prompt-improver

# Prompt Improver

## Overview

A skill that analyzes and improves prompts based on general LLM/agent best practices. It focuses on verifiability, clear scope, explicit constraints, and context economy so the agent can execute with minimal back-and-forth.

If you are running in Claude Code, also read `references/claude.md` and apply the additional Claude-specific techniques.
If you are running in Codex CLI, also read `references/codex.md` and apply the additional Codex-specific techniques.

When the input is a document that instructs an agent (e.g., plan files, AGENTS.md, system instruction docs), treat the document as the improvement target; identify issues and propose concrete improvements, and include a revised draft when helpful.

## Workflow

### Step 0: Classify Task and Complexity

Classify the task and decide whether an explicit exploration/planning phase should be recommended:

- Task type: bugfix, feature, refactor, research, UI/visual, docs, ops
- Complexity: single-file/small change vs multi-file/uncertain impact
- Risk: data safety, security, compatibility, performance
- Input type: prompt vs agent-instruction document (plan files, AGENTS.md, system instruction docs)

If the task is complex or ambiguous, the improved prompt should explicitly request an exploration/planning phase before implementation.

### Step 1: Analyze the Prompt

Analyze the user-provided prompt from the following perspectives:

1. **Verifiability**: Does it include means for Claude to verify its own work?
2. **Specificity**: Are files, scenarios, and constraints clearly specified?
3. **Context**: Is necessary background information provided?
4. **Scope**: Is the task scope appropriately defined?
5. **Expected Outcome**: Are success criteria clear?
6. **Constraints**: Are language/runtime versions, dependencies, security, or compatibility requirements specified?
7. **Context Economy**: Is the prompt concise and focused, without unnecessary information?
8. **Execution Preference**: I