# Prompt Creator > Expert prompt engineering for AI models (especially Claude). Use when the user wants to: create a new prompt from scratch, improve or debug an existing prompt, design a system prompt for a chatbot or assistant, write few-shot examples, structure chain-of-thought reasoning, generate structured/JSON output, build RAG or agentic prompts, or evaluate why a prompt is underperforming and how to fix it. Triggers on requests like "write a prompt for...", "create a system prompt", "improve this prompt", "help me prompt engineer", "why isn't my prompt working", "make a prompt that outputs JSON". - Skill: `skizha/prompt-creator` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add skizha/prompt-creator` - Raw SKILL.md: https://api.skillmd.com/api/skills/skizha/prompt-creator/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: skizha (https://skillmd.com/u/skizha) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/skizha/prompt-creator --- # Prompt Creator ## Workflow Follow this process for every prompt engineering request: **1. Clarify the goal (if not already clear)** - What is the task the prompt must accomplish? - Who/what will execute it (Claude, GPT, a specific model)? - How will the output be used (human reads it / parsed by code / passed to another model)? - What does "good" look like? Any examples of ideal output? **2. Select the right techniques** Determine which techniques apply based on the request type: | Request type | Primary techniques | |---|---| | Chatbot / assistant | Role/persona, system prompt split, constraint specification | | Data extraction | Structured output (JSON), XML tags, constraint specification, give model an "out" | | Classification | Few-shot examples (`` tags), structured output, conclusions-last ordering | | Reasoning / analysis | Chain-of-thought (CoT), extended thinking, output priming | | Multi-phase complex tasks | Prompt chaining, self-correction chain | | Document generation | Decomposition, template pattern, output priming | | Code gen / review | Constraint specification, structured output, CoT self-verification | | RAG / document QA | Docs-above-query ordering, citations as anti-hallucination, give model an "out" | | Agent / tool use | Agentic pattern, constraint specification | | Writing a prompt from scratch | Meta-prompting — describe the task, let the model draft | | Improving a bad prompt | Diagnose first (see Diagnostics below), meta-prompting for alternatives | Read [references/techniques.md](references/techniques.md) for full technique details and examples (including Prompt Chaining). Read [references/claude-specifics.md](references/claude-specifics.md) when targeting Claude specifically (XML tags, **critical long-context ordering**, extended thinking, system vs human split, prompt injection defense). Read [references/prompt-types.md](references/prompt-types.md) for ready-to-adapt templates for common prompt categories (including RAG, Prompt Chaining, Agentic). **3. Draft the prompt** - Use the relevant template from prompt-types.md as a starting point - Apply techniques from techniques.md - Apply Claude-specific best practices from claude-specifics.md when relevant - Follow the quality checklist below before delivering **4. Deliver and offer iteration** Present the finished prompt in a code block. Briefly explain key design choices (1-3 sentences). Offer to: - Add few-shot examples - Adjust tone, length, or format constraints - Create test cases to validate the prompt --- ## Quality Checklist Before delivering any prompt, verify: - [ ] Task is unambiguous — a stranger could execute it correctly - [ ] Output format is explicitly specified (or output primed with a partial phrase) - [ ] Both "do" and "do NOT" constraints are present where relevant - [ ] Model has an "out" for cases it can't answer (e.g., "respond with 'not found' if...") - [ ] Variables/placeholders use a consistent convention: `[PLACEHOLDER]` or `{{placeholder}}` - [ ] Long prompts use XML tags or clear section headers to separate concerns - [ ] No conflicting instructions; key constraints repeated at the end if critical - [ ] Role/persona is assigned if tone or expertise level matters - [ ] Few-shot examples included if output style is non-obvious - [ ] Reasoning requested (CoT) if the task involves multi-step logic - [ ] Citations requested if factual accuracy and grounding are critical --- ## Diagnostics: Improving a Bad Prompt When a user brings an underperforming prompt, diagnose before prescribing: **Hallucination / making things up** - Add a fallback: "If the answer isn't in the context, respond with 'Not found.'" - Add citation requirement: "After each claim, cite the source. Do not make claims you can't cite." (forcing citations forces double errors to fabricate) - For RAG: add explicit grounding instructions and prompt injection defense (see claude-specifics.md) **Wrong format / structure** - Be more explicit about output format; provide a template or example output **Inconsistent results** - Add few-shot examples; tighten constraints; reduce degrees of freedom **Too long / too short** - Add explicit length constraint: "Respond in exactly 3 bullet points" / "Keep response under 100 words" **Wrong tone** - Assign a specific persona; add explicit tone descriptors; provide a style example **Ignoring part of the instructions** - Use XML tags to separate sections; repeat critical instructions at the end (recency bias — the model gives more weight to content near the end) - Simplify: break into multiple smaller prompts if complexity is the issue **Reasoning errors** - Add chain-of-thought: "Think step by step before answering" - Use extended thinking mode if available --- ## Output Format Present the final prompt in a fenced code block: ``` [FINAL PROMPT HERE] ``` Then add a short **Design notes** section (2-4 bullets) explaining the key choices made.