates prompt
- Hook determines prompt is vague (missing specifics, context, or clear target)
- Hook invokes this skill to guide research and questioning
Manual invocation:
- To enrich a vague prompt with research-based questions
- When building or testing prompt evaluation systems
- When prompt lacks sufficient context even with conversation history
Assumptions:
- Prompt has already been identified as vague
- Evaluation phase is complete (done by hook)
- Proceed directly to research and clarification
Core Workflow
This skill follows a 4-phase approach to prompt enrichment:
Phase 1: Research
Create a dynamic research plan using TodoWrite before asking questions.
Research Plan Template:
- Check conversation history first - Avoid redundant exploration if context already exists
- Review codebase if needed:
- Task/Explore for architecture and project structure
- Grep/Glob for specific patterns, related files
- Check git log for recent changes
- Search for errors, failing tests, TODO/FIXME comments
- Gather additional context as needed:
- Read local documentation files
- WebFetch for online documentation
- WebSearch for best practices, common approaches, current information
- Document findings to ground questions in actual project context
Critical Rules:
- NEVER skip research
- Check conversation history before exploring codebase
- Questions must be grounded in actual findings, not assumptions or base knowledge
- Route Glob, Grep, WebSearch, WebFetch, and multi-file Read through
Task/Explore— never call them directly in main context - Include conversation-relevant context (file paths, errors, prior decisions) in every Explore prompt — Explore cannot see prior turns
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 163 lines · 42 tokens per session scan A 99d0bd3b757d
prompt-improver is a skill published in the GitHub repository severity1/claude-code-prompt-improver (1,918 stars, last pushed 3mo ago), licensed MIT. It adds 42 tokens to every session and 1,397 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 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.
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…
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-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…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt is slop-heavy, generic, padded with empty quality words, tripping false-positive filters, or needs precise English production vocabulary for camera, lighting, motion, VFX, audio, and constraints.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…