How do I install the Prompt Craft skill?

Run npx skillmds@latest add tomevault-io/prompt-craft in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Prompt Craft safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: CAUTION, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Prompt Craft?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Prompt Craft free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Prompt Craft?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Prompt Craft by tomevault-io — Agent Skill

Name: Prompt Craft
Author: tomevault-io

Prompt Craft

Prompt engineering techniques for dual-model systems — template structure, few-shot design, structured output prompting, model-adaptive strategies for both small (4B-8B) and large (GPT-5, Claude) models Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

Prompt Scaffold

[ROLE]        Who the model is / expertise
[CONTEXT]     Background, domain constraints
[TASK]        One clear instruction
[FORMAT]      Output structure with example
[CONSTRAINTS] Boundaries, edge cases, what to avoid
[EXAMPLES]    Few-shot demonstrations (if needed)

Claude: use XML tags. GPT: markdown headers. Small models: minimal structure, maximum explicitness.

Dual-Model Adaptation

For Small Models (4B-8B Ollama)

Max 1000 tokens prompt length. Cut ruthlessly.
One task per prompt. No compound instructions.
Complete worked example is mandatory — the model mimics format.
Every enum value listed explicitly: quality: one of "excellent", "good", "fair", "poor"
No chain-of-thought unless tested (often hurts structured output).
Positive instructions: "Write X" not "Don't do Y."
Sandwich: format spec at start AND end.

For Large Models (GPT-5, Claude)

Multi-section prompts with nuanced instructions.
<thinking> tags for chain-of-thought (Claude).
Few-shot optional — large models generalize from descriptions.
Can handle "Don't X unless Y" conditional constraints.
System prompt for persona, user prompt for task.

Few-Shot Design

3-5 examples. More than 7 has diminishing returns.
Include at least one edge case example.
Show reasoning, not just input→output.
Order: easy → medium → edge case.
For small models: examples are the prompt. They define the contract.

Structured Output

Provide the exact schema with field descriptions.
Include a complete worked example (JSON, ready to copy).
Specify handling for missing/ambiguous fields.
For enums, list ALL valid values in the prompt.
Show good AND bad examples for critical fields:

## Dilemma ID Naming (CRITICAL)
GOOD: `host_benevolent_or_self_serving`
BAD: `host_motivation`

Defensive Patterns

Sandwich: Repeat critical instructions at start and end.
Validate → Feedback → Repair: Validate output, format structured errors, ask model to fix.
Discuss → Freeze → Serialize: Separate exploration from structured output generation.
Anti-pleasantry: "Do NOT end with 'Good luck!' or similar."

Systematic Testing

Define success criteria before iterating.
Test set of 10-20 diverse inputs including edge cases.
Change one thing at a time.
Track: prompt version, model, temperature, pass rate, failure modes.
Failure taxonomy: wrong format, hallucination, refusal, partial output, off-topic.

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---name: prompt-craftdescription: Prompt engineering techniques for dual-model systems — template structure, few-shot design, structured output prompting, model-adaptive strategies for both small (4B-8B) and large (GPT-5, Claude) models Use when this capability is needed.---## Prompt Scaffold```[ROLE] Who the model is / expertise[CONTEXT] Background, domain constraints[TASK] One clear instruction[FORMAT] Output structure with example[CONSTRAINTS] Boundaries, edge cases, what to avoid[EXAMPLES] Few-shot demonstrations (if needed)```Claude: use XML tags. GPT: markdown headers. Small models: minimal structure, maximum explicitness.## Dual-Model Adaptation### For Small Models (4B-8B Ollama)- **Max 1000 tokens** prompt length. Cut ruthlessly.- **One task per prompt**. No compound instructions.- **Complete worked example** is mandatory — the model mimics format.- Every enum value listed explicitly: `quality: one of "excellent", "good", "fair", "poor"`- **No chain-of-thought** unless tested (often hurts structured output).- Positive instructions: "Write X" not "Don't do Y."- **Sandwich**: format spec at start AND end.### For Large Models (GPT-5, Claude)- Multi-section prompts with nuanced instructions.- `<thinking>` tags for chain-of-thought (Claude).- Few-shot optional — large models generalize from descriptions.- Can handle "Don't X unless Y" conditional constraints.- System prompt for persona, user prompt for task.## Few-Shot Design- **3-5 examples**. More than 7 has diminishing returns.- Include at least one **edge case** example.- Show **reasoning**, not just input→output.- Order: easy → medium → edge case.- For small models: examples are the prompt. They define the contract.## Structured Output1. Provide the **exact schema** with field descriptions.2. Include a **complete worked example** (JSON, ready to copy).3. Specify handling for missing/ambiguous fields.4. For enums, list ALL valid values in the prompt.5. Show good AND bad examples for critical fields:```## Dilemma ID Naming (CRITICAL)GOOD: `host_benevolent_or_self_serving`BAD: `host_motivation````## Defensive Patterns- **Sandwich**: Repeat critical instructions at start and end.- **Validate → Feedback → Repair**: Validate output, format structured errors, ask model to fix.- **Discuss → Freeze → Serialize**: Separate exploration from structured output generation.- **Anti-pleasantry**: "Do NOT end with 'Good luck!' or similar."## Systematic Testing1. Define success criteria before iterating.2. Test set of 10-20 diverse inputs including edge cases.3. Change one thing at a time.4. Track: prompt version, model, temperature, pass rate, failure modes.5. Failure taxonomy: wrong format, hallucination, refusal, partial output, off-topic.---> Converted and distributed by [TomeVault](https://tomevault.io/claim/pvliesdonk) — claim your Tome and manage your conversions.

Frequently asked questions