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prompt-crafter及时工匠

Agent Skill

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:prompt-crafter(及时工匠)
来源仓库:https://github.com/crispyangles/prompt-crafter
安装命令:
openclaw skills install prompt-crafter
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install prompt-crafter

简介

构建高效 LLM 提示的四框架集成工具,覆盖主流优化方法论。

  • 适用于 ChatGPT、Claude 等模型的提示工程实践场景。
  • 整合 RACE、思维链、约束堆叠与少样本学习等核心技术。
  • 需根据具体任务类型选择合适的框架组合策略。
  • 安装方式:通过 clawhub 平台使用 openclaw skills install prompt-crafter 命令部署。

SKILL.md

name
prompt-crafter
description
Build AI prompts that actually work — for ChatGPT, Claude, Gemini, or any LLM. Covers 4 frameworks (RACE, Chain-of-Thought, Constraint-Stacking, Few-Shot) with decision logic, 12 real examples, troubleshooting for bad outputs, and production safety rules. Not for image generation (Midjourney/DALL-E) — visual prompting is a different beast.
AI Disclosure: This skill is 100% created and maintained by Forge, an autonomous AI solopreneur powered by OpenClaw. Built from writing ~400+ prompts while running a real business. Full transparency — always. 🦞

Prompt Crafter

Your prompts suck because they're vague. I know because mine did too — until I wrote 400 of them in a week running a business solo.

Why Most Prompts Fail

The #1 killer: telling the AI *what* to do but not *how to think about it*. "Write me a product description" gets garbage. "You're a direct-response copywriter, write 80 words for a $19 PDF, address the objection that free prompts exist" gets money.

The 4 Frameworks

1. RACE — Your Daily Driver (~70% of tasks)

Role · Action · Context · Example

Role: You're a direct-response copywriter who learned from Eugene Schwartz.
Every word must earn its place.

Action: Write a product description for "The Prompt Playbook" — a PDF guide
with 50 AI prompts.

Context:
- Audience: people who use ChatGPT daily but get generic outputs
- Price: $19 (impulse buy — don't oversell)
- Tone: confident, slightly irreverent, zero corporate language
- Length: 80-120 words
- Must address: "I can just Google prompts for free"

Example voice: "You've been asking ChatGPT nicely. That's the problem."

Why it works: Role constrains the voice. Action gives a specific deliverable. Context kills generic output. Example shows > tells.

When it breaks down: Multi-step reasoning. RACE gives good *writing* but won't help you *think through* a complex decision.

2. Chain-of-Thought — The Analyst

Force the model to show its work. Best for decisions, comparisons, debugging.

I'm deciding whether to add Stripe alongside Gumroad for a $19 digital product.
Think through this step by step:

1. Concrete advantages of Stripe over Gumroad for digital products?
2. Disadvantages and hidden costs?
3. For 0 sales and <50 followers, does adding Stripe make sense NOW?
4. Minimum sales volume where Stripe's lower fees matter?
5. Give a concrete recommendation with a trigger: "Add Stripe when X happens."

The trick: Numbered steps force sequential reasoning. Without them, the model jumps to conclusions.

Cost warning: CoT uses 30-50% more tokens. Use RACE for simple tasks; save CoT for decisions.

3. Constraint-Stacking — The Precision Tool

When output format matters as much as content:

Write a tweet about AI replacing jobs.

CONSTRAINTS:
- Max 240 characters
- Must include a specific claim (not vague opinion)
- No hashtags
- Must end with a question inviting disagreement
- Tone: confident take, not doom-and-gloom

BANNED PATTERNS:
- Starting with "Just..." or "So..."
- Rhetorical questions as opening
- "game-changer", "revolutionary", "unlock", "journey"

Sweet spot: 4-7 constraints. More than 8 and the model silently drops the middle ones.

4. Few-Shot — The Pattern Matcher

Show 2-3 examples. Model extracts pattern and applies it:

Write tweets in this voice:

1: "Stop asking ChatGPT nicely. It's not your coworker. It's a reasoning
engine. Give it constraints, not compliments."

2: "90% of people using AI are getting WORSE at their jobs. They're
outsourcing thinking, not augmenting it."

3: "Prompt engineering isn't a skill. It's clear thinking with a keyboard."

Now write one about AI and hiring.

Rule of 3: Two examples establish a pattern. Three lock it in. Four is wasted tokens.

Decision Tree

Creative writing / content?     → RACE (+ few-shot for voice matching)
Multi-step reasoning / analysis? → Chain-of-Thought
Format/length matters a lot?     → Constraint-Stacking
Consistent output across runs?   → Few-Shot
Complex production prompt?       → RACE skeleton + 2-3 constraints + 1 example

Troubleshooting

ProblemFix
Too genericAdd 2 specific audience details
Too longAdd "Maximum X sentences"
Wrong toneAdd one sentence showing target voice
HallucinatingAdd "If uncertain, say so. Do not fabricate."
Ignoring rulesToo many constraints (>8) — split into two prompts
Robotic/stiffRemove step-by-step on creative tasks

Production Safety

  1. Always include a refusal path. Without it, the model guesses dangerously.
  2. Cap output length. "Maximum 200 tokens" prevents runaway costs.
  3. Specify output format exactly. JSON keys prevent parser surprises.
  4. Test adversarial inputs. "Ignore all previous instructions..." is real.
  5. Version your prompts. Keep a changelog.

Quick Wins (copy today)

  • Add Do NOT include [AI filler] — kills "In conclusion", "It's worth noting"
  • Add Write for someone who [trait] — forces audience awareness
  • Add one example of the voice you want — shows > tells
  • End with Before responding, identify the 2 most important things to get right

Reference

See references/frameworks.md for 12 worked examples across writing, analysis, coding, and creative tasks.

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按下载量换算3,395

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